From f23de0df53ac075d6b4ef430af7bc2bed89fb3c6 Mon Sep 17 00:00:00 2001
From: zehao-intel <zehao.huang@intel.com>
Date: Thu, 4 Jul 2024 17:30:23 +0800
Subject: [PATCH 01/14] Update Examples for TF 3x API

Signed-off-by: zehao-intel <zehao.huang@intel.com>
---
 .../.config/model_params_tensorflow_3x.json   | 109 ++-
 .../cv/densenet121/quantization/ptq/README.md |  78 ++
 .../quantization/ptq/data_process.py          | 581 +++++++++++++++
 .../cv/densenet121/quantization/ptq/main.py   | 147 ++++
 .../quantization/ptq/requirements.txt         |   2 +
 .../quantization/ptq/run_benchmark.sh         |  51 ++
 .../quantization/ptq/run_quant.sh             |   0
 .../inception_v3/quantization/ptq/README.md   |  75 ++
 .../quantization/ptq/data_process.py          | 511 +++++++++++++
 .../cv/inception_v3/quantization/ptq/main.py  | 144 ++++
 .../quantization/ptq/requirements.txt         |   2 +
 .../quantization/ptq/run_benchmark.sh         |  51 ++
 .../quantization/ptq/run_quant.sh             |  39 +
 .../mobilenet_v2/quantization/ptq/README.md   | 108 +++
 .../quantization/ptq/data_process.py          | 511 +++++++++++++
 .../cv/mobilenet_v2/quantization/ptq/main.py  | 143 ++++
 .../quantization/ptq/requirements.txt         |   2 +
 .../quantization/ptq/run_benchmark.sh         |  51 ++
 .../quantization/ptq/run_quant.sh             |  39 +
 .../prepare_dataset.sh                        |   0
 .../resnet_v2_50/quantization/ptq/README.md   | 107 +++
 .../quantization/ptq/data_process.py          | 511 +++++++++++++
 .../cv/resnet_v2_50/quantization/ptq/main.py  | 144 ++++
 .../quantization/ptq/requirements.txt         |   2 +
 .../quantization/ptq/run_benchmark.sh         |  51 ++
 .../quantization/ptq/run_quant.sh             |  39 +
 .../cv/vgg16/quantization/ptq/README.md       | 108 +++
 .../cv/vgg16/quantization/ptq/data_process.py | 581 +++++++++++++++
 .../cv/vgg16/quantization/ptq/main.py         | 146 ++++
 .../vgg16/quantization/ptq/requirements.txt   |   2 +
 .../vgg16/quantization/ptq/run_benchmark.sh   |  51 ++
 .../cv/vgg16/quantization/ptq/run_quant.sh    |  39 +
 .../quantization/ptq/README.md                |   8 +-
 .../quantization/ptq/__init__.py              |   0
 .../quantization/ptq/data_process.py          |   0
 .../quantization/ptq/main.py                  |   1 -
 .../quantization/ptq/requirements.txt         |   0
 .../quantization/ptq/run_benchmark.sh         |   0
 .../quantization/ptq/run_quant.sh             |  39 +
 .../quantization/ptq/README.md                |   8 +-
 .../quantization/ptq/main.py                  |   3 -
 .../quantization/ptq/README.md                | 187 +++++
 .../quantization/ptq/download_dataset.py      |  38 +
 .../distilbert_base/quantization/ptq/main.py  | 324 ++++++++
 .../quantization/ptq/requirements.txt         |   7 +
 .../quantization/ptq/run_benchmark.sh         |  88 +++
 .../quantization/ptq/run_quant.sh             |  83 +++
 .../transformer_lt/quantization/ptq/README.md | 132 ++++
 .../transformer_lt/quantization/ptq/main.py   | 261 +++++++
 .../quantization/ptq/prepare_dataset_model.sh |  62 ++
 .../quantization/ptq/run_benchmark.sh         |  64 ++
 .../quantization/ptq/run_quant.sh             |  43 ++
 .../quantization/ptq/utils/__init__.py        |   0
 .../quantization/ptq/utils/metrics.py         | 490 +++++++++++++
 .../quantization/ptq/utils/tokenizer.py       | 620 ++++++++++++++++
 .../quantization/ptq/utils/tokenizer_test.py  | 182 +++++
 .../quantization/ptq/README.md                | 130 ++++
 .../quantization/ptq/coco_tools.py            | 694 ++++++++++++++++++
 .../quantization/ptq/data_process.py          | 655 +++++++++++++++++
 .../quantization/ptq/main.py                  | 128 ++++
 .../quantization/ptq/run_benchmark.sh         |  51 ++
 .../quantization/ptq/run_quant.sh             |  41 ++
 .../quantization/ptq/README.md                | 140 ++++
 .../quantization/ptq/coco_tools.py            | 694 ++++++++++++++++++
 .../quantization/ptq/data_process.py          | 655 +++++++++++++++++
 .../quantization/ptq/main.py                  | 131 ++++
 .../quantization/ptq/run_benchmark.sh         |  51 ++
 .../quantization/ptq/run_quant.sh             |  41 ++
 .../object_detection/prepare_dataset.sh       | 136 ++++
 .../object_detection/requirements.txt         |   8 +
 .../quantization/ptq/README.md                | 158 ++++
 .../quantization/ptq/coco_tools.py            | 694 ++++++++++++++++++
 .../quantization/ptq/data_process.py          | 655 +++++++++++++++++
 .../ssd_mobilenet_v1/quantization/ptq/main.py | 129 ++++
 .../quantization/ptq/prepare_model.py         |  99 +++
 .../quantization/ptq/run_benchmark.sh         |  52 ++
 .../quantization/ptq/run_quant.sh             |  41 ++
 .../quantization/ptq/README.md                |  98 +++
 .../quantization/ptq/main.py                  | 348 +++++++++
 .../ptq/preprocess_csv_tfrecords.py           | 155 ++++
 .../quantization/ptq/requirements.txt         |   9 +
 .../quantization/ptq/run_benchmark.sh         |  57 ++
 .../quantization/ptq/run_quant.sh             |  48 ++
 .../3dunet-mlperf/quantization/ptq/README.md  |  73 ++
 .../quantization/ptq/__init__.py              |  19 +
 .../ptq/brats_cal_images_list.txt             |  40 +
 .../3dunet-mlperf/quantization/ptq/main.py    | 219 ++++++
 .../ptq/nnUNet/Task043_BraTS_2019.py          | 125 ++++
 .../quantization/ptq/nnUNet/__init__.py       |  19 +
 .../ptq/nnUNet/folds/fold0_validation.txt     |  67 ++
 .../ptq/nnUNet/folds/fold1_validation.txt     |  67 ++
 .../ptq/nnUNet/folds/fold2_validation.txt     |  67 ++
 .../ptq/nnUNet/folds/fold3_validation.txt     |  67 ++
 .../ptq/nnUNet/folds/fold4_validation.txt     |  67 ++
 .../quantization/ptq/nnUNet/postprocess.py    |  72 ++
 .../quantization/ptq/nnUNet/preprocess.py     | 109 +++
 .../quantization/ptq/nnUNet/setup.py          |  81 ++
 .../quantization/ptq/requirements.txt         |   1 +
 .../quantization/ptq/run_benchmark.sh         |  61 ++
 .../quantization/ptq/run_quant.sh             |  48 ++
 .../quantization/ptq/README.md                | 131 ++++
 .../ptq/content_images/colva_beach_sq.jpg     | Bin 0 -> 14235 bytes
 .../ptq/content_images/golden_gate_sq.jpg     | Bin 0 -> 12423 bytes
 .../quantization/ptq/data_process.py          | 489 ++++++++++++
 .../quantization/ptq/main.py                  | 208 ++++++
 .../quantization/ptq/prepare_model.py         |  33 +
 .../quantization/ptq/requirements.txt         |   2 +
 .../quantization/ptq/run_benchmark.sh         |  61 ++
 .../quantization/ptq/run_quant.sh             |  50 ++
 .../ptq/style_images/kanagawa_great_wave.jpg  | Bin 0 -> 28352 bytes
 .../ptq/style_images/zigzag_colorful.jpg      | Bin 0 -> 19632 bytes
 111 files changed, 15326 insertions(+), 13 deletions(-)
 create mode 100644 examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/README.md
 create mode 100644 examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/data_process.py
 create mode 100644 examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/main.py
 create mode 100644 examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/requirements.txt
 create mode 100644 examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/run_benchmark.sh
 rename examples/3.x_api/tensorflow/{image_recognition/vision_transformer => cv/densenet121}/quantization/ptq/run_quant.sh (100%)
 create mode 100644 examples/3.x_api/tensorflow/cv/inception_v3/quantization/ptq/README.md
 create mode 100644 examples/3.x_api/tensorflow/cv/inception_v3/quantization/ptq/data_process.py
 create mode 100644 examples/3.x_api/tensorflow/cv/inception_v3/quantization/ptq/main.py
 create mode 100644 examples/3.x_api/tensorflow/cv/inception_v3/quantization/ptq/requirements.txt
 create mode 100644 examples/3.x_api/tensorflow/cv/inception_v3/quantization/ptq/run_benchmark.sh
 create mode 100644 examples/3.x_api/tensorflow/cv/inception_v3/quantization/ptq/run_quant.sh
 create mode 100644 examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/README.md
 create mode 100644 examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/data_process.py
 create mode 100644 examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/main.py
 create mode 100644 examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/requirements.txt
 create mode 100644 examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/run_benchmark.sh
 create mode 100644 examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/run_quant.sh
 rename examples/3.x_api/tensorflow/{image_recognition => cv}/prepare_dataset.sh (100%)
 create mode 100644 examples/3.x_api/tensorflow/cv/resnet_v2_50/quantization/ptq/README.md
 create mode 100644 examples/3.x_api/tensorflow/cv/resnet_v2_50/quantization/ptq/data_process.py
 create mode 100644 examples/3.x_api/tensorflow/cv/resnet_v2_50/quantization/ptq/main.py
 create mode 100644 examples/3.x_api/tensorflow/cv/resnet_v2_50/quantization/ptq/requirements.txt
 create mode 100644 examples/3.x_api/tensorflow/cv/resnet_v2_50/quantization/ptq/run_benchmark.sh
 create mode 100644 examples/3.x_api/tensorflow/cv/resnet_v2_50/quantization/ptq/run_quant.sh
 create mode 100644 examples/3.x_api/tensorflow/cv/vgg16/quantization/ptq/README.md
 create mode 100644 examples/3.x_api/tensorflow/cv/vgg16/quantization/ptq/data_process.py
 create mode 100644 examples/3.x_api/tensorflow/cv/vgg16/quantization/ptq/main.py
 create mode 100644 examples/3.x_api/tensorflow/cv/vgg16/quantization/ptq/requirements.txt
 create mode 100644 examples/3.x_api/tensorflow/cv/vgg16/quantization/ptq/run_benchmark.sh
 create mode 100644 examples/3.x_api/tensorflow/cv/vgg16/quantization/ptq/run_quant.sh
 rename examples/3.x_api/tensorflow/{image_recognition => cv}/vision_transformer/quantization/ptq/README.md (77%)
 rename examples/3.x_api/tensorflow/{image_recognition => cv}/vision_transformer/quantization/ptq/__init__.py (100%)
 rename examples/3.x_api/tensorflow/{image_recognition => cv}/vision_transformer/quantization/ptq/data_process.py (100%)
 rename examples/3.x_api/tensorflow/{image_recognition => cv}/vision_transformer/quantization/ptq/main.py (99%)
 rename examples/3.x_api/tensorflow/{image_recognition => cv}/vision_transformer/quantization/ptq/requirements.txt (100%)
 rename examples/3.x_api/tensorflow/{image_recognition => cv}/vision_transformer/quantization/ptq/run_benchmark.sh (100%)
 create mode 100644 examples/3.x_api/tensorflow/cv/vision_transformer/quantization/ptq/run_quant.sh
 create mode 100644 examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/README.md
 create mode 100644 examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/download_dataset.py
 create mode 100644 examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/main.py
 create mode 100644 examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/requirements.txt
 create mode 100644 examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/run_benchmark.sh
 create mode 100644 examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/run_quant.sh
 create mode 100644 examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/README.md
 create mode 100644 examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/main.py
 create mode 100644 examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/prepare_dataset_model.sh
 create mode 100644 examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/run_benchmark.sh
 create mode 100644 examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/run_quant.sh
 create mode 100644 examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/utils/__init__.py
 create mode 100644 examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/utils/metrics.py
 create mode 100644 examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/utils/tokenizer.py
 create mode 100644 examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/utils/tokenizer_test.py
 create mode 100644 examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/README.md
 create mode 100644 examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/coco_tools.py
 create mode 100644 examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/data_process.py
 create mode 100644 examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/main.py
 create mode 100644 examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/run_benchmark.sh
 create mode 100644 examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/run_quant.sh
 create mode 100644 examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/README.md
 create mode 100644 examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/coco_tools.py
 create mode 100644 examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/data_process.py
 create mode 100644 examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/main.py
 create mode 100644 examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/run_benchmark.sh
 create mode 100644 examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/run_quant.sh
 create mode 100644 examples/3.x_api/tensorflow/object_detection/prepare_dataset.sh
 create mode 100644 examples/3.x_api/tensorflow/object_detection/requirements.txt
 create mode 100644 examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/README.md
 create mode 100644 examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/coco_tools.py
 create mode 100644 examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/data_process.py
 create mode 100644 examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/main.py
 create mode 100644 examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/prepare_model.py
 create mode 100644 examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/run_benchmark.sh
 create mode 100644 examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/run_quant.sh
 create mode 100644 examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/README.md
 create mode 100644 examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/main.py
 create mode 100644 examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/preprocess_csv_tfrecords.py
 create mode 100644 examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/requirements.txt
 create mode 100644 examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/run_benchmark.sh
 create mode 100644 examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/run_quant.sh
 create mode 100644 examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/README.md
 create mode 100644 examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/__init__.py
 create mode 100644 examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/brats_cal_images_list.txt
 create mode 100644 examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/main.py
 create mode 100644 examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/Task043_BraTS_2019.py
 create mode 100644 examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/__init__.py
 create mode 100644 examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/folds/fold0_validation.txt
 create mode 100644 examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/folds/fold1_validation.txt
 create mode 100644 examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/folds/fold2_validation.txt
 create mode 100644 examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/folds/fold3_validation.txt
 create mode 100644 examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/folds/fold4_validation.txt
 create mode 100644 examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/postprocess.py
 create mode 100644 examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/preprocess.py
 create mode 100644 examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/setup.py
 create mode 100644 examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/requirements.txt
 create mode 100644 examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/run_benchmark.sh
 create mode 100644 examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/run_quant.sh
 create mode 100644 examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/README.md
 create mode 100644 examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/content_images/colva_beach_sq.jpg
 create mode 100644 examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/content_images/golden_gate_sq.jpg
 create mode 100644 examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/data_process.py
 create mode 100644 examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/main.py
 create mode 100644 examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/prepare_model.py
 create mode 100644 examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/requirements.txt
 create mode 100644 examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/run_benchmark.sh
 create mode 100644 examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/run_quant.sh
 create mode 100644 examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/style_images/kanagawa_great_wave.jpg
 create mode 100644 examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/style_images/zigzag_colorful.jpg

diff --git a/examples/.config/model_params_tensorflow_3x.json b/examples/.config/model_params_tensorflow_3x.json
index 70e1497e508..f81b8abfeb3 100644
--- a/examples/.config/model_params_tensorflow_3x.json
+++ b/examples/.config/model_params_tensorflow_3x.json
@@ -8,6 +8,20 @@
       "batch_size": 64,
       "fp32_model_url": "https://storage.googleapis.com/intel-optimized-tensorflow/models/v2_7_0/fp32_bert_squad.pb"
     },
+    "distilbert_base": {
+      "model_src_dir": "nlp/distilbert_base/quantization/ptq",
+      "dataset_location": "/tf_dataset2/datasets/sst2_validation_dataset",
+      "input_model": "/tf_dataset2/models/tensorflow/distilbert_base/fp32/distilbert_base_fp32.pb",
+      "main_script": "run_inference.py",
+      "batch_size": 128
+    },
+    "distilbert_base_sq": {
+      "model_src_dir": "nlp/distilbert_base/quantization/ptq",
+      "dataset_location": "/tf_dataset2/datasets/sst2_validation_dataset",
+      "input_model": "/tf_dataset2/models/tensorflow/distilbert_base/fp32/distilbert_base_fp32.pb",
+      "main_script": "run_inference.py",
+      "batch_size": 128
+    },
     "opt_125m_sq": {
       "model_src_dir": "nlp/large_language_models/quantization/ptq/smoothquant",
       "dataset_location": "",
@@ -29,8 +43,44 @@
       "main_script": "main.py",
       "batch_size": 1
     },
+    "transformer_lt": {
+      "model_src_dir": "nlp/transformer_lt/quantization/ptq",
+      "dataset_location": "/tf_dataset/tensorflow/transformer-lt-official-fp32-inference/transformer_lt_official_fp32_pretrained_model/data",
+      "input_model": "/tf_dataset/tensorflow/transformer-lt-official-fp32-inference/transformer_lt_official_fp32_pretrained_model/graph/fp32_graphdef.pb",
+      "main_script": "main.py",
+      "batch_size": 64
+    },
+    "densenet121": {
+      "model_src_dir": "cv/densenet121/quantization/ptq",
+      "dataset_location": "/tf_dataset/dataset/imagenet",
+      "input_model": "/tf_dataset/tensorflow/densenet/densenet-121.pb",
+      "main_script": "main.py",
+      "batch_size": 32
+    },
+    "inception_v3": {
+      "model_src_dir": "cv/inception_v3/quantization/ptq",
+      "dataset_location": "/tf_dataset/dataset/imagenet",
+      "input_model": "/tf_dataset/pre-trained-models/inceptionv3/fp32/freezed_inceptionv3.pb",
+      "main_script": "main.py",
+      "batch_size": 32,
+      "fp32_model_url": "https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_8/inceptionv3_fp32_pretrained_model.pb"
+    },
+    "mobilenetv2": {
+      "model_src_dir": "cv/mobilenet_v2/quantization/ptq",
+      "dataset_location": "/tf_dataset/dataset/imagenet",
+      "input_model": "/tf_dataset/pre-train-model-slim/pbfile/frozen_pb/frozen_mobilenet_v2.pb",
+      "main_script": "main.py",
+      "batch_size": 32
+    },
+    "vgg16": {
+      "model_src_dir": "cv/vgg16/quantization/ptq",
+      "dataset_location": "/tf_dataset/dataset/imagenet",
+      "input_model": "/tf_dataset/pre-train-model-slim/pbfile/frozen_pb/frozen_vgg16.pb",
+      "main_script": "main.py",
+      "batch_size": 32
+    },
     "ViT": {
-      "model_src_dir": "image_recognition/vision_transformer/quantization/ptq",
+      "model_src_dir": "cv/vision_transformer/quantization/ptq",
       "dataset_location": "/tf_dataset/dataset/imagenet",
       "input_model": "/tf_dataset/tensorflow/vit/HF-ViT-Base16-Img224-frozen.pb",
       "main_script": "main.py",
@@ -42,6 +92,63 @@
       "input_model": "/tf_dataset/tensorflow/graphsage/graphsage_frozen_model.pb",
       "main_script": "main.py",
       "batch_size": 1000
+    },
+    "faster_rcnn_resnet50": {
+      "model_src_dir": "object_detection/faster_rcnn_resnet50/quantization/ptq",
+      "dataset_location": "/tf_dataset/tensorflow/coco_val.record",
+      "input_model": "/tf_dataset/pre-train-model-oob/object_detection/faster_rcnn_resnet50/frozen_inference_graph.pb",
+      "main_script": "main.py",
+      "batch_size": 10
+    },
+    "mask_rcnn_inception_v2": {
+      "model_src_dir": "object_detection/mask_rcnn_inception_v2/quantization/ptq",
+      "dataset_location": "/tf_dataset/tensorflow/coco_val.record",
+      "input_model": "/tf_dataset/pre-train-model-oob/object_detection/mask_rcnn_inception_v2/frozen_inference_graph.pb",
+      "main_script": "main.py",
+      "batch_size": 10
+    },
+    "mask_rcnn_inception_v2_ckpt": {
+      "model_src_dir": "object_detection/mask_rcnn_inception_v2/quantization/ptq",
+      "dataset_location": "/tf_dataset/tensorflow/coco_val.record",
+      "input_model": "/tf_dataset/pre-train-model-oob/object_detection/mask_rcnn_inception_v2",
+      "main_script": "main.py",
+      "batch_size": 10
+    },
+    "ssd_mobilenet_v1": {
+      "model_src_dir": "object_detection/ssd_mobilenet_v1/quantization/ptq",
+      "dataset_location": "/tf_dataset/tensorflow/coco_val.record",
+      "input_model": "/tf_dataset/pre-train-model-oob/object_detection/ssd_mobilenet_v1/frozen_inference_graph.pb",
+      "main_script": "main.py",       
+      "batch_size": 10
+    },
+    "ssd_mobilenet_v1_ckpt": {
+      "model_src_dir": "object_detection/ssd_mobilenet_v1/quantization/ptq",
+      "dataset_location": "/tf_dataset/tensorflow/coco_val.record",
+      "input_model": "/tf_dataset/pre-train-model-oob/object_detection/ssd_mobilenet_v1",
+      "main_script": "main.py",       
+      "batch_size": 10
+    },
+    "wide_deep_large_ds": {
+      "model_src_dir": "recommendation/wide_deep_large_ds/quantization/ptq",
+      "dataset_location": "/tf_dataset/tensorflow/wide_deep_large_ds/dataset",
+      "input_model": "/tf_dataset/tensorflow/wide_deep_large_ds/fp32_optimized_graph.pb",
+      "main_script": "inference.py",
+      "batch_size": 256,
+      "fp32_model_url": "https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_8/wide_deep_fp32_pretrained_model.pb"
+    },
+    "3dunet-mlperf": {
+      "model_src_dir": "semantic_image_segmentation/3dunet-mlperf/quantization/ptq",
+      "dataset_location": "/tf_dataset2/models/tensorflow/3dunet/build",
+      "input_model": "/tf_dataset2/models/tensorflow/3dunet/3dunet_dynamic_ndhwc.pb",
+      "main_script": "run_accuracy.py",
+      "batch_size": 100
+    },
+    "style_transfer": {
+      "model_src_dir": "style_transfer/arbitrary_style_transfer/quantization/ptq",
+      "dataset_location": "style_images,content_images",
+      "input_model": "/tf_dataset/tensorflow/style_transfer/arbitrary_style_transfer/model.ckpt",
+      "main_script": "style_tune.py",
+      "batch_size": 1
     }
   }
 }
diff --git a/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/README.md b/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/README.md
new file mode 100644
index 00000000000..7dbe04de7ca
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/README.md
@@ -0,0 +1,78 @@
+Step-by-Step
+============
+
+This document list steps of reproducing densenet121 model tuning and benchmark results via Neural Compressor.
+This example can run on Intel CPUs and GPUs.
+
+> **Note**: 
+> The models is supported in Validated TensorFlow [Version](/docs/source/installation_guide.md#validated-software-environment).
+# Prerequisite
+
+## 1. Environment
+
+### Installation
+Recommend python 3.9 or higher version.
+```shell
+pip install -r requirements.txt
+```
+
+### Install Intel Extension for Tensorflow
+#### Quantizing the model on Intel GPU(Mandatory to install ITEX)
+Intel Extension for Tensorflow is mandatory to be installed for quantizing the model on Intel GPUs.
+
+```shell
+pip install --upgrade intel-extension-for-tensorflow[xpu]
+```
+For any more details, please follow the procedure in [install-gpu-drivers](https://github.com/intel/intel-extension-for-tensorflow/blob/main/docs/install/install_for_xpu.md#install-gpu-drivers)
+
+#### Quantizing the model on Intel CPU(Optional to install ITEX)
+Intel Extension for Tensorflow for Intel CPUs is experimental currently. It's not mandatory for quantizing the model on Intel CPUs.
+
+```shell
+pip install --upgrade intel-extension-for-tensorflow[cpu]
+```
+> **Note**: 
+> The version compatibility of stock Tensorflow and ITEX can be checked [here](https://github.com/intel/intel-extension-for-tensorflow#compatibility-table). Please make sure you have installed compatible Tensorflow and ITEX.
+
+## 2. Prepare pre-trained model
+The densenet-series comes from [tensorflow-densenet](https://github.com/pudae/tensorflow-densenet), please also follow the step [Prepare pre-trained model](#3-prepare-pre-trained-model) to get the pb files or use openvino download tools.
+   ```shell
+   git clone https://github.com/openvinotoolkit/open_model_zoo.git
+   cd open_model_zoo/tools/downloader
+   git checkout tags/2021.2
+   pip install -r requirements.in
+   python downloader.py --name densenet-{121|161|169}-tf -o /PATH/TO/MODEL
+   ```
+
+## 3. Prepare Dataset
+
+  TensorFlow [models](https://github.com/tensorflow/models) repo provides [scripts and instructions](https://github.com/tensorflow/models/tree/master/research/slim#an-automated-script-for-processing-imagenet-data) to download, process and convert the ImageNet dataset to the TF records format.
+  We also prepared related scripts in ` examples/3.x_api/tensorflow/cv` directory. To download the raw images, the user must create an account with image-net.org. If you have downloaded the raw data and preprocessed the validation data by moving the images into the appropriate sub-directory based on the label (synset) of the image. we can use below command ro convert it to tf records format.
+
+  ```shell
+  cd examples/3.x_api/tensorflow/cv
+  # convert validation subset
+  bash prepare_dataset.sh --output_dir=./densenet121/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
+  # convert train subset
+  bash prepare_dataset.sh --output_dir=./densenet121/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/train/ --subset=train
+  ```
+> **Note**: 
+> The raw ImageNet dataset resides in JPEG files should be in the following directory structure. Taking validation set as an example:<br>
+> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/PATH/TO/img_raw/val/n01440764/ILSVRC2012_val_00000293.JPEG<br>
+> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/PATH/TO/img_raw/val/n01440764/ILSVRC2012_val_00000543.JPEG<br>
+> where 'n01440764' is the unique synset label associated with these images.
+
+# Run
+
+## 1 Quantization
+
+  ```shell
+  bash run_quant.sh --input_model=/PATH/TO/densenet-121.pb \
+      --output_model=./nc_densenet121.pb --dataset_location=/path/to/ImageNet/
+  ```
+
+## 2. Benchmark
+  ```shell
+  bash run_benchmark.sh --input_model=./nc_densenet121.pb --mode=accuracy --dataset_location=/path/to/ImageNet/ --batch_size=32
+  bash run_benchmark.sh --input_model=./nc_densenet121.pb --mode=performance --dataset_location=/path/to/ImageNet/ --batch_size=1
+  ```
diff --git a/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/data_process.py b/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/data_process.py
new file mode 100644
index 00000000000..17b4d9cec5e
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/data_process.py
@@ -0,0 +1,581 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2024 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import os
+import collections
+
+import numpy as np
+import tensorflow as tf
+
+from abc import abstractmethod
+from neural_compressor.common import logger
+from neural_compressor.tensorflow.utils.data import default_collate
+
+class ParseDecodeImagenet:
+    """Parse features in Example proto.
+
+    Returns:
+        tuple of parsed image and label
+    """
+
+    def __call__(self, sample):
+        """Parse features in example."""
+        # Dense features in Example proto.
+        feature_map = {
+            "image/encoded": tf.io.FixedLenFeature([], dtype=tf.string, default_value=""),
+            "image/class/label": tf.io.FixedLenFeature([1], dtype=tf.int64, default_value=-1),
+        }
+
+        sparse_float32 = tf.io.VarLenFeature(dtype=tf.float32)
+        # Sparse features in Example proto.
+        feature_map.update(
+            {
+                k: sparse_float32
+                for k in [
+                    "image/object/bbox/xmin",
+                    "image/object/bbox/ymin",
+                    "image/object/bbox/xmax",
+                    "image/object/bbox/ymax",
+                ]
+            }
+        )
+
+        features = tf.io.parse_single_example(serialized=sample, features=feature_map)
+        label = tf.cast(features["image/class/label"], dtype=tf.int32)
+        image = features["image/encoded"]
+        image = tf.image.decode_jpeg(image, channels=3, fancy_upscaling=False, dct_method="INTEGER_FAST")
+        return (image, label)
+
+
+class ResizeCropImagenet(object):
+    """Combination of a series of transforms which is applicable to images in Imagenet.
+
+    Args:
+        height (int): Height of the result
+        width (int): Width of the result
+        random_crop (bool, default=False): whether to random crop
+        resize_side (int, default=256):desired shape after resize operation
+        random_flip_left_right (bool, default=False): whether to random flip left and right
+        mean_value (list, default=[0.0,0.0,0.0]):means for each channel
+        scale (float, default=1.0):std value
+
+    Returns:
+        tuple of processed image and label
+    """
+
+    def __init__(
+        self,
+        height,
+        width,
+        random_crop=False,
+        resize_side=256,
+        resize_method="bilinear",
+        random_flip_left_right=False,
+        mean_value=[0.0, 0.0, 0.0],
+        scale=1.0,
+        data_format="channels_last",
+        subpixels="RGB",
+    ):
+        """Initialize `TensorflowResizeCropImagenetTransform` class."""
+        self.height = height
+        self.width = width
+        self.mean_value = mean_value
+        self.scale = scale
+        self.random_crop = random_crop
+        self.random_flip_left_right = random_flip_left_right
+        self.resize_side = resize_side
+        self.resize_method = resize_method
+        self.data_format = data_format
+        self.subpixels = subpixels
+
+    # sample is (images, labels)
+    def __call__(self, sample):
+        """Convert `TensorflowResizeCropImagenetTransform` feature."""
+        image, label = sample
+        shape = tf.shape(input=image)
+
+        height = (
+            tf.cast(shape[0], dtype=tf.float32)
+            if self.data_format == "channels_last"
+            else tf.cast(shape[1], dtype=tf.float32)
+        )
+        width = (
+            tf.cast(shape[1], dtype=tf.float32)
+            if self.data_format == "channels_last"
+            else tf.cast(shape[2], dtype=tf.float32)
+        )
+        scale = tf.cond(
+            pred=tf.greater(height, width),
+            true_fn=lambda: self.resize_side / width,
+            false_fn=lambda: self.resize_side / height,
+        )
+
+        scale = tf.cast(scale, dtype=tf.float32)
+        new_height = tf.cast(tf.math.rint(height * scale), dtype=tf.int32)
+        new_width = tf.cast(tf.math.rint(width * scale), dtype=tf.int32)
+
+        if self.subpixels == "BGR" and self.data_format == "channels_first":
+            # 'RGB'->'BGR'
+            image = tf.cond(
+                tf.equal(tf.rank(image), 3),
+                lambda: tf.experimental.numpy.moveaxis(image[::-1, ...], 0, -1),
+                lambda: tf.experimental.numpy.moveaxis(image[:, ::-1, ...], 1, -1),
+            )
+        elif self.subpixels == "BGR":
+            # 'RGB'->'BGR'
+            image = image[..., ::-1]
+        image = tf.expand_dims(image, 0)
+        image = tf.image.resize(image, [new_height, new_width], method=self.resize_method)
+        image = tf.squeeze(image)
+        shape = tf.shape(input=image)
+        if self.random_crop:
+            y0 = tf.random.uniform(shape=[], minval=0, maxval=(shape[0] - self.height + 1), dtype=tf.dtypes.int32)
+            x0 = tf.random.uniform(shape=[], minval=0, maxval=(shape[1] - self.width + 1), dtype=tf.dtypes.int32)
+        else:
+            y0 = (shape[0] - self.height) // 2
+            x0 = (shape[1] - self.width) // 2
+
+        image = tf.image.crop_to_bounding_box(image, y0, x0, self.height, self.width)
+        image.set_shape([self.height, self.width, 3])
+        if self.random_flip_left_right:
+            image = tf.image.random_flip_left_right(image)
+        means = tf.broadcast_to(self.mean_value, tf.shape(input=image))
+        image = (image - means) * self.scale
+        return (image, label)
+
+
+class ComposeTransform(object):
+    """Composes several transforms together.
+
+    Args:
+        transform_list (list of Transform objects):  list of transforms to compose
+
+    Returns:
+        sample (tuple): tuple of processed image and label
+    """
+
+    def __init__(self, transform_list):
+        """Initialize `ComposeTransform` class."""
+        self.transform_list = transform_list
+
+    def __call__(self, sample):
+        """Call transforms in transform_list."""
+        for transform in self.transform_list:
+            sample = transform(sample)
+        return sample
+
+
+class LabelShift(object):
+    """Convert label to label - label_shift.
+
+    Args:
+        label_shift(int, default=0): number of label shift
+
+    Returns:
+        tuple of processed image and label
+    """
+
+    def __init__(self, label_shift=0):
+        """Initialize `LabelShift` class."""
+        self.label_shift = label_shift
+
+    def __call__(self, sample):
+        """Convert label to label_shift."""
+        images, labels = sample
+        if isinstance(labels, np.ndarray):
+            labels = labels - self.label_shift
+        elif isinstance(labels, list):
+            if isinstance(labels[0], tuple):
+                labels = [tuple(np.array(label) - self.label_shift) for label in labels]
+            elif isinstance(labels[0], np.ndarray):
+                labels = [label - self.label_shift for label in labels]
+            else:
+                labels = np.array(labels) - self.label_shift
+                labels = labels.tolist()
+        else:
+            labels = np.array(labels) - self.label_shift
+        return images, labels
+
+
+class ImageRecordDataset(object):
+    """Tensorflow imageNet database in tf record format.
+
+    Please arrange data in this way:
+        root/validation-000-of-100
+        root/validation-001-of-100
+        ...
+        root/validation-099-of-100
+    The file name needs to follow this pattern: '* - * -of- *'
+
+    Args: root (str): Root directory of dataset.
+          transform (transform object, default=None):  transform to process input data.
+          filter (Filter objects, default=None): filter out examples according
+                                                 to specific conditions.
+    """
+
+    """Configuration for Imagenet dataset."""
+
+    def __new__(cls, root, transform=None, filter=None):
+        """Build a new object of TensorflowImageRecord class."""
+        from tensorflow.python.platform import gfile  # pylint: disable=no-name-in-module
+
+        glob_pattern = os.path.join(root, "*-*-of-*")
+        file_names = gfile.Glob(glob_pattern)
+        if not file_names:
+            raise ValueError("Found no files in --root matching: {}".format(glob_pattern))
+
+        # pylint: disable=no-name-in-module
+        from tensorflow.python.data.experimental import parallel_interleave
+
+        ds = tf.data.TFRecordDataset.list_files(file_names, shuffle=False)
+        ds = ds.apply(parallel_interleave(tf.data.TFRecordDataset, cycle_length=len(file_names)))
+
+        if transform is not None:
+            transform.transform_list.insert(0, ParseDecodeImagenet())
+        else:
+            transform = ParseDecodeImagenet()
+        ds = ds.map(transform, num_parallel_calls=None)
+        ds = ds.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)  # this number can be tuned
+        return ds
+
+
+class BaseMetric(object):
+    """The base class of Metric."""
+
+    def __init__(self, metric, single_output=False, hvd=None):
+        """Initialize the basic metric.
+
+        Args:
+            metric: The metric class.
+            single_output: Whether the output is single or not, defaults to False.
+            hvd: The Horovod class for distributed training, defaults to None.
+        """
+        self._metric_cls = metric
+        self._single_output = single_output
+        self._hvd = hvd
+
+    def __call__(self, *args, **kwargs):
+        """Evaluate the model predictions, and the reference.
+
+        Returns:
+            The class itself.
+        """
+        self._metric = self._metric_cls(*args, **kwargs)
+        return self
+
+    @abstractmethod
+    def update(self, preds, labels=None, sample_weight=None):
+        """Update the state that need to be evaluated.
+
+        Args:
+            preds: The prediction result.
+            labels: The reference. Defaults to None.
+            sample_weight: The sampling weight. Defaults to None.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def reset(self):
+        """Clear the predictions and labels.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def result(self):
+        """Evaluate the difference between predictions and labels.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @property
+    def metric(self):
+        """Return its metric class.
+
+        Returns:
+            The metric class.
+        """
+        return self._metric_cls
+
+    @property
+    def hvd(self):
+        """Return its hvd class.
+
+        Returns:
+            The hvd class.
+        """
+        return self._hvd
+
+    @hvd.setter
+    def hvd(self, hvd):
+        """Set its hvd.
+
+        Args:
+            hvd: The Horovod class for distributed training.
+        """
+        self._hvd = hvd
+
+
+class TopKMetric(BaseMetric):
+    """Compute Top-k Accuracy classification score for Tensorflow model.
+
+    This metric computes the number of times where the correct label is among
+    the top k labels predicted.
+
+    Attributes:
+        k (int): The number of most likely outcomes considered to find the correct label.
+        num_correct: The number of predictions that were correct classified.
+        num_sample: The total number of predictions.
+    """
+
+    def __init__(self, k=1):
+        """Initialize the k, number of samples and correct predictions.
+
+        Args:
+            k: The number of most likely outcomes considered to find the correct label.
+        """
+        self.k = k
+        self.num_correct = 0
+        self.num_sample = 0
+
+    def update(self, preds, labels, sample_weight=None):
+        """Add the predictions and labels.
+
+        Args:
+            preds: The predictions.
+            labels: The labels corresponding to the predictions.
+            sample_weight: The sample weight.
+        """
+        preds, labels = TopKMetric._topk_shape_validate(preds, labels)
+
+        labels = labels.reshape([len(labels)])
+        with tf.Graph().as_default() as acc_graph:
+            topk = tf.nn.in_top_k(
+                predictions=tf.constant(preds, dtype=tf.float32), targets=tf.constant(labels, dtype=tf.int32), k=self.k
+            )
+            fp32_topk = tf.cast(topk, tf.float32)
+            correct_tensor = tf.reduce_sum(input_tensor=fp32_topk)
+
+            with tf.compat.v1.Session() as acc_sess:
+                correct = acc_sess.run(correct_tensor)
+
+        self.num_sample += len(labels)
+        self.num_correct += correct
+
+    def reset(self):
+        """Reset the number of samples and correct predictions."""
+        self.num_correct = 0
+        self.num_sample = 0
+
+    def result(self):
+        """Compute the top-k score.
+
+        Returns:
+            The top-k score.
+        """
+        if self.num_sample == 0:
+            logger.warning("Sample num during evaluation is 0.")
+            return 0
+        elif getattr(self, "_hvd", None) is not None:  # pragma: no cover
+            allgather_num_correct = sum(self._hvd.allgather_object(self.num_correct))
+            allgather_num_sample = sum(self._hvd.allgather_object(self.num_sample))
+            return allgather_num_correct / allgather_num_sample
+        return self.num_correct / self.num_sample
+
+    @staticmethod
+    def _topk_shape_validate(preds, labels):
+        # preds shape can be Nxclass_num or class_num(N=1 by default)
+        # it's more suitable for 'Accuracy' with preds shape Nx1(or 1) output from argmax
+        if isinstance(preds, int):
+            preds = [preds]
+            preds = np.array(preds)
+        elif isinstance(preds, np.ndarray):
+            preds = np.array(preds)
+        elif isinstance(preds, list):
+            preds = np.array(preds)
+            preds = preds.reshape((-1, preds.shape[-1]))
+
+        # consider labels just int value 1x1
+        if isinstance(labels, int):
+            labels = [labels]
+            labels = np.array(labels)
+        elif isinstance(labels, tuple):
+            labels = np.array([labels])
+            labels = labels.reshape((labels.shape[-1], -1))
+        elif isinstance(labels, list):
+            if isinstance(labels[0], int):
+                labels = np.array(labels)
+                labels = labels.reshape((labels.shape[0], 1))
+            elif isinstance(labels[0], tuple):
+                labels = np.array(labels)
+                labels = labels.reshape((labels.shape[-1], -1))
+            else:
+                labels = np.array(labels)
+        # labels most have 2 axis, 2 cases: N(or Nx1 sparse) or Nxclass_num(one-hot)
+        # only support 2 dimension one-shot labels
+        # or 1 dimension one-hot class_num will confuse with N
+
+        if len(preds.shape) == 1:
+            N = 1
+            class_num = preds.shape[0]
+            preds = preds.reshape([-1, class_num])
+        elif len(preds.shape) >= 2:
+            N = preds.shape[0]
+            preds = preds.reshape([N, -1])
+            class_num = preds.shape[1]
+
+        label_N = labels.shape[0]
+        assert label_N == N, "labels batch size should same with preds"
+        labels = labels.reshape([N, -1])
+        # one-hot labels will have 2 dimension not equal 1
+        if labels.shape[1] != 1:
+            labels = labels.argsort()[..., -1:]
+        return preds, labels
+    
+
+class TFDataLoader(object):  # pragma: no cover
+    """Tensorflow dataloader class.
+
+    In tensorflow1.x dataloader is coupled with the graph, but it also support feed_dict
+    method to do session run, this dataloader is designed to satisfy the usage of feed dict
+    in tf1.x. Although it's a general dataloader and can be used in MXNet and PyTorch.
+
+    Args:
+        dataset: obj. wrapper of needed data.
+        batch_size: int. batch size
+    """
+
+    def __init__(self, dataset, batch_size=1, last_batch="rollover"):
+        """Initialize `TFDataDataLoader` class."""
+        self.dataset = dataset
+        self.last_batch = last_batch
+        self.batch_size = batch_size
+        dataset = dataset.batch(batch_size)
+
+    def batch(self, batch_size, last_batch="rollover"):
+        """Dataset return data per batch."""
+        drop_last = False if last_batch == "rollover" else True
+        self.batch_size = batch_size
+        self.dataset = self.dataset.batch(batch_size, drop_last)
+
+    def __iter__(self):
+        """Iterate dataloader."""
+        return self._generate_dataloader(
+            self.dataset,
+            batch_size=self.batch_size,
+            last_batch=self.last_batch,
+        )
+
+    def _generate_dataloader(
+        self,
+        dataset,
+        batch_size=1,
+        last_batch="rollover",
+        collate_fn=None,
+        sampler=None,
+        batch_sampler=None,
+        num_workers=None,
+        pin_memory=None,
+        distributed=False,
+    ):
+        """Yield data."""
+        drop_last = False if last_batch == "rollover" else True
+
+        def check_dynamic_shape(element_spec):
+            if isinstance(element_spec, collections.abc.Sequence):
+                return any([check_dynamic_shape(ele) for ele in element_spec])
+            elif isinstance(element_spec, tf.TensorSpec):
+                return True if element_spec.shape.num_elements() is None else False
+            else:
+                raise ValueError("unrecognized element spec...")
+
+        def squeeze_output(output):
+            if isinstance(output, collections.abc.Sequence):
+                return [squeeze_output(ele) for ele in output]
+            elif isinstance(output, np.ndarray):
+                return np.squeeze(output, axis=0)
+            else:
+                raise ValueError("not supported output format....")
+
+        if tf.executing_eagerly():
+            index = 0
+            outputs = []
+            for iter_tensors in dataset:
+                samples = []
+                iter_inputs, iter_labels = iter_tensors[0], iter_tensors[1]
+                if isinstance(iter_inputs, tf.Tensor):
+                    samples.append(iter_inputs.numpy())
+                else:
+                    samples.append(tuple(iter_input.numpy() for iter_input in iter_inputs))
+                if isinstance(iter_labels, tf.Tensor):
+                    samples.append(iter_labels.numpy())
+                else:
+                    samples.append([np.array(l) for l in iter_labels])
+                index += 1
+                outputs.append(samples)
+                if index == batch_size:
+                    outputs = default_collate(outputs)
+                    yield outputs
+                    outputs = []
+                    index = 0
+            if len(outputs) > 0:
+                outputs = default_collate(outputs)
+                yield outputs
+        else:
+            try_single_batch = check_dynamic_shape(dataset.element_spec)
+            dataset = dataset.batch(1 if try_single_batch else batch_size, drop_last)
+            ds_iterator = tf.compat.v1.data.make_one_shot_iterator(dataset)
+            iter_tensors = ds_iterator.get_next()
+            data_config = tf.compat.v1.ConfigProto()
+            data_config.use_per_session_threads = 1
+            data_config.intra_op_parallelism_threads = 1
+            data_config.inter_op_parallelism_threads = 16
+            data_sess = tf.compat.v1.Session(config=data_config)
+            # pylint: disable=no-name-in-module
+            from tensorflow.python.framework.errors_impl import OutOfRangeError
+
+            while True:
+                if not try_single_batch:
+                    try:
+                        outputs = data_sess.run(iter_tensors)
+                        yield outputs
+                    except OutOfRangeError:
+                        data_sess.close()
+                        return
+                else:
+                    try:
+                        outputs = []
+                        for i in range(0, batch_size):
+                            outputs.append(squeeze_output(data_sess.run(iter_tensors)))
+                        outputs = default_collate(outputs)
+                        yield outputs
+                    except OutOfRangeError:
+                        if len(outputs) == 0:
+                            data_sess.close()
+                            return
+                        else:
+                            outputs = default_collate(outputs)
+                            yield outputs
+                            data_sess.close()
+                            return
diff --git a/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/main.py b/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/main.py
new file mode 100644
index 00000000000..21ca3bcb3d8
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/main.py
@@ -0,0 +1,147 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2023 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import time
+
+import numpy as np
+import tensorflow as tf
+
+from argparse import ArgumentParser
+from data_process import (
+    ImageRecordDataset, 
+    ComposeTransform, 
+    ResizeCropImagenet, 
+    LabelShift,
+    TFDataLoader,
+    TopKMetric
+)
+
+tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
+
+arg_parser = ArgumentParser(description='Parse args')
+arg_parser.add_argument('-g', "--input-graph",
+                        help='Specify the input graph for the transform tool',
+                        dest='input_graph')
+arg_parser.add_argument("--output-graph",
+                        help='Specify tune result model save dir',
+                        dest='output_graph')
+arg_parser.add_argument('--benchmark', dest='benchmark', action='store_true', help='run benchmark')
+arg_parser.add_argument('--mode', dest='mode', default='performance', help='benchmark mode')
+arg_parser.add_argument('--tune', dest='tune', action='store_true', help='use neural_compressor to tune.')
+arg_parser.add_argument('--dataset_location', dest='dataset_location',
+                          help='location of calibration dataset and evaluate dataset')
+arg_parser.add_argument('--batch_size', type=int, default=32, dest='batch_size', help='batch_size of benchmark')
+arg_parser.add_argument('--iters', type=int, default=100, dest='iters', help='interations')
+args = arg_parser.parse_args()
+
+def evaluate(model, eval_dataloader, metric, postprocess=None):
+    """Custom evaluate function to estimate the accuracy of the model.
+
+    Args:
+        model (tf.Graph_def): The input model graph
+        
+    Returns:
+        accuracy (float): evaluation result, the larger is better.
+    """
+    from neural_compressor.tensorflow import Model
+    model = Model(model)
+    input_tensor = model.input_tensor
+    output_tensor = model.output_tensor if len(model.output_tensor)>1 else \
+                        model.output_tensor[0]
+    iteration = -1
+    if args.benchmark and args.mode == 'performance':
+        iteration = args.iters
+
+    def eval_func(dataloader):
+        latency_list = []
+        for idx, (inputs, labels) in enumerate(dataloader):
+            # dataloader should keep the order and len of inputs same with input_tensor
+            inputs = np.array([inputs])
+            feed_dict = dict(zip(input_tensor, inputs))
+
+            start = time.time()
+            predictions = model.sess.run(output_tensor, feed_dict)
+            end = time.time()
+
+            if postprocess:
+                predictions, labels = postprocess((predictions, labels))
+
+            metric.update(predictions, labels)
+            latency_list.append(end-start)
+            if idx + 1 == iteration:
+                break
+        latency = np.array(latency_list).mean() / args.batch_size
+        return latency
+
+    latency = eval_func(eval_dataloader)
+    if args.benchmark and args.mode == 'performance':
+        print("Batch size = {}".format(args.batch_size))
+        print("Latency: {:.3f} ms".format(latency * 1000))
+        print("Throughput: {:.3f} images/sec".format(1. / latency))
+    acc = metric.result()
+    return acc
+
+class eval_classifier_optimized_graph:
+    """Evaluate image classifier with optimized TensorFlow graph."""
+
+    def run(self):
+        """This is neural_compressor function include tuning, export and benchmark option."""
+        from neural_compressor.common import set_random_seed
+        set_random_seed(9527)
+
+        if args.tune:
+            from neural_compressor.tensorflow import StaticQuantConfig, quantize_model
+
+            dataset = ImageRecordDataset(
+                root=args.dataset_location, 
+                transform=ComposeTransform(transform_list= [
+                    ResizeCropImagenet(height=224, width=224, scale=0.017, mean_value=[123.68, 116.78, 103.94]),
+                    ]
+                )
+            )
+            calib_dataloader = TFDataLoader(dataset=dataset, batch_size=10)
+            
+            quant_config = StaticQuantConfig()
+            q_model = quantize_model(args.input_graph, quant_config, calib_dataloader)
+            q_model.save(args.output_graph)
+
+        if args.benchmark:
+            dataset = ImageRecordDataset(
+                root=args.dataset_location, 
+                transform=ComposeTransform(transform_list= [
+                    ResizeCropImagenet(height=224, width=224, scale=0.017, mean_value=[123.68, 116.78, 103.94]),
+                    ]
+                )
+            )
+            dataloader = TFDataLoader(dataset=dataset, batch_size=args.batch_size)
+
+            def eval(model):
+                top1 = TopKMetric(k=1)
+                postprocess = LabelShift(label_shift=1)
+                return evaluate(model, dataloader, top1, postprocess)
+
+            if args.mode == 'performance':
+                eval(args.input_graph)
+            elif args.mode == 'accuracy':
+                acc_result = eval(args.input_graph)
+                print("Batch size = %d" % dataloader.batch_size)
+                print("Accuracy: %.5f" % acc_result)
+
+if __name__ == "__main__":
+    evaluate_opt_graph = eval_classifier_optimized_graph()
+    evaluate_opt_graph.run()
diff --git a/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/requirements.txt b/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/requirements.txt
new file mode 100644
index 00000000000..2755e1a41ac
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/requirements.txt
@@ -0,0 +1,2 @@
+tensorflow
+neural-compressor
diff --git a/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/run_benchmark.sh b/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/run_benchmark.sh
new file mode 100644
index 00000000000..8ecac837cf7
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/run_benchmark.sh
@@ -0,0 +1,51 @@
+#!/bin/bash
+set -x
+
+function main {
+
+  init_params "$@"
+  run_benchmark
+
+}
+
+# init params
+function init_params {
+  batch_size=32
+  iters=100
+
+  for var in "$@"
+  do
+    case $var in
+      --input_model=*)
+          input_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --mode=*)
+          mode=$(echo $var |cut -f2 -d=)
+      ;;
+      --dataset_location=*)
+          dataset_location=$(echo $var |cut -f2 -d=)
+      ;;
+      --batch_size=*)
+          batch_size=$(echo $var |cut -f2 -d=)
+      ;;
+      --iters=*)
+          iters=$(echo $var |cut -f2 -d=)
+      ;;
+    esac
+  done
+
+}
+
+# run_tuning
+function run_benchmark {
+
+    python main.py \
+            --input-graph ${input_model} \
+            --mode ${mode} \
+            --dataset_location ${dataset_location} \
+            --batch_size ${batch_size} \
+            --benchmark \
+            --iters ${iters}
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/image_recognition/vision_transformer/quantization/ptq/run_quant.sh b/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/run_quant.sh
similarity index 100%
rename from examples/3.x_api/tensorflow/image_recognition/vision_transformer/quantization/ptq/run_quant.sh
rename to examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/run_quant.sh
diff --git a/examples/3.x_api/tensorflow/cv/inception_v3/quantization/ptq/README.md b/examples/3.x_api/tensorflow/cv/inception_v3/quantization/ptq/README.md
new file mode 100644
index 00000000000..34eb64fcf74
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/inception_v3/quantization/ptq/README.md
@@ -0,0 +1,75 @@
+Step-by-Step
+============
+
+This document list steps of reproducing inception_v3 model tuning and benchmark results via Neural Compressor.
+This example can run on Intel CPUs and GPUs.
+
+> **Note**: 
+> The models is supported in validated TensorFlow [Version](/docs/source/installation_guide.md#validated-software-environment).
+# Prerequisite
+
+## 1. Environment
+
+### Installation
+Recommend python 3.9 or higher version.
+```shell
+pip install -r requirements.txt
+```
+
+### Install Intel Extension for Tensorflow
+#### Quantizing the model on Intel GPU(Mandatory to install ITEX)
+Intel Extension for Tensorflow is mandatory to be installed for quantizing the model on Intel GPUs.
+
+```shell
+pip install --upgrade intel-extension-for-tensorflow[xpu]
+```
+For any more details, please follow the procedure in [install-gpu-drivers](https://github.com/intel/intel-extension-for-tensorflow/blob/main/docs/install/install_for_xpu.md#install-gpu-drivers)
+
+#### Quantizing the model on Intel CPU(Optional to install ITEX)
+Intel Extension for Tensorflow for Intel CPUs is experimental currently. It's not mandatory for quantizing the model on Intel CPUs.
+
+```shell
+pip install --upgrade intel-extension-for-tensorflow[cpu]
+```
+> **Note**: 
+> The version compatibility of stock Tensorflow and ITEX can be checked [here](https://github.com/intel/intel-extension-for-tensorflow#compatibility-table). Please make sure you have installed compatible Tensorflow and ITEX.
+
+## 2. Prepare pre-trained model
+
+  Download pre-trained PB
+  ```shell
+  wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_6/inceptionv3_fp32_pretrained_model.pb
+  ```
+
+## 3. Prepare Dataset
+
+  TensorFlow [models](https://github.com/tensorflow/models) repo provides [scripts and instructions](https://github.com/tensorflow/models/tree/master/research/slim#an-automated-script-for-processing-imagenet-data) to download, process and convert the ImageNet dataset to the TF records format.
+  We also prepared related scripts in ` examples/3.x_api/tensorflow/cv` directory. To download the raw images, the user must create an account with image-net.org. If you have downloaded the raw data and preprocessed the validation data by moving the images into the appropriate sub-directory based on the label (synset) of the image. we can use below command ro convert it to tf records format.
+
+  ```shell
+  cd examples/3.x_api/tensorflow/cv
+  # convert validation subset
+  bash prepare_dataset.sh --output_dir=./inception_v3/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
+  # convert train subset
+  bash prepare_dataset.sh --output_dir=./inception_v3/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/train/ --subset=train
+  ```
+> **Note**: 
+> The raw ImageNet dataset resides in JPEG files should be in the following directory structure. Taking validation set as an example:<br>
+> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/PATH/TO/img_raw/val/n01440764/ILSVRC2012_val_00000293.JPEG<br>
+> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/PATH/TO/img_raw/val/n01440764/ILSVRC2012_val_00000543.JPEG<br>
+> where 'n01440764' is the unique synset label associated with these images.
+
+# Run
+
+## 1 Quantization
+
+  ```shell
+  bash run_quant.sh --input_model=/PATH/TO/inceptionv3_fp32_pretrained_model.pb \
+      --output_model=./nc_inception_v3.pb --dataset_location=/path/to/ImageNet/
+  ```
+
+## 2. Benchmark
+  ```shell
+  bash run_benchmark.sh --input_model=./nc_inception_v3.pb --mode=accuracy --dataset_location=/path/to/ImageNet/ --batch_size=32
+  bash run_benchmark.sh --input_model=./nc_inception_v3.pb --mode=performance --dataset_location=/path/to/ImageNet/ --batch_size=1
+  ```
diff --git a/examples/3.x_api/tensorflow/cv/inception_v3/quantization/ptq/data_process.py b/examples/3.x_api/tensorflow/cv/inception_v3/quantization/ptq/data_process.py
new file mode 100644
index 00000000000..ecfca2348cd
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/inception_v3/quantization/ptq/data_process.py
@@ -0,0 +1,511 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2024 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import os
+import collections
+
+import numpy as np
+import tensorflow as tf
+
+from abc import abstractmethod
+from neural_compressor.common import logger
+from neural_compressor.tensorflow.utils.data import default_collate
+
+class ParseDecodeImagenet:
+    """Parse features in Example proto.
+
+    Returns:
+        tuple of parsed image and label
+    """
+
+    def __call__(self, sample):
+        """Parse features in example."""
+        # Dense features in Example proto.
+        feature_map = {
+            "image/encoded": tf.io.FixedLenFeature([], dtype=tf.string, default_value=""),
+            "image/class/label": tf.io.FixedLenFeature([1], dtype=tf.int64, default_value=-1),
+        }
+
+        sparse_float32 = tf.io.VarLenFeature(dtype=tf.float32)
+        # Sparse features in Example proto.
+        feature_map.update(
+            {
+                k: sparse_float32
+                for k in [
+                    "image/object/bbox/xmin",
+                    "image/object/bbox/ymin",
+                    "image/object/bbox/xmax",
+                    "image/object/bbox/ymax",
+                ]
+            }
+        )
+
+        features = tf.io.parse_single_example(serialized=sample, features=feature_map)
+        label = tf.cast(features["image/class/label"], dtype=tf.int32)
+        image = features["image/encoded"]
+        image = tf.image.decode_jpeg(image, channels=3, fancy_upscaling=False, dct_method="INTEGER_FAST")
+        return (image, label)
+
+
+class BilinearImagenetTransform(object):
+    """Combination of a series of transforms which is applicable to images in Imagenet.
+
+    Args:
+        height: Height of the result
+        width:Width of the result
+        central_fraction(float, default=0.875):fraction of size to crop
+        mean_value(list, default=[0.0,0.0,0.0]):means for each channel
+        scale(float, default=1.0):std value
+
+    Returns:
+        tuple of processed image and label
+    """
+
+    def __init__(self, height, width, central_fraction=0.875, mean_value=[0.0, 0.0, 0.0], scale=1.0):
+        """Initialize `BilinearImagenetTransform` class."""
+        self.height = height
+        self.width = width
+        self.mean_value = mean_value
+        self.scale = scale
+        self.central_fraction = central_fraction
+
+    # sample is (images, labels)
+    def __call__(self, sample):
+        """Convert `BilinearImagenetTransform` feature."""
+        image, label = sample
+        if image.dtype is not tf.float32:
+            image = tf.image.convert_image_dtype(image, dtype=tf.float32)
+        # Crop the central region of the image containing 87.5% area of the original image.
+        if self.central_fraction:
+            image = tf.image.central_crop(image, central_fraction=self.central_fraction)
+
+        if self.height and self.width:
+            # Resize the image to the specified height and width.
+            image = tf.expand_dims(image, 0)
+            image = tf.image.resize(image, [self.height, self.width], method=tf.image.ResizeMethod.BILINEAR)
+            image = tf.squeeze(image, [0])
+
+        image = tf.subtract(image, 0.5)
+        image = tf.multiply(image, 2.0)
+        means = tf.broadcast_to(self.mean_value, tf.shape(input=image))
+        image = (image - means) * self.scale
+        return (image, label)
+
+
+class ComposeTransform(object):
+    """Composes several transforms together.
+
+    Args:
+        transform_list (list of Transform objects):  list of transforms to compose
+
+    Returns:
+        sample (tuple): tuple of processed image and label
+    """
+
+    def __init__(self, transform_list):
+        """Initialize `ComposeTransform` class."""
+        self.transform_list = transform_list
+
+    def __call__(self, sample):
+        """Call transforms in transform_list."""
+        for transform in self.transform_list:
+            sample = transform(sample)
+        return sample
+
+
+class ShiftRescale(object):
+    """Label shift by 1 and rescale.
+
+    Returns:
+        tuple of processed image and label
+    """
+
+    def __call__(self, sample):
+        image, label = sample
+        label -= 1
+        image = (image - 127.5) / 127.5
+        return (image, label)
+
+
+class ImageRecordDataset(object):
+    """Tensorflow imageNet database in tf record format.
+
+    Please arrange data in this way:
+        root/validation-000-of-100
+        root/validation-001-of-100
+        ...
+        root/validation-099-of-100
+    The file name needs to follow this pattern: '* - * -of- *'
+
+    Args: root (str): Root directory of dataset.
+          transform (transform object, default=None):  transform to process input data.
+          filter (Filter objects, default=None): filter out examples according
+                                                 to specific conditions.
+    """
+
+    """Configuration for Imagenet dataset."""
+
+    def __new__(cls, root, transform=None, filter=None):
+        """Build a new object of TensorflowImageRecord class."""
+        from tensorflow.python.platform import gfile  # pylint: disable=no-name-in-module
+
+        glob_pattern = os.path.join(root, "*-*-of-*")
+        file_names = gfile.Glob(glob_pattern)
+        if not file_names:
+            raise ValueError("Found no files in --root matching: {}".format(glob_pattern))
+
+        # pylint: disable=no-name-in-module
+        from tensorflow.python.data.experimental import parallel_interleave
+
+        ds = tf.data.TFRecordDataset.list_files(file_names, shuffle=False)
+        ds = ds.apply(parallel_interleave(tf.data.TFRecordDataset, cycle_length=len(file_names)))
+
+        if transform is not None:
+            transform.transform_list.insert(0, ParseDecodeImagenet())
+        else:
+            transform = ParseDecodeImagenet()
+        ds = ds.map(transform, num_parallel_calls=None)
+        ds = ds.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)  # this number can be tuned
+        return ds
+
+
+class BaseMetric(object):
+    """The base class of Metric."""
+
+    def __init__(self, metric, single_output=False, hvd=None):
+        """Initialize the basic metric.
+
+        Args:
+            metric: The metric class.
+            single_output: Whether the output is single or not, defaults to False.
+            hvd: The Horovod class for distributed training, defaults to None.
+        """
+        self._metric_cls = metric
+        self._single_output = single_output
+        self._hvd = hvd
+
+    def __call__(self, *args, **kwargs):
+        """Evaluate the model predictions, and the reference.
+
+        Returns:
+            The class itself.
+        """
+        self._metric = self._metric_cls(*args, **kwargs)
+        return self
+
+    @abstractmethod
+    def update(self, preds, labels=None, sample_weight=None):
+        """Update the state that need to be evaluated.
+
+        Args:
+            preds: The prediction result.
+            labels: The reference. Defaults to None.
+            sample_weight: The sampling weight. Defaults to None.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def reset(self):
+        """Clear the predictions and labels.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def result(self):
+        """Evaluate the difference between predictions and labels.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @property
+    def metric(self):
+        """Return its metric class.
+
+        Returns:
+            The metric class.
+        """
+        return self._metric_cls
+
+    @property
+    def hvd(self):
+        """Return its hvd class.
+
+        Returns:
+            The hvd class.
+        """
+        return self._hvd
+
+    @hvd.setter
+    def hvd(self, hvd):
+        """Set its hvd.
+
+        Args:
+            hvd: The Horovod class for distributed training.
+        """
+        self._hvd = hvd
+
+
+class TopKMetric(BaseMetric):
+    """Compute Top-k Accuracy classification score for Tensorflow model.
+
+    This metric computes the number of times where the correct label is among
+    the top k labels predicted.
+
+    Attributes:
+        k (int): The number of most likely outcomes considered to find the correct label.
+        num_correct: The number of predictions that were correct classified.
+        num_sample: The total number of predictions.
+    """
+
+    def __init__(self, k=1):
+        """Initialize the k, number of samples and correct predictions.
+
+        Args:
+            k: The number of most likely outcomes considered to find the correct label.
+        """
+        self.k = k
+        self.num_correct = 0
+        self.num_sample = 0
+
+    def update(self, preds, labels, sample_weight=None):
+        """Add the predictions and labels.
+
+        Args:
+            preds: The predictions.
+            labels: The labels corresponding to the predictions.
+            sample_weight: The sample weight.
+        """
+        preds, labels = TopKMetric._topk_shape_validate(preds, labels)
+
+        labels = labels.reshape([len(labels)])
+        with tf.Graph().as_default() as acc_graph:
+            topk = tf.nn.in_top_k(
+                predictions=tf.constant(preds, dtype=tf.float32), targets=tf.constant(labels, dtype=tf.int32), k=self.k
+            )
+            fp32_topk = tf.cast(topk, tf.float32)
+            correct_tensor = tf.reduce_sum(input_tensor=fp32_topk)
+
+            with tf.compat.v1.Session() as acc_sess:
+                correct = acc_sess.run(correct_tensor)
+
+        self.num_sample += len(labels)
+        self.num_correct += correct
+
+    def reset(self):
+        """Reset the number of samples and correct predictions."""
+        self.num_correct = 0
+        self.num_sample = 0
+
+    def result(self):
+        """Compute the top-k score.
+
+        Returns:
+            The top-k score.
+        """
+        if self.num_sample == 0:
+            logger.warning("Sample num during evaluation is 0.")
+            return 0
+        elif getattr(self, "_hvd", None) is not None:  # pragma: no cover
+            allgather_num_correct = sum(self._hvd.allgather_object(self.num_correct))
+            allgather_num_sample = sum(self._hvd.allgather_object(self.num_sample))
+            return allgather_num_correct / allgather_num_sample
+        return self.num_correct / self.num_sample
+
+    @staticmethod
+    def _topk_shape_validate(preds, labels):
+        # preds shape can be Nxclass_num or class_num(N=1 by default)
+        # it's more suitable for 'Accuracy' with preds shape Nx1(or 1) output from argmax
+        if isinstance(preds, int):
+            preds = [preds]
+            preds = np.array(preds)
+        elif isinstance(preds, np.ndarray):
+            preds = np.array(preds)
+        elif isinstance(preds, list):
+            preds = np.array(preds)
+            preds = preds.reshape((-1, preds.shape[-1]))
+
+        # consider labels just int value 1x1
+        if isinstance(labels, int):
+            labels = [labels]
+            labels = np.array(labels)
+        elif isinstance(labels, tuple):
+            labels = np.array([labels])
+            labels = labels.reshape((labels.shape[-1], -1))
+        elif isinstance(labels, list):
+            if isinstance(labels[0], int):
+                labels = np.array(labels)
+                labels = labels.reshape((labels.shape[0], 1))
+            elif isinstance(labels[0], tuple):
+                labels = np.array(labels)
+                labels = labels.reshape((labels.shape[-1], -1))
+            else:
+                labels = np.array(labels)
+        # labels most have 2 axis, 2 cases: N(or Nx1 sparse) or Nxclass_num(one-hot)
+        # only support 2 dimension one-shot labels
+        # or 1 dimension one-hot class_num will confuse with N
+
+        if len(preds.shape) == 1:
+            N = 1
+            class_num = preds.shape[0]
+            preds = preds.reshape([-1, class_num])
+        elif len(preds.shape) >= 2:
+            N = preds.shape[0]
+            preds = preds.reshape([N, -1])
+            class_num = preds.shape[1]
+
+        label_N = labels.shape[0]
+        assert label_N == N, "labels batch size should same with preds"
+        labels = labels.reshape([N, -1])
+        # one-hot labels will have 2 dimension not equal 1
+        if labels.shape[1] != 1:
+            labels = labels.argsort()[..., -1:]
+        return preds, labels
+    
+
+class TFDataLoader(object):  # pragma: no cover
+    """Tensorflow dataloader class.
+
+    In tensorflow1.x dataloader is coupled with the graph, but it also support feed_dict
+    method to do session run, this dataloader is designed to satisfy the usage of feed dict
+    in tf1.x. Although it's a general dataloader and can be used in MXNet and PyTorch.
+
+    Args:
+        dataset: obj. wrapper of needed data.
+        batch_size: int. batch size
+    """
+
+    def __init__(self, dataset, batch_size=1, last_batch="rollover"):
+        """Initialize `TFDataDataLoader` class."""
+        self.dataset = dataset
+        self.last_batch = last_batch
+        self.batch_size = batch_size
+        dataset = dataset.batch(batch_size)
+
+    def batch(self, batch_size, last_batch="rollover"):
+        """Dataset return data per batch."""
+        drop_last = False if last_batch == "rollover" else True
+        self.batch_size = batch_size
+        self.dataset = self.dataset.batch(batch_size, drop_last)
+
+    def __iter__(self):
+        """Iterate dataloader."""
+        return self._generate_dataloader(
+            self.dataset,
+            batch_size=self.batch_size,
+            last_batch=self.last_batch,
+        )
+
+    def _generate_dataloader(
+        self,
+        dataset,
+        batch_size=1,
+        last_batch="rollover",
+        collate_fn=None,
+        sampler=None,
+        batch_sampler=None,
+        num_workers=None,
+        pin_memory=None,
+        distributed=False,
+    ):
+        """Yield data."""
+        drop_last = False if last_batch == "rollover" else True
+
+        def check_dynamic_shape(element_spec):
+            if isinstance(element_spec, collections.abc.Sequence):
+                return any([check_dynamic_shape(ele) for ele in element_spec])
+            elif isinstance(element_spec, tf.TensorSpec):
+                return True if element_spec.shape.num_elements() is None else False
+            else:
+                raise ValueError("unrecognized element spec...")
+
+        def squeeze_output(output):
+            if isinstance(output, collections.abc.Sequence):
+                return [squeeze_output(ele) for ele in output]
+            elif isinstance(output, np.ndarray):
+                return np.squeeze(output, axis=0)
+            else:
+                raise ValueError("not supported output format....")
+
+        if tf.executing_eagerly():
+            index = 0
+            outputs = []
+            for iter_tensors in dataset:
+                samples = []
+                iter_inputs, iter_labels = iter_tensors[0], iter_tensors[1]
+                if isinstance(iter_inputs, tf.Tensor):
+                    samples.append(iter_inputs.numpy())
+                else:
+                    samples.append(tuple(iter_input.numpy() for iter_input in iter_inputs))
+                if isinstance(iter_labels, tf.Tensor):
+                    samples.append(iter_labels.numpy())
+                else:
+                    samples.append([np.array(l) for l in iter_labels])
+                index += 1
+                outputs.append(samples)
+                if index == batch_size:
+                    outputs = default_collate(outputs)
+                    yield outputs
+                    outputs = []
+                    index = 0
+            if len(outputs) > 0:
+                outputs = default_collate(outputs)
+                yield outputs
+        else:
+            try_single_batch = check_dynamic_shape(dataset.element_spec)
+            dataset = dataset.batch(1 if try_single_batch else batch_size, drop_last)
+            ds_iterator = tf.compat.v1.data.make_one_shot_iterator(dataset)
+            iter_tensors = ds_iterator.get_next()
+            data_config = tf.compat.v1.ConfigProto()
+            data_config.use_per_session_threads = 1
+            data_config.intra_op_parallelism_threads = 1
+            data_config.inter_op_parallelism_threads = 16
+            data_sess = tf.compat.v1.Session(config=data_config)
+            # pylint: disable=no-name-in-module
+            from tensorflow.python.framework.errors_impl import OutOfRangeError
+
+            while True:
+                if not try_single_batch:
+                    try:
+                        outputs = data_sess.run(iter_tensors)
+                        yield outputs
+                    except OutOfRangeError:
+                        data_sess.close()
+                        return
+                else:
+                    try:
+                        outputs = []
+                        for i in range(0, batch_size):
+                            outputs.append(squeeze_output(data_sess.run(iter_tensors)))
+                        outputs = default_collate(outputs)
+                        yield outputs
+                    except OutOfRangeError:
+                        if len(outputs) == 0:
+                            data_sess.close()
+                            return
+                        else:
+                            outputs = default_collate(outputs)
+                            yield outputs
+                            data_sess.close()
+                            return
diff --git a/examples/3.x_api/tensorflow/cv/inception_v3/quantization/ptq/main.py b/examples/3.x_api/tensorflow/cv/inception_v3/quantization/ptq/main.py
new file mode 100644
index 00000000000..9b0f737b619
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/inception_v3/quantization/ptq/main.py
@@ -0,0 +1,144 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2023 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import time
+
+import tensorflow as tf
+import numpy as np
+
+from argparse import ArgumentParser
+from data_process import (
+    ImageRecordDataset, 
+    ComposeTransform, 
+    BilinearImagenetTransform, 
+    TFDataLoader,
+    TopKMetric,
+)
+
+tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
+
+arg_parser = ArgumentParser(description='Parse args')
+arg_parser.add_argument('-g', "--input-graph",
+                        help='Specify the input graph for the transform tool',
+                        dest='input_graph')
+arg_parser.add_argument("--output-graph",
+                        help='Specify tune result model save dir',
+                        dest='output_graph')
+arg_parser.add_argument('--benchmark', dest='benchmark', action='store_true', help='run benchmark')
+arg_parser.add_argument('--mode', dest='mode', default='performance', help='benchmark mode')
+arg_parser.add_argument('--tune', dest='tune', action='store_true', help='use neural_compressor to tune.')
+arg_parser.add_argument('--dataset_location', dest='dataset_location',
+                          help='location of calibration dataset and evaluate dataset')
+arg_parser.add_argument('--batch_size', type=int, default=32, dest='batch_size', help='batch_size of benchmark')
+arg_parser.add_argument('--iters', type=int, default=100, dest='iters', help='interations')
+args = arg_parser.parse_args()
+
+def evaluate(model, eval_dataloader, metric, postprocess=None):
+    """Custom evaluate function to estimate the accuracy of the model.
+
+    Args:
+        model (tf.Graph_def): The input model graph
+        
+    Returns:
+        accuracy (float): evaluation result, the larger is better.
+    """
+    from neural_compressor.tensorflow import Model
+    model = Model(model)
+    input_tensor = model.input_tensor
+    output_tensor = model.output_tensor if len(model.output_tensor)>1 else \
+                        model.output_tensor[0]
+    iteration = -1
+    if args.benchmark and args.mode == 'performance':
+        iteration = args.iters
+
+    def eval_func(dataloader):
+        latency_list = []
+        for idx, (inputs, labels) in enumerate(dataloader):
+            # dataloader should keep the order and len of inputs same with input_tensor
+            inputs = np.array([inputs])
+            feed_dict = dict(zip(input_tensor, inputs))
+
+            start = time.time()
+            predictions = model.sess.run(output_tensor, feed_dict)
+            end = time.time()
+
+            metric.update(predictions, labels)
+            latency_list.append(end-start)
+            if idx + 1 == iteration:
+                break
+        latency = np.array(latency_list).mean() / args.batch_size
+        return latency
+
+    latency = eval_func(eval_dataloader)
+    if args.benchmark and args.mode == 'performance':
+        print("Batch size = {}".format(args.batch_size))
+        print("Latency: {:.3f} ms".format(latency * 1000))
+        print("Throughput: {:.3f} images/sec".format(1. / latency))
+    acc = metric.result()
+    return acc
+
+class eval_classifier_optimized_graph:
+    """Evaluate image classifier with optimized TensorFlow graph."""
+
+    def run(self):
+        """This is neural_compressor function include tuning, export and benchmark option."""
+        from neural_compressor import set_random_seed
+        set_random_seed(9527)
+
+        if args.tune:
+            from neural_compressor.tensorflow import StaticQuantConfig, quantize_model
+
+            dataset = ImageRecordDataset(
+                root=args.dataset_location, 
+                transform=ComposeTransform(transform_list= [
+                    BilinearImagenetTransform(height=299, width=299),
+                    ]
+                )
+            )
+            calib_dataloader = TFDataLoader(dataset=dataset)
+
+            # maybe we need to exclud bf16
+            quant_config = StaticQuantConfig()
+            conv_config = StaticQuantConfig(weight_dtype="fp32", act_dtype="fp32")
+            quant_config.set_local("v0/cg/conv0/conv2d/Conv2D", conv_config)
+            q_model = quantize_model(args.input_graph, quant_config, calib_dataloader)
+            q_model.save(args.output_graph)
+
+        if args.benchmark:
+            dataset = ImageRecordDataset(
+                root=args.dataset_location, 
+                transform=ComposeTransform(transform_list= [
+                    BilinearImagenetTransform(height=299, width=299),
+                    ]
+                )
+            )
+            dataloader = TFDataLoader(dataset=dataset, batch_size=args.batch_size)
+            def eval(model):
+                top1 = TopKMetric(k=1)
+                return evaluate(model, dataloader, top1)
+
+            if args.mode == 'performance':
+                eval(args.input_graph)
+            elif args.mode == 'accuracy':
+                acc_result = eval(args.input_graph)
+                print("Batch size = %d" % dataloader.batch_size)
+                print("Accuracy: %.5f" % acc_result)
+
+if __name__ == "__main__":
+    evaluate_opt_graph = eval_classifier_optimized_graph()
+    evaluate_opt_graph.run()
diff --git a/examples/3.x_api/tensorflow/cv/inception_v3/quantization/ptq/requirements.txt b/examples/3.x_api/tensorflow/cv/inception_v3/quantization/ptq/requirements.txt
new file mode 100644
index 00000000000..2755e1a41ac
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/inception_v3/quantization/ptq/requirements.txt
@@ -0,0 +1,2 @@
+tensorflow
+neural-compressor
diff --git a/examples/3.x_api/tensorflow/cv/inception_v3/quantization/ptq/run_benchmark.sh b/examples/3.x_api/tensorflow/cv/inception_v3/quantization/ptq/run_benchmark.sh
new file mode 100644
index 00000000000..8ecac837cf7
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/inception_v3/quantization/ptq/run_benchmark.sh
@@ -0,0 +1,51 @@
+#!/bin/bash
+set -x
+
+function main {
+
+  init_params "$@"
+  run_benchmark
+
+}
+
+# init params
+function init_params {
+  batch_size=32
+  iters=100
+
+  for var in "$@"
+  do
+    case $var in
+      --input_model=*)
+          input_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --mode=*)
+          mode=$(echo $var |cut -f2 -d=)
+      ;;
+      --dataset_location=*)
+          dataset_location=$(echo $var |cut -f2 -d=)
+      ;;
+      --batch_size=*)
+          batch_size=$(echo $var |cut -f2 -d=)
+      ;;
+      --iters=*)
+          iters=$(echo $var |cut -f2 -d=)
+      ;;
+    esac
+  done
+
+}
+
+# run_tuning
+function run_benchmark {
+
+    python main.py \
+            --input-graph ${input_model} \
+            --mode ${mode} \
+            --dataset_location ${dataset_location} \
+            --batch_size ${batch_size} \
+            --benchmark \
+            --iters ${iters}
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/cv/inception_v3/quantization/ptq/run_quant.sh b/examples/3.x_api/tensorflow/cv/inception_v3/quantization/ptq/run_quant.sh
new file mode 100644
index 00000000000..6a9e1b859c9
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/inception_v3/quantization/ptq/run_quant.sh
@@ -0,0 +1,39 @@
+#!/bin/bash
+set -x
+
+function main {
+  init_params "$@"
+  run_tuning
+
+}
+
+# init params
+function init_params {
+
+  for var in "$@"
+  do
+    case $var in
+      --input_model=*)
+          input_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --output_model=*)
+          output_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --dataset_location=*)
+          dataset_location=$(echo $var |cut -f2 -d=)
+      ;;
+    esac
+  done
+
+}
+
+# run_tuning
+function run_tuning {
+    python main.py \
+            --input-graph ${input_model} \
+            --output-graph ${output_model} \
+            --dataset_location ${dataset_location} \
+            --tune
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/README.md b/examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/README.md
new file mode 100644
index 00000000000..25755074a06
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/README.md
@@ -0,0 +1,108 @@
+Step-by-Step
+============
+
+This document list steps of reproducing mobilenet_v2 model tuning and benchmark results via Neural Compressor.
+This example can run on Intel CPUs and GPUs.
+
+> **Note**: 
+> The model is supported in validated TensorFlow [Version](/docs/source/installation_guide.md#validated-software-environment).
+# Prerequisite
+
+## 1. Environment
+
+### Installation
+Recommend python 3.9 or higher version.
+```shell
+pip install -r requirements.txt
+```
+
+### Install Intel Extension for Tensorflow
+#### Quantizing the model on Intel GPU(Mandatory to install ITEX)
+Intel Extension for Tensorflow is mandatory to be installed for quantizing the model on Intel GPUs.
+
+```shell
+pip install --upgrade intel-extension-for-tensorflow[xpu]
+```
+For any more details, please follow the procedure in [install-gpu-drivers](https://github.com/intel/intel-extension-for-tensorflow/blob/main/docs/install/install_for_xpu.md#install-gpu-drivers)
+
+#### Quantizing the model on Intel CPU(Optional to install ITEX)
+Intel Extension for Tensorflow for Intel CPUs is experimental currently. It's not mandatory for quantizing the model on Intel CPUs.
+
+```shell
+pip install --upgrade intel-extension-for-tensorflow[cpu]
+```
+> **Note**: 
+> The version compatibility of stock Tensorflow and ITEX can be checked [here](https://github.com/intel/intel-extension-for-tensorflow#compatibility-table). Please make sure you have installed compatible Tensorflow and ITEX.
+
+## 2. Prepare pre-trained model
+
+The mobilenet_v2 checkpoint file comes from [models](https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models).
+We can get the pb file by convert the checkpoint file.
+
+  1. Download the checkpoint file from [here](https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models)
+  ```shell
+  wget https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.4_224.tgz
+  tar -xvf mobilenet_v2_1.4_224.tgz
+  ```
+
+  2. Exporting the Inference Graph
+  ```shell
+  git clone https://github.com/tensorflow/models
+  cd models/research/slim
+  python export_inference_graph.py \
+          --alsologtostderr \
+          --model_name=mobilenet_v2 \
+          --output_file=/tmp/mobilenet_v2_inf_graph.pb
+  ```
+  Make sure to use intel-tensorflow v1.15, and pip install tf_slim.
+  #### Install Intel Tensorflow 1.15 up2
+  Check your python version and use pip install 1.15.0 up2 from links below:
+  https://storage.googleapis.com/intel-optimized-tensorflow/intel_tensorflow-1.15.0up2-cp36-cp36m-manylinux2010_x86_64.whl                
+  https://storage.googleapis.com/intel-optimized-tensorflow/intel_tensorflow-1.15.0up2-cp37-cp37m-manylinux2010_x86_64.whl
+  https://storage.googleapis.com/intel-optimized-tensorflow/intel_tensorflow-1.15.0up2-cp35-cp35m-manylinux2010_x86_64.whl
+  > Please note: The ImageNet dataset has 1001, the **VGG** and **ResNet V1** final layers have only 1000 outputs rather than 1001. So we need add the `--labels_offset=1` flag in the inference graph exporting command.
+
+  3. Use [Netron](https://lutzroeder.github.io/netron/) to get the input/output layer name of inference graph pb, for vgg_16 the output layer name is `MobilenetV2/Predictions/Reshape_1`
+
+  4. Freezing the exported Graph, please use the tool `freeze_graph.py` in [tensorflow v1.15.2](https://github.com/tensorflow/tensorflow/blob/v1.15.2/tensorflow/python/tools/freeze_graph.py) repo 
+  ```shell
+  python freeze_graph.py \
+          --input_graph=/tmp/mobilenet_v2_inf_graph.pb \
+          --input_checkpoint=./mobilenet_v2.ckpt \
+          --input_binary=true \
+          --output_graph=./frozen_mobilenet_v2.pb \
+          --output_node_names=MobilenetV2/Predictions/Reshape_1
+  ```
+
+## 3. Prepare Dataset
+
+  TensorFlow [models](https://github.com/tensorflow/models) repo provides [scripts and instructions](https://github.com/tensorflow/models/tree/master/research/slim#an-automated-script-for-processing-imagenet-data) to download, process and convert the ImageNet dataset to the TF records format.
+  We also prepared related scripts in ` examples/3.x_api/tensorflow/cv` directory. To download the raw images, the user must create an account with image-net.org. If you have downloaded the raw data and preprocessed the validation data by moving the images into the appropriate sub-directory based on the label (synset) of the image. we can use below command ro convert it to tf records format.
+
+  ```shell
+  cd examples/3.x_api/tensorflow/cv
+  # convert validation subset
+  bash prepare_dataset.sh --output_dir=./mobilenet_v2/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
+  # convert train subset
+  bash prepare_dataset.sh --output_dir=./mobilenet_v2/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/train/ --subset=train
+  ```
+> **Note**: 
+> The raw ImageNet dataset resides in JPEG files should be in the following directory structure. Taking validation set as an example:<br>
+> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/PATH/TO/img_raw/val/n01440764/ILSVRC2012_val_00000293.JPEG<br>
+> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/PATH/TO/img_raw/val/n01440764/ILSVRC2012_val_00000543.JPEG<br>
+> where 'n01440764' is the unique synset label associated with these images.
+
+# Run
+
+## 1 Quantization
+
+  ```shell
+  bash run_quant.sh --input_model=/PATH/TO/frozen_mobilenet_v2.pb \
+      --output_model=./nc_mobilenet_v2.pb --dataset_location=/path/to/ImageNet/
+  ```
+
+## 2. Benchmark
+  ```shell
+  bash run_benchmark.sh --input_model=./nc_mobilenet_v2.pb --mode=accuracy --dataset_location=/path/to/ImageNet/ --batch_size=32
+  bash run_benchmark.sh --input_model=./nc_mobilenet_v2.pb --mode=performance --dataset_location=/path/to/ImageNet/ --batch_size=1
+  ```
diff --git a/examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/data_process.py b/examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/data_process.py
new file mode 100644
index 00000000000..ecfca2348cd
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/data_process.py
@@ -0,0 +1,511 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2024 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import os
+import collections
+
+import numpy as np
+import tensorflow as tf
+
+from abc import abstractmethod
+from neural_compressor.common import logger
+from neural_compressor.tensorflow.utils.data import default_collate
+
+class ParseDecodeImagenet:
+    """Parse features in Example proto.
+
+    Returns:
+        tuple of parsed image and label
+    """
+
+    def __call__(self, sample):
+        """Parse features in example."""
+        # Dense features in Example proto.
+        feature_map = {
+            "image/encoded": tf.io.FixedLenFeature([], dtype=tf.string, default_value=""),
+            "image/class/label": tf.io.FixedLenFeature([1], dtype=tf.int64, default_value=-1),
+        }
+
+        sparse_float32 = tf.io.VarLenFeature(dtype=tf.float32)
+        # Sparse features in Example proto.
+        feature_map.update(
+            {
+                k: sparse_float32
+                for k in [
+                    "image/object/bbox/xmin",
+                    "image/object/bbox/ymin",
+                    "image/object/bbox/xmax",
+                    "image/object/bbox/ymax",
+                ]
+            }
+        )
+
+        features = tf.io.parse_single_example(serialized=sample, features=feature_map)
+        label = tf.cast(features["image/class/label"], dtype=tf.int32)
+        image = features["image/encoded"]
+        image = tf.image.decode_jpeg(image, channels=3, fancy_upscaling=False, dct_method="INTEGER_FAST")
+        return (image, label)
+
+
+class BilinearImagenetTransform(object):
+    """Combination of a series of transforms which is applicable to images in Imagenet.
+
+    Args:
+        height: Height of the result
+        width:Width of the result
+        central_fraction(float, default=0.875):fraction of size to crop
+        mean_value(list, default=[0.0,0.0,0.0]):means for each channel
+        scale(float, default=1.0):std value
+
+    Returns:
+        tuple of processed image and label
+    """
+
+    def __init__(self, height, width, central_fraction=0.875, mean_value=[0.0, 0.0, 0.0], scale=1.0):
+        """Initialize `BilinearImagenetTransform` class."""
+        self.height = height
+        self.width = width
+        self.mean_value = mean_value
+        self.scale = scale
+        self.central_fraction = central_fraction
+
+    # sample is (images, labels)
+    def __call__(self, sample):
+        """Convert `BilinearImagenetTransform` feature."""
+        image, label = sample
+        if image.dtype is not tf.float32:
+            image = tf.image.convert_image_dtype(image, dtype=tf.float32)
+        # Crop the central region of the image containing 87.5% area of the original image.
+        if self.central_fraction:
+            image = tf.image.central_crop(image, central_fraction=self.central_fraction)
+
+        if self.height and self.width:
+            # Resize the image to the specified height and width.
+            image = tf.expand_dims(image, 0)
+            image = tf.image.resize(image, [self.height, self.width], method=tf.image.ResizeMethod.BILINEAR)
+            image = tf.squeeze(image, [0])
+
+        image = tf.subtract(image, 0.5)
+        image = tf.multiply(image, 2.0)
+        means = tf.broadcast_to(self.mean_value, tf.shape(input=image))
+        image = (image - means) * self.scale
+        return (image, label)
+
+
+class ComposeTransform(object):
+    """Composes several transforms together.
+
+    Args:
+        transform_list (list of Transform objects):  list of transforms to compose
+
+    Returns:
+        sample (tuple): tuple of processed image and label
+    """
+
+    def __init__(self, transform_list):
+        """Initialize `ComposeTransform` class."""
+        self.transform_list = transform_list
+
+    def __call__(self, sample):
+        """Call transforms in transform_list."""
+        for transform in self.transform_list:
+            sample = transform(sample)
+        return sample
+
+
+class ShiftRescale(object):
+    """Label shift by 1 and rescale.
+
+    Returns:
+        tuple of processed image and label
+    """
+
+    def __call__(self, sample):
+        image, label = sample
+        label -= 1
+        image = (image - 127.5) / 127.5
+        return (image, label)
+
+
+class ImageRecordDataset(object):
+    """Tensorflow imageNet database in tf record format.
+
+    Please arrange data in this way:
+        root/validation-000-of-100
+        root/validation-001-of-100
+        ...
+        root/validation-099-of-100
+    The file name needs to follow this pattern: '* - * -of- *'
+
+    Args: root (str): Root directory of dataset.
+          transform (transform object, default=None):  transform to process input data.
+          filter (Filter objects, default=None): filter out examples according
+                                                 to specific conditions.
+    """
+
+    """Configuration for Imagenet dataset."""
+
+    def __new__(cls, root, transform=None, filter=None):
+        """Build a new object of TensorflowImageRecord class."""
+        from tensorflow.python.platform import gfile  # pylint: disable=no-name-in-module
+
+        glob_pattern = os.path.join(root, "*-*-of-*")
+        file_names = gfile.Glob(glob_pattern)
+        if not file_names:
+            raise ValueError("Found no files in --root matching: {}".format(glob_pattern))
+
+        # pylint: disable=no-name-in-module
+        from tensorflow.python.data.experimental import parallel_interleave
+
+        ds = tf.data.TFRecordDataset.list_files(file_names, shuffle=False)
+        ds = ds.apply(parallel_interleave(tf.data.TFRecordDataset, cycle_length=len(file_names)))
+
+        if transform is not None:
+            transform.transform_list.insert(0, ParseDecodeImagenet())
+        else:
+            transform = ParseDecodeImagenet()
+        ds = ds.map(transform, num_parallel_calls=None)
+        ds = ds.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)  # this number can be tuned
+        return ds
+
+
+class BaseMetric(object):
+    """The base class of Metric."""
+
+    def __init__(self, metric, single_output=False, hvd=None):
+        """Initialize the basic metric.
+
+        Args:
+            metric: The metric class.
+            single_output: Whether the output is single or not, defaults to False.
+            hvd: The Horovod class for distributed training, defaults to None.
+        """
+        self._metric_cls = metric
+        self._single_output = single_output
+        self._hvd = hvd
+
+    def __call__(self, *args, **kwargs):
+        """Evaluate the model predictions, and the reference.
+
+        Returns:
+            The class itself.
+        """
+        self._metric = self._metric_cls(*args, **kwargs)
+        return self
+
+    @abstractmethod
+    def update(self, preds, labels=None, sample_weight=None):
+        """Update the state that need to be evaluated.
+
+        Args:
+            preds: The prediction result.
+            labels: The reference. Defaults to None.
+            sample_weight: The sampling weight. Defaults to None.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def reset(self):
+        """Clear the predictions and labels.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def result(self):
+        """Evaluate the difference between predictions and labels.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @property
+    def metric(self):
+        """Return its metric class.
+
+        Returns:
+            The metric class.
+        """
+        return self._metric_cls
+
+    @property
+    def hvd(self):
+        """Return its hvd class.
+
+        Returns:
+            The hvd class.
+        """
+        return self._hvd
+
+    @hvd.setter
+    def hvd(self, hvd):
+        """Set its hvd.
+
+        Args:
+            hvd: The Horovod class for distributed training.
+        """
+        self._hvd = hvd
+
+
+class TopKMetric(BaseMetric):
+    """Compute Top-k Accuracy classification score for Tensorflow model.
+
+    This metric computes the number of times where the correct label is among
+    the top k labels predicted.
+
+    Attributes:
+        k (int): The number of most likely outcomes considered to find the correct label.
+        num_correct: The number of predictions that were correct classified.
+        num_sample: The total number of predictions.
+    """
+
+    def __init__(self, k=1):
+        """Initialize the k, number of samples and correct predictions.
+
+        Args:
+            k: The number of most likely outcomes considered to find the correct label.
+        """
+        self.k = k
+        self.num_correct = 0
+        self.num_sample = 0
+
+    def update(self, preds, labels, sample_weight=None):
+        """Add the predictions and labels.
+
+        Args:
+            preds: The predictions.
+            labels: The labels corresponding to the predictions.
+            sample_weight: The sample weight.
+        """
+        preds, labels = TopKMetric._topk_shape_validate(preds, labels)
+
+        labels = labels.reshape([len(labels)])
+        with tf.Graph().as_default() as acc_graph:
+            topk = tf.nn.in_top_k(
+                predictions=tf.constant(preds, dtype=tf.float32), targets=tf.constant(labels, dtype=tf.int32), k=self.k
+            )
+            fp32_topk = tf.cast(topk, tf.float32)
+            correct_tensor = tf.reduce_sum(input_tensor=fp32_topk)
+
+            with tf.compat.v1.Session() as acc_sess:
+                correct = acc_sess.run(correct_tensor)
+
+        self.num_sample += len(labels)
+        self.num_correct += correct
+
+    def reset(self):
+        """Reset the number of samples and correct predictions."""
+        self.num_correct = 0
+        self.num_sample = 0
+
+    def result(self):
+        """Compute the top-k score.
+
+        Returns:
+            The top-k score.
+        """
+        if self.num_sample == 0:
+            logger.warning("Sample num during evaluation is 0.")
+            return 0
+        elif getattr(self, "_hvd", None) is not None:  # pragma: no cover
+            allgather_num_correct = sum(self._hvd.allgather_object(self.num_correct))
+            allgather_num_sample = sum(self._hvd.allgather_object(self.num_sample))
+            return allgather_num_correct / allgather_num_sample
+        return self.num_correct / self.num_sample
+
+    @staticmethod
+    def _topk_shape_validate(preds, labels):
+        # preds shape can be Nxclass_num or class_num(N=1 by default)
+        # it's more suitable for 'Accuracy' with preds shape Nx1(or 1) output from argmax
+        if isinstance(preds, int):
+            preds = [preds]
+            preds = np.array(preds)
+        elif isinstance(preds, np.ndarray):
+            preds = np.array(preds)
+        elif isinstance(preds, list):
+            preds = np.array(preds)
+            preds = preds.reshape((-1, preds.shape[-1]))
+
+        # consider labels just int value 1x1
+        if isinstance(labels, int):
+            labels = [labels]
+            labels = np.array(labels)
+        elif isinstance(labels, tuple):
+            labels = np.array([labels])
+            labels = labels.reshape((labels.shape[-1], -1))
+        elif isinstance(labels, list):
+            if isinstance(labels[0], int):
+                labels = np.array(labels)
+                labels = labels.reshape((labels.shape[0], 1))
+            elif isinstance(labels[0], tuple):
+                labels = np.array(labels)
+                labels = labels.reshape((labels.shape[-1], -1))
+            else:
+                labels = np.array(labels)
+        # labels most have 2 axis, 2 cases: N(or Nx1 sparse) or Nxclass_num(one-hot)
+        # only support 2 dimension one-shot labels
+        # or 1 dimension one-hot class_num will confuse with N
+
+        if len(preds.shape) == 1:
+            N = 1
+            class_num = preds.shape[0]
+            preds = preds.reshape([-1, class_num])
+        elif len(preds.shape) >= 2:
+            N = preds.shape[0]
+            preds = preds.reshape([N, -1])
+            class_num = preds.shape[1]
+
+        label_N = labels.shape[0]
+        assert label_N == N, "labels batch size should same with preds"
+        labels = labels.reshape([N, -1])
+        # one-hot labels will have 2 dimension not equal 1
+        if labels.shape[1] != 1:
+            labels = labels.argsort()[..., -1:]
+        return preds, labels
+    
+
+class TFDataLoader(object):  # pragma: no cover
+    """Tensorflow dataloader class.
+
+    In tensorflow1.x dataloader is coupled with the graph, but it also support feed_dict
+    method to do session run, this dataloader is designed to satisfy the usage of feed dict
+    in tf1.x. Although it's a general dataloader and can be used in MXNet and PyTorch.
+
+    Args:
+        dataset: obj. wrapper of needed data.
+        batch_size: int. batch size
+    """
+
+    def __init__(self, dataset, batch_size=1, last_batch="rollover"):
+        """Initialize `TFDataDataLoader` class."""
+        self.dataset = dataset
+        self.last_batch = last_batch
+        self.batch_size = batch_size
+        dataset = dataset.batch(batch_size)
+
+    def batch(self, batch_size, last_batch="rollover"):
+        """Dataset return data per batch."""
+        drop_last = False if last_batch == "rollover" else True
+        self.batch_size = batch_size
+        self.dataset = self.dataset.batch(batch_size, drop_last)
+
+    def __iter__(self):
+        """Iterate dataloader."""
+        return self._generate_dataloader(
+            self.dataset,
+            batch_size=self.batch_size,
+            last_batch=self.last_batch,
+        )
+
+    def _generate_dataloader(
+        self,
+        dataset,
+        batch_size=1,
+        last_batch="rollover",
+        collate_fn=None,
+        sampler=None,
+        batch_sampler=None,
+        num_workers=None,
+        pin_memory=None,
+        distributed=False,
+    ):
+        """Yield data."""
+        drop_last = False if last_batch == "rollover" else True
+
+        def check_dynamic_shape(element_spec):
+            if isinstance(element_spec, collections.abc.Sequence):
+                return any([check_dynamic_shape(ele) for ele in element_spec])
+            elif isinstance(element_spec, tf.TensorSpec):
+                return True if element_spec.shape.num_elements() is None else False
+            else:
+                raise ValueError("unrecognized element spec...")
+
+        def squeeze_output(output):
+            if isinstance(output, collections.abc.Sequence):
+                return [squeeze_output(ele) for ele in output]
+            elif isinstance(output, np.ndarray):
+                return np.squeeze(output, axis=0)
+            else:
+                raise ValueError("not supported output format....")
+
+        if tf.executing_eagerly():
+            index = 0
+            outputs = []
+            for iter_tensors in dataset:
+                samples = []
+                iter_inputs, iter_labels = iter_tensors[0], iter_tensors[1]
+                if isinstance(iter_inputs, tf.Tensor):
+                    samples.append(iter_inputs.numpy())
+                else:
+                    samples.append(tuple(iter_input.numpy() for iter_input in iter_inputs))
+                if isinstance(iter_labels, tf.Tensor):
+                    samples.append(iter_labels.numpy())
+                else:
+                    samples.append([np.array(l) for l in iter_labels])
+                index += 1
+                outputs.append(samples)
+                if index == batch_size:
+                    outputs = default_collate(outputs)
+                    yield outputs
+                    outputs = []
+                    index = 0
+            if len(outputs) > 0:
+                outputs = default_collate(outputs)
+                yield outputs
+        else:
+            try_single_batch = check_dynamic_shape(dataset.element_spec)
+            dataset = dataset.batch(1 if try_single_batch else batch_size, drop_last)
+            ds_iterator = tf.compat.v1.data.make_one_shot_iterator(dataset)
+            iter_tensors = ds_iterator.get_next()
+            data_config = tf.compat.v1.ConfigProto()
+            data_config.use_per_session_threads = 1
+            data_config.intra_op_parallelism_threads = 1
+            data_config.inter_op_parallelism_threads = 16
+            data_sess = tf.compat.v1.Session(config=data_config)
+            # pylint: disable=no-name-in-module
+            from tensorflow.python.framework.errors_impl import OutOfRangeError
+
+            while True:
+                if not try_single_batch:
+                    try:
+                        outputs = data_sess.run(iter_tensors)
+                        yield outputs
+                    except OutOfRangeError:
+                        data_sess.close()
+                        return
+                else:
+                    try:
+                        outputs = []
+                        for i in range(0, batch_size):
+                            outputs.append(squeeze_output(data_sess.run(iter_tensors)))
+                        outputs = default_collate(outputs)
+                        yield outputs
+                    except OutOfRangeError:
+                        if len(outputs) == 0:
+                            data_sess.close()
+                            return
+                        else:
+                            outputs = default_collate(outputs)
+                            yield outputs
+                            data_sess.close()
+                            return
diff --git a/examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/main.py b/examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/main.py
new file mode 100644
index 00000000000..024225431f4
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/main.py
@@ -0,0 +1,143 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2023 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import time
+
+import tensorflow as tf
+import numpy as np
+
+from argparse import ArgumentParser
+from data_process import (
+    ImageRecordDataset, 
+    ComposeTransform, 
+    BilinearImagenetTransform, 
+    TFDataLoader,
+    TopKMetric,
+)
+
+tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
+
+arg_parser = ArgumentParser(description='Parse args')
+arg_parser.add_argument('-g', "--input-graph",
+                        help='Specify the input graph for the transform tool',
+                        dest='input_graph')
+arg_parser.add_argument("--output-graph",
+                        help='Specify tune result model save dir',
+                        dest='output_graph')
+arg_parser.add_argument('--benchmark', dest='benchmark', action='store_true', help='run benchmark')
+arg_parser.add_argument('--mode', dest='mode', default='performance', help='benchmark mode')
+arg_parser.add_argument('--tune', dest='tune', action='store_true', help='use neural_compressor to tune.')
+arg_parser.add_argument('--dataset_location', dest='dataset_location',
+                          help='location of calibration dataset and evaluate dataset')
+arg_parser.add_argument('--batch_size', type=int, default=32, dest='batch_size', help='batch_size of benchmark')
+arg_parser.add_argument('--iters', type=int, default=100, dest='iters', help='interations')
+args = arg_parser.parse_args()
+
+def evaluate(model, eval_dataloader, metric, postprocess=None):
+    """Custom evaluate function to estimate the accuracy of the model.
+
+    Args:
+        model (tf.Graph_def): The input model graph
+        
+    Returns:
+        accuracy (float): evaluation result, the larger is better.
+    """
+    from neural_compressor.tensorflow import Model
+    model = Model(model)
+    input_tensor = model.input_tensor
+    output_tensor = model.output_tensor if len(model.output_tensor)>1 else \
+                        model.output_tensor[0]
+    iteration = -1
+    if args.benchmark and args.mode == 'performance':
+        iteration = args.iters
+
+    def eval_func(dataloader):
+        latency_list = []
+        for idx, (inputs, labels) in enumerate(dataloader):
+            # dataloader should keep the order and len of inputs same with input_tensor
+            inputs = np.array([inputs])
+            feed_dict = dict(zip(input_tensor, inputs))
+
+            start = time.time()
+            predictions = model.sess.run(output_tensor, feed_dict)
+            end = time.time()
+
+            metric.update(predictions, labels)
+            latency_list.append(end-start)
+            if idx + 1 == iteration:
+                break
+        latency = np.array(latency_list).mean() / args.batch_size
+        return latency
+
+    latency = eval_func(eval_dataloader)
+    if args.benchmark and args.mode == 'performance':
+        print("Batch size = {}".format(args.batch_size))
+        print("Latency: {:.3f} ms".format(latency * 1000))
+        print("Throughput: {:.3f} images/sec".format(1. / latency))
+    acc = metric.result()
+    return acc
+
+class eval_classifier_optimized_graph:
+    """Evaluate image classifier with optimized TensorFlow graph."""
+
+    def run(self):
+        """This is neural_compressor function include tuning, export and benchmark option."""
+        from neural_compressor.common import set_random_seed
+        set_random_seed(9527)
+
+        if args.tune:
+            from neural_compressor.tensorflow import StaticQuantConfig, quantize_model
+
+            dataset = ImageRecordDataset(
+                root=args.dataset_location, 
+                transform=ComposeTransform(transform_list= [
+                    BilinearImagenetTransform(height=224, width=224),
+                    ]
+                )
+            )
+            calib_dataloader = TFDataLoader(dataset=dataset, batch_size=10)
+
+            quant_config = StaticQuantConfig()
+            q_model = quantize_model(args.input_graph, quant_config, calib_dataloader)
+            q_model.save(args.output_graph)
+
+        if args.benchmark:
+            dataloader = create_dataloader('tensorflow', dataloader_args)
+            dataset = ImageRecordDataset(
+                root=args.dataset_location, 
+                transform=ComposeTransform(transform_list= [
+                    BilinearImagenetTransform(height=224, width=224),
+                    ]
+                )
+            )
+            dataloader = TFDataLoader(dataset=dataset, batch_size=args.batch_size)
+
+            def eval(model):
+                top1 = TopKMetric(k=1)
+                return evaluate(model, dataloader, top1)
+
+            if args.mode == 'performance':
+                eval(args.input_graph)
+            elif args.mode == 'accuracy':
+                acc_result = eval(args.input_graph)
+                print("Batch size = %d" % dataloader.batch_size)
+                print("Accuracy: %.5f" % acc_result)
+
+if __name__ == "__main__":
+    evaluate_opt_graph = eval_classifier_optimized_graph()
+    evaluate_opt_graph.run()
diff --git a/examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/requirements.txt b/examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/requirements.txt
new file mode 100644
index 00000000000..2755e1a41ac
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/requirements.txt
@@ -0,0 +1,2 @@
+tensorflow
+neural-compressor
diff --git a/examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/run_benchmark.sh b/examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/run_benchmark.sh
new file mode 100644
index 00000000000..8ecac837cf7
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/run_benchmark.sh
@@ -0,0 +1,51 @@
+#!/bin/bash
+set -x
+
+function main {
+
+  init_params "$@"
+  run_benchmark
+
+}
+
+# init params
+function init_params {
+  batch_size=32
+  iters=100
+
+  for var in "$@"
+  do
+    case $var in
+      --input_model=*)
+          input_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --mode=*)
+          mode=$(echo $var |cut -f2 -d=)
+      ;;
+      --dataset_location=*)
+          dataset_location=$(echo $var |cut -f2 -d=)
+      ;;
+      --batch_size=*)
+          batch_size=$(echo $var |cut -f2 -d=)
+      ;;
+      --iters=*)
+          iters=$(echo $var |cut -f2 -d=)
+      ;;
+    esac
+  done
+
+}
+
+# run_tuning
+function run_benchmark {
+
+    python main.py \
+            --input-graph ${input_model} \
+            --mode ${mode} \
+            --dataset_location ${dataset_location} \
+            --batch_size ${batch_size} \
+            --benchmark \
+            --iters ${iters}
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/run_quant.sh b/examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/run_quant.sh
new file mode 100644
index 00000000000..6a9e1b859c9
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/run_quant.sh
@@ -0,0 +1,39 @@
+#!/bin/bash
+set -x
+
+function main {
+  init_params "$@"
+  run_tuning
+
+}
+
+# init params
+function init_params {
+
+  for var in "$@"
+  do
+    case $var in
+      --input_model=*)
+          input_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --output_model=*)
+          output_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --dataset_location=*)
+          dataset_location=$(echo $var |cut -f2 -d=)
+      ;;
+    esac
+  done
+
+}
+
+# run_tuning
+function run_tuning {
+    python main.py \
+            --input-graph ${input_model} \
+            --output-graph ${output_model} \
+            --dataset_location ${dataset_location} \
+            --tune
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/image_recognition/prepare_dataset.sh b/examples/3.x_api/tensorflow/cv/prepare_dataset.sh
similarity index 100%
rename from examples/3.x_api/tensorflow/image_recognition/prepare_dataset.sh
rename to examples/3.x_api/tensorflow/cv/prepare_dataset.sh
diff --git a/examples/3.x_api/tensorflow/cv/resnet_v2_50/quantization/ptq/README.md b/examples/3.x_api/tensorflow/cv/resnet_v2_50/quantization/ptq/README.md
new file mode 100644
index 00000000000..bc07e651f96
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/resnet_v2_50/quantization/ptq/README.md
@@ -0,0 +1,107 @@
+Step-by-Step
+============
+
+This document list steps of reproducing resnet_v2_50 model tuning and benchmark results via Neural Compressor.
+This example can run on Intel CPUs and GPUs.
+
+> **Note**: 
+> The models is supported in validated TensorFlow [Version](/docs/source/installation_guide.md#validated-software-environment).
+# Prerequisite
+
+## 1. Environment
+
+### Installation
+Recommend python 3.9 or higher version.
+```shell
+pip install -r requirements.txt
+```
+
+### Install Intel Extension for Tensorflow
+#### Quantizing the model on Intel GPU(Mandatory to install ITEX)
+Intel Extension for Tensorflow is mandatory to be installed for quantizing the model on Intel GPUs.
+
+```shell
+pip install --upgrade intel-extension-for-tensorflow[xpu]
+```
+For any more details, please follow the procedure in [install-gpu-drivers](https://github.com/intel/intel-extension-for-tensorflow/blob/main/docs/install/install_for_xpu.md#install-gpu-drivers)
+
+#### Quantizing the model on Intel CPU(Optional to install ITEX)
+Intel Extension for Tensorflow for Intel CPUs is experimental currently. It's not mandatory for quantizing the model on Intel CPUs.
+
+```shell
+pip install --upgrade intel-extension-for-tensorflow[cpu]
+```
+> **Note**: 
+> The version compatibility of stock Tensorflow and ITEX can be checked [here](https://github.com/intel/intel-extension-for-tensorflow#compatibility-table). Please make sure you have installed compatible Tensorflow and ITEX.
+
+## 2. Prepare pre-trained model
+The resnet_v2_50 checkpoint file comes from [models](https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models).
+We can get the pb file by convert the checkpoint file.
+
+  1. Download the checkpoint file from [here](https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models)
+  ```shell
+  wget http://download.tensorflow.org/models/resnet_v2_50_2017_04_14.tar.gz
+  tar -xvf resnet_v2_50_2017_04_14.tar.gz
+  ```
+
+  2. Exporting the Inference Graph
+  ```shell
+  git clone https://github.com/tensorflow/models
+  cd models/research/slim
+  python export_inference_graph.py \
+          --alsologtostderr \
+          --model_name=resnet_v2_50 \
+          --output_file=/tmp/resnet_v2_50_inf_graph.pb
+  ```
+  Make sure to use intel-tensorflow v1.15, and pip install tf_slim.
+  #### Install Intel Tensorflow 1.15 up2
+  Check your python version and use pip install 1.15.0 up2 from links below:
+  https://storage.googleapis.com/intel-optimized-tensorflow/intel_tensorflow-1.15.0up2-cp36-cp36m-manylinux2010_x86_64.whl                
+  https://storage.googleapis.com/intel-optimized-tensorflow/intel_tensorflow-1.15.0up2-cp37-cp37m-manylinux2010_x86_64.whl
+  https://storage.googleapis.com/intel-optimized-tensorflow/intel_tensorflow-1.15.0up2-cp35-cp35m-manylinux2010_x86_64.whl
+  > Please note: The ImageNet dataset has 1001, the **VGG** and **ResNet V1** final layers have only 1000 outputs rather than 1001. So we need add the `--labels_offset=1` flag in the inference graph exporting command.
+
+  3. Use [Netron](https://lutzroeder.github.io/netron/) to get the input/output layer name of inference graph pb, for vgg_16 the output layer name is `resnet_v2_50/predictions/Reshape_1`
+
+  4. Freezing the exported Graph, please use the tool `freeze_graph.py` in [tensorflow v1.15.2](https://github.com/tensorflow/tensorflow/blob/v1.15.2/tensorflow/python/tools/freeze_graph.py) repo 
+  ```shell
+  python freeze_graph.py \
+          --input_graph=/tmp/resnet_v2_50_inf_graph.pb \
+          --input_checkpoint=./resnet_v2_50.ckpt \
+          --input_binary=true \
+          --output_graph=./frozen_resnet_v2_50.pb \
+          --output_node_names=resnet_v2_50/predictions/Reshape_1
+  ```
+
+## 3. Prepare Dataset
+
+  TensorFlow [models](https://github.com/tensorflow/models) repo provides [scripts and instructions](https://github.com/tensorflow/models/tree/master/research/slim#an-automated-script-for-processing-imagenet-data) to download, process and convert the ImageNet dataset to the TF records format.
+  We also prepared related scripts in ` examples/3.x_api/tensorflow/cv` directory. To download the raw images, the user must create an account with image-net.org. If you have downloaded the raw data and preprocessed the validation data by moving the images into the appropriate sub-directory based on the label (synset) of the image. we can use below command ro convert it to tf records format.
+
+  ```shell
+  cd examples/3.x_api/tensorflow/cv
+  # convert validation subset
+  bash prepare_dataset.sh --output_dir=./resnet_v2_50/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
+  # convert train subset
+  bash prepare_dataset.sh --output_dir=./resnet_v2_50/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/train/ --subset=train
+  ```
+> **Note**: 
+> The raw ImageNet dataset resides in JPEG files should be in the following directory structure. Taking validation set as an example:<br>
+> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/PATH/TO/img_raw/val/n01440764/ILSVRC2012_val_00000293.JPEG<br>
+> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/PATH/TO/img_raw/val/n01440764/ILSVRC2012_val_00000543.JPEG<br>
+> where 'n01440764' is the unique synset label associated with these images.
+
+# Run
+
+## 1 Quantization
+
+  ```shell
+  bash run_quant.sh --input_model=/PATH/TO/frozen_resnet_v2_50.pb \
+      --output_model=./nc_resnet_v2_50.pb --dataset_location=/path/to/ImageNet/
+  ```
+
+## 2. Benchmark
+  ```shell
+  bash run_benchmark.sh --input_model=./nc_resnet_v2_50.pb --mode=accuracy --dataset_location=/path/to/ImageNet/ --batch_size=32
+  bash run_benchmark.sh --input_model=./nc_resnet_v2_50.pb --mode=performance --dataset_location=/path/to/ImageNet/ --batch_size=1
+  ```
diff --git a/examples/3.x_api/tensorflow/cv/resnet_v2_50/quantization/ptq/data_process.py b/examples/3.x_api/tensorflow/cv/resnet_v2_50/quantization/ptq/data_process.py
new file mode 100644
index 00000000000..ecfca2348cd
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/resnet_v2_50/quantization/ptq/data_process.py
@@ -0,0 +1,511 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2024 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import os
+import collections
+
+import numpy as np
+import tensorflow as tf
+
+from abc import abstractmethod
+from neural_compressor.common import logger
+from neural_compressor.tensorflow.utils.data import default_collate
+
+class ParseDecodeImagenet:
+    """Parse features in Example proto.
+
+    Returns:
+        tuple of parsed image and label
+    """
+
+    def __call__(self, sample):
+        """Parse features in example."""
+        # Dense features in Example proto.
+        feature_map = {
+            "image/encoded": tf.io.FixedLenFeature([], dtype=tf.string, default_value=""),
+            "image/class/label": tf.io.FixedLenFeature([1], dtype=tf.int64, default_value=-1),
+        }
+
+        sparse_float32 = tf.io.VarLenFeature(dtype=tf.float32)
+        # Sparse features in Example proto.
+        feature_map.update(
+            {
+                k: sparse_float32
+                for k in [
+                    "image/object/bbox/xmin",
+                    "image/object/bbox/ymin",
+                    "image/object/bbox/xmax",
+                    "image/object/bbox/ymax",
+                ]
+            }
+        )
+
+        features = tf.io.parse_single_example(serialized=sample, features=feature_map)
+        label = tf.cast(features["image/class/label"], dtype=tf.int32)
+        image = features["image/encoded"]
+        image = tf.image.decode_jpeg(image, channels=3, fancy_upscaling=False, dct_method="INTEGER_FAST")
+        return (image, label)
+
+
+class BilinearImagenetTransform(object):
+    """Combination of a series of transforms which is applicable to images in Imagenet.
+
+    Args:
+        height: Height of the result
+        width:Width of the result
+        central_fraction(float, default=0.875):fraction of size to crop
+        mean_value(list, default=[0.0,0.0,0.0]):means for each channel
+        scale(float, default=1.0):std value
+
+    Returns:
+        tuple of processed image and label
+    """
+
+    def __init__(self, height, width, central_fraction=0.875, mean_value=[0.0, 0.0, 0.0], scale=1.0):
+        """Initialize `BilinearImagenetTransform` class."""
+        self.height = height
+        self.width = width
+        self.mean_value = mean_value
+        self.scale = scale
+        self.central_fraction = central_fraction
+
+    # sample is (images, labels)
+    def __call__(self, sample):
+        """Convert `BilinearImagenetTransform` feature."""
+        image, label = sample
+        if image.dtype is not tf.float32:
+            image = tf.image.convert_image_dtype(image, dtype=tf.float32)
+        # Crop the central region of the image containing 87.5% area of the original image.
+        if self.central_fraction:
+            image = tf.image.central_crop(image, central_fraction=self.central_fraction)
+
+        if self.height and self.width:
+            # Resize the image to the specified height and width.
+            image = tf.expand_dims(image, 0)
+            image = tf.image.resize(image, [self.height, self.width], method=tf.image.ResizeMethod.BILINEAR)
+            image = tf.squeeze(image, [0])
+
+        image = tf.subtract(image, 0.5)
+        image = tf.multiply(image, 2.0)
+        means = tf.broadcast_to(self.mean_value, tf.shape(input=image))
+        image = (image - means) * self.scale
+        return (image, label)
+
+
+class ComposeTransform(object):
+    """Composes several transforms together.
+
+    Args:
+        transform_list (list of Transform objects):  list of transforms to compose
+
+    Returns:
+        sample (tuple): tuple of processed image and label
+    """
+
+    def __init__(self, transform_list):
+        """Initialize `ComposeTransform` class."""
+        self.transform_list = transform_list
+
+    def __call__(self, sample):
+        """Call transforms in transform_list."""
+        for transform in self.transform_list:
+            sample = transform(sample)
+        return sample
+
+
+class ShiftRescale(object):
+    """Label shift by 1 and rescale.
+
+    Returns:
+        tuple of processed image and label
+    """
+
+    def __call__(self, sample):
+        image, label = sample
+        label -= 1
+        image = (image - 127.5) / 127.5
+        return (image, label)
+
+
+class ImageRecordDataset(object):
+    """Tensorflow imageNet database in tf record format.
+
+    Please arrange data in this way:
+        root/validation-000-of-100
+        root/validation-001-of-100
+        ...
+        root/validation-099-of-100
+    The file name needs to follow this pattern: '* - * -of- *'
+
+    Args: root (str): Root directory of dataset.
+          transform (transform object, default=None):  transform to process input data.
+          filter (Filter objects, default=None): filter out examples according
+                                                 to specific conditions.
+    """
+
+    """Configuration for Imagenet dataset."""
+
+    def __new__(cls, root, transform=None, filter=None):
+        """Build a new object of TensorflowImageRecord class."""
+        from tensorflow.python.platform import gfile  # pylint: disable=no-name-in-module
+
+        glob_pattern = os.path.join(root, "*-*-of-*")
+        file_names = gfile.Glob(glob_pattern)
+        if not file_names:
+            raise ValueError("Found no files in --root matching: {}".format(glob_pattern))
+
+        # pylint: disable=no-name-in-module
+        from tensorflow.python.data.experimental import parallel_interleave
+
+        ds = tf.data.TFRecordDataset.list_files(file_names, shuffle=False)
+        ds = ds.apply(parallel_interleave(tf.data.TFRecordDataset, cycle_length=len(file_names)))
+
+        if transform is not None:
+            transform.transform_list.insert(0, ParseDecodeImagenet())
+        else:
+            transform = ParseDecodeImagenet()
+        ds = ds.map(transform, num_parallel_calls=None)
+        ds = ds.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)  # this number can be tuned
+        return ds
+
+
+class BaseMetric(object):
+    """The base class of Metric."""
+
+    def __init__(self, metric, single_output=False, hvd=None):
+        """Initialize the basic metric.
+
+        Args:
+            metric: The metric class.
+            single_output: Whether the output is single or not, defaults to False.
+            hvd: The Horovod class for distributed training, defaults to None.
+        """
+        self._metric_cls = metric
+        self._single_output = single_output
+        self._hvd = hvd
+
+    def __call__(self, *args, **kwargs):
+        """Evaluate the model predictions, and the reference.
+
+        Returns:
+            The class itself.
+        """
+        self._metric = self._metric_cls(*args, **kwargs)
+        return self
+
+    @abstractmethod
+    def update(self, preds, labels=None, sample_weight=None):
+        """Update the state that need to be evaluated.
+
+        Args:
+            preds: The prediction result.
+            labels: The reference. Defaults to None.
+            sample_weight: The sampling weight. Defaults to None.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def reset(self):
+        """Clear the predictions and labels.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def result(self):
+        """Evaluate the difference between predictions and labels.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @property
+    def metric(self):
+        """Return its metric class.
+
+        Returns:
+            The metric class.
+        """
+        return self._metric_cls
+
+    @property
+    def hvd(self):
+        """Return its hvd class.
+
+        Returns:
+            The hvd class.
+        """
+        return self._hvd
+
+    @hvd.setter
+    def hvd(self, hvd):
+        """Set its hvd.
+
+        Args:
+            hvd: The Horovod class for distributed training.
+        """
+        self._hvd = hvd
+
+
+class TopKMetric(BaseMetric):
+    """Compute Top-k Accuracy classification score for Tensorflow model.
+
+    This metric computes the number of times where the correct label is among
+    the top k labels predicted.
+
+    Attributes:
+        k (int): The number of most likely outcomes considered to find the correct label.
+        num_correct: The number of predictions that were correct classified.
+        num_sample: The total number of predictions.
+    """
+
+    def __init__(self, k=1):
+        """Initialize the k, number of samples and correct predictions.
+
+        Args:
+            k: The number of most likely outcomes considered to find the correct label.
+        """
+        self.k = k
+        self.num_correct = 0
+        self.num_sample = 0
+
+    def update(self, preds, labels, sample_weight=None):
+        """Add the predictions and labels.
+
+        Args:
+            preds: The predictions.
+            labels: The labels corresponding to the predictions.
+            sample_weight: The sample weight.
+        """
+        preds, labels = TopKMetric._topk_shape_validate(preds, labels)
+
+        labels = labels.reshape([len(labels)])
+        with tf.Graph().as_default() as acc_graph:
+            topk = tf.nn.in_top_k(
+                predictions=tf.constant(preds, dtype=tf.float32), targets=tf.constant(labels, dtype=tf.int32), k=self.k
+            )
+            fp32_topk = tf.cast(topk, tf.float32)
+            correct_tensor = tf.reduce_sum(input_tensor=fp32_topk)
+
+            with tf.compat.v1.Session() as acc_sess:
+                correct = acc_sess.run(correct_tensor)
+
+        self.num_sample += len(labels)
+        self.num_correct += correct
+
+    def reset(self):
+        """Reset the number of samples and correct predictions."""
+        self.num_correct = 0
+        self.num_sample = 0
+
+    def result(self):
+        """Compute the top-k score.
+
+        Returns:
+            The top-k score.
+        """
+        if self.num_sample == 0:
+            logger.warning("Sample num during evaluation is 0.")
+            return 0
+        elif getattr(self, "_hvd", None) is not None:  # pragma: no cover
+            allgather_num_correct = sum(self._hvd.allgather_object(self.num_correct))
+            allgather_num_sample = sum(self._hvd.allgather_object(self.num_sample))
+            return allgather_num_correct / allgather_num_sample
+        return self.num_correct / self.num_sample
+
+    @staticmethod
+    def _topk_shape_validate(preds, labels):
+        # preds shape can be Nxclass_num or class_num(N=1 by default)
+        # it's more suitable for 'Accuracy' with preds shape Nx1(or 1) output from argmax
+        if isinstance(preds, int):
+            preds = [preds]
+            preds = np.array(preds)
+        elif isinstance(preds, np.ndarray):
+            preds = np.array(preds)
+        elif isinstance(preds, list):
+            preds = np.array(preds)
+            preds = preds.reshape((-1, preds.shape[-1]))
+
+        # consider labels just int value 1x1
+        if isinstance(labels, int):
+            labels = [labels]
+            labels = np.array(labels)
+        elif isinstance(labels, tuple):
+            labels = np.array([labels])
+            labels = labels.reshape((labels.shape[-1], -1))
+        elif isinstance(labels, list):
+            if isinstance(labels[0], int):
+                labels = np.array(labels)
+                labels = labels.reshape((labels.shape[0], 1))
+            elif isinstance(labels[0], tuple):
+                labels = np.array(labels)
+                labels = labels.reshape((labels.shape[-1], -1))
+            else:
+                labels = np.array(labels)
+        # labels most have 2 axis, 2 cases: N(or Nx1 sparse) or Nxclass_num(one-hot)
+        # only support 2 dimension one-shot labels
+        # or 1 dimension one-hot class_num will confuse with N
+
+        if len(preds.shape) == 1:
+            N = 1
+            class_num = preds.shape[0]
+            preds = preds.reshape([-1, class_num])
+        elif len(preds.shape) >= 2:
+            N = preds.shape[0]
+            preds = preds.reshape([N, -1])
+            class_num = preds.shape[1]
+
+        label_N = labels.shape[0]
+        assert label_N == N, "labels batch size should same with preds"
+        labels = labels.reshape([N, -1])
+        # one-hot labels will have 2 dimension not equal 1
+        if labels.shape[1] != 1:
+            labels = labels.argsort()[..., -1:]
+        return preds, labels
+    
+
+class TFDataLoader(object):  # pragma: no cover
+    """Tensorflow dataloader class.
+
+    In tensorflow1.x dataloader is coupled with the graph, but it also support feed_dict
+    method to do session run, this dataloader is designed to satisfy the usage of feed dict
+    in tf1.x. Although it's a general dataloader and can be used in MXNet and PyTorch.
+
+    Args:
+        dataset: obj. wrapper of needed data.
+        batch_size: int. batch size
+    """
+
+    def __init__(self, dataset, batch_size=1, last_batch="rollover"):
+        """Initialize `TFDataDataLoader` class."""
+        self.dataset = dataset
+        self.last_batch = last_batch
+        self.batch_size = batch_size
+        dataset = dataset.batch(batch_size)
+
+    def batch(self, batch_size, last_batch="rollover"):
+        """Dataset return data per batch."""
+        drop_last = False if last_batch == "rollover" else True
+        self.batch_size = batch_size
+        self.dataset = self.dataset.batch(batch_size, drop_last)
+
+    def __iter__(self):
+        """Iterate dataloader."""
+        return self._generate_dataloader(
+            self.dataset,
+            batch_size=self.batch_size,
+            last_batch=self.last_batch,
+        )
+
+    def _generate_dataloader(
+        self,
+        dataset,
+        batch_size=1,
+        last_batch="rollover",
+        collate_fn=None,
+        sampler=None,
+        batch_sampler=None,
+        num_workers=None,
+        pin_memory=None,
+        distributed=False,
+    ):
+        """Yield data."""
+        drop_last = False if last_batch == "rollover" else True
+
+        def check_dynamic_shape(element_spec):
+            if isinstance(element_spec, collections.abc.Sequence):
+                return any([check_dynamic_shape(ele) for ele in element_spec])
+            elif isinstance(element_spec, tf.TensorSpec):
+                return True if element_spec.shape.num_elements() is None else False
+            else:
+                raise ValueError("unrecognized element spec...")
+
+        def squeeze_output(output):
+            if isinstance(output, collections.abc.Sequence):
+                return [squeeze_output(ele) for ele in output]
+            elif isinstance(output, np.ndarray):
+                return np.squeeze(output, axis=0)
+            else:
+                raise ValueError("not supported output format....")
+
+        if tf.executing_eagerly():
+            index = 0
+            outputs = []
+            for iter_tensors in dataset:
+                samples = []
+                iter_inputs, iter_labels = iter_tensors[0], iter_tensors[1]
+                if isinstance(iter_inputs, tf.Tensor):
+                    samples.append(iter_inputs.numpy())
+                else:
+                    samples.append(tuple(iter_input.numpy() for iter_input in iter_inputs))
+                if isinstance(iter_labels, tf.Tensor):
+                    samples.append(iter_labels.numpy())
+                else:
+                    samples.append([np.array(l) for l in iter_labels])
+                index += 1
+                outputs.append(samples)
+                if index == batch_size:
+                    outputs = default_collate(outputs)
+                    yield outputs
+                    outputs = []
+                    index = 0
+            if len(outputs) > 0:
+                outputs = default_collate(outputs)
+                yield outputs
+        else:
+            try_single_batch = check_dynamic_shape(dataset.element_spec)
+            dataset = dataset.batch(1 if try_single_batch else batch_size, drop_last)
+            ds_iterator = tf.compat.v1.data.make_one_shot_iterator(dataset)
+            iter_tensors = ds_iterator.get_next()
+            data_config = tf.compat.v1.ConfigProto()
+            data_config.use_per_session_threads = 1
+            data_config.intra_op_parallelism_threads = 1
+            data_config.inter_op_parallelism_threads = 16
+            data_sess = tf.compat.v1.Session(config=data_config)
+            # pylint: disable=no-name-in-module
+            from tensorflow.python.framework.errors_impl import OutOfRangeError
+
+            while True:
+                if not try_single_batch:
+                    try:
+                        outputs = data_sess.run(iter_tensors)
+                        yield outputs
+                    except OutOfRangeError:
+                        data_sess.close()
+                        return
+                else:
+                    try:
+                        outputs = []
+                        for i in range(0, batch_size):
+                            outputs.append(squeeze_output(data_sess.run(iter_tensors)))
+                        outputs = default_collate(outputs)
+                        yield outputs
+                    except OutOfRangeError:
+                        if len(outputs) == 0:
+                            data_sess.close()
+                            return
+                        else:
+                            outputs = default_collate(outputs)
+                            yield outputs
+                            data_sess.close()
+                            return
diff --git a/examples/3.x_api/tensorflow/cv/resnet_v2_50/quantization/ptq/main.py b/examples/3.x_api/tensorflow/cv/resnet_v2_50/quantization/ptq/main.py
new file mode 100644
index 00000000000..3b9595476e6
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/resnet_v2_50/quantization/ptq/main.py
@@ -0,0 +1,144 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2023 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import time
+
+import tensorflow as tf
+import numpy as np
+
+from argparse import ArgumentParser
+from data_process import (
+    ImageRecordDataset, 
+    ComposeTransform, 
+    BilinearImagenetTransform, 
+    TFDataLoader,
+    TopKMetric,
+)
+
+tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
+
+arg_parser = ArgumentParser(description='Parse args')
+arg_parser.add_argument('-g', "--input-graph",
+                        help='Specify the input graph for the transform tool',
+                        dest='input_graph')
+arg_parser.add_argument("--output-graph",
+                        help='Specify tune result model save dir',
+                        dest='output_graph')
+arg_parser.add_argument('--benchmark', dest='benchmark', action='store_true', help='run benchmark')
+arg_parser.add_argument('--mode', dest='mode', default='performance', help='benchmark mode')
+arg_parser.add_argument('--tune', dest='tune', action='store_true', help='use neural_compressor to tune.')
+arg_parser.add_argument('--diagnose', dest='diagnose', action='store_true', help='use Neural Insights to diagnose tuning and benchmark.')
+arg_parser.add_argument('--dataset_location', dest='dataset_location',
+                          help='location of calibration dataset and evaluate dataset')
+arg_parser.add_argument('--batch_size', type=int, default=32, dest='batch_size', help='batch_size of benchmark')
+arg_parser.add_argument('--iters', type=int, default=100, dest='iters', help='interations')
+args = arg_parser.parse_args()
+
+def evaluate(model, eval_dataloader, metric, postprocess=None):
+    """Custom evaluate function to estimate the accuracy of the model.
+
+    Args:
+        model (tf.Graph_def): The input model graph
+        
+    Returns:
+        accuracy (float): evaluation result, the larger is better.
+    """
+    from neural_compressor.tensorflow import Model
+    model = Model(model)
+    input_tensor = model.input_tensor
+    output_tensor = model.output_tensor if len(model.output_tensor)>1 else \
+                        model.output_tensor[0]
+    iteration = -1
+    if args.benchmark and args.mode == 'performance':
+        iteration = args.iters
+
+    def eval_func(dataloader):
+        latency_list = []
+        for idx, (inputs, labels) in enumerate(dataloader):
+            # dataloader should keep the order and len of inputs same with input_tensor
+            inputs = np.array([inputs])
+            feed_dict = dict(zip(input_tensor, inputs))
+
+            start = time.time()
+            predictions = model.sess.run(output_tensor, feed_dict)
+            end = time.time()
+
+            metric.update(predictions, labels)
+            latency_list.append(end-start)
+            if idx + 1 == iteration:
+                break
+        latency = np.array(latency_list).mean() / args.batch_size
+        return latency
+
+    latency = eval_func(eval_dataloader)
+    if args.benchmark and args.mode == 'performance':
+        print("Batch size = {}".format(args.batch_size))
+        print("Latency: {:.3f} ms".format(latency * 1000))
+        print("Throughput: {:.3f} images/sec".format(1. / latency))
+    acc = metric.result()
+    return acc
+
+class eval_classifier_optimized_graph:
+    """Evaluate image classifier with optimized TensorFlow graph."""
+
+    def run(self):
+        """This is neural_compressor function include tuning, export and benchmark option."""
+        from neural_compressor.common import set_random_seed
+        set_random_seed(9527)
+
+        if args.tune:
+            from neural_compressor.tensorflow import StaticQuantConfig, quantize_model
+
+            dataset = ImageRecordDataset(
+                root=args.dataset_location, 
+                transform=ComposeTransform(transform_list= [
+                    BilinearImagenetTransform(height=224, width=224),
+                    ]
+                )
+            )
+            calib_dataloader = TFDataLoader(dataset=dataset, batch_size=10)
+
+            quant_config = StaticQuantConfig()
+            q_model = quantize_model(args.input_graph, quant_config, calib_dataloader)
+            q_model.save(args.output_graph)
+
+        if args.benchmark:
+            dataloader = create_dataloader('tensorflow', dataloader_args)
+            dataset = ImageRecordDataset(
+                root=args.dataset_location, 
+                transform=ComposeTransform(transform_list= [
+                    BilinearImagenetTransform(height=224, width=224),
+                    ]
+                )
+            )
+            dataloader = TFDataLoader(dataset=dataset, batch_size=args.batch_size)
+
+            def eval(model):
+                top1 = TopKMetric(k=1)
+                return evaluate(model, dataloader, top1)
+
+            if args.mode == 'performance':
+                eval(args.input_graph)
+            elif args.mode == 'accuracy':
+                acc_result = eval(args.input_graph)
+                print("Batch size = %d" % dataloader.batch_size)
+                print("Accuracy: %.5f" % acc_result)
+
+if __name__ == "__main__":
+    evaluate_opt_graph = eval_classifier_optimized_graph()
+    evaluate_opt_graph.run()
diff --git a/examples/3.x_api/tensorflow/cv/resnet_v2_50/quantization/ptq/requirements.txt b/examples/3.x_api/tensorflow/cv/resnet_v2_50/quantization/ptq/requirements.txt
new file mode 100644
index 00000000000..2755e1a41ac
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/resnet_v2_50/quantization/ptq/requirements.txt
@@ -0,0 +1,2 @@
+tensorflow
+neural-compressor
diff --git a/examples/3.x_api/tensorflow/cv/resnet_v2_50/quantization/ptq/run_benchmark.sh b/examples/3.x_api/tensorflow/cv/resnet_v2_50/quantization/ptq/run_benchmark.sh
new file mode 100644
index 00000000000..8ecac837cf7
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/resnet_v2_50/quantization/ptq/run_benchmark.sh
@@ -0,0 +1,51 @@
+#!/bin/bash
+set -x
+
+function main {
+
+  init_params "$@"
+  run_benchmark
+
+}
+
+# init params
+function init_params {
+  batch_size=32
+  iters=100
+
+  for var in "$@"
+  do
+    case $var in
+      --input_model=*)
+          input_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --mode=*)
+          mode=$(echo $var |cut -f2 -d=)
+      ;;
+      --dataset_location=*)
+          dataset_location=$(echo $var |cut -f2 -d=)
+      ;;
+      --batch_size=*)
+          batch_size=$(echo $var |cut -f2 -d=)
+      ;;
+      --iters=*)
+          iters=$(echo $var |cut -f2 -d=)
+      ;;
+    esac
+  done
+
+}
+
+# run_tuning
+function run_benchmark {
+
+    python main.py \
+            --input-graph ${input_model} \
+            --mode ${mode} \
+            --dataset_location ${dataset_location} \
+            --batch_size ${batch_size} \
+            --benchmark \
+            --iters ${iters}
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/cv/resnet_v2_50/quantization/ptq/run_quant.sh b/examples/3.x_api/tensorflow/cv/resnet_v2_50/quantization/ptq/run_quant.sh
new file mode 100644
index 00000000000..6a9e1b859c9
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/resnet_v2_50/quantization/ptq/run_quant.sh
@@ -0,0 +1,39 @@
+#!/bin/bash
+set -x
+
+function main {
+  init_params "$@"
+  run_tuning
+
+}
+
+# init params
+function init_params {
+
+  for var in "$@"
+  do
+    case $var in
+      --input_model=*)
+          input_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --output_model=*)
+          output_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --dataset_location=*)
+          dataset_location=$(echo $var |cut -f2 -d=)
+      ;;
+    esac
+  done
+
+}
+
+# run_tuning
+function run_tuning {
+    python main.py \
+            --input-graph ${input_model} \
+            --output-graph ${output_model} \
+            --dataset_location ${dataset_location} \
+            --tune
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/cv/vgg16/quantization/ptq/README.md b/examples/3.x_api/tensorflow/cv/vgg16/quantization/ptq/README.md
new file mode 100644
index 00000000000..00e00c7846d
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/vgg16/quantization/ptq/README.md
@@ -0,0 +1,108 @@
+Step-by-Step
+============
+
+This document list steps of reproducing vgg16 model tuning and benchmark results via Neural Compressor.
+This example can run on Intel CPUs and GPUs.
+
+> **Note**: 
+> The model is supported in validated TensorFlow [Version](/docs/source/installation_guide.md#validated-software-environment).
+# Prerequisite
+
+## 1. Environment
+
+### Installation
+Recommend python 3.9 or higher version.
+```shell
+pip install -r requirements.txt
+```
+
+### Install Intel Extension for Tensorflow
+#### Quantizing the model on Intel GPU(Mandatory to install ITEX)
+Intel Extension for Tensorflow is mandatory to be installed for quantizing the model on Intel GPUs.
+
+```shell
+pip install --upgrade intel-extension-for-tensorflow[xpu]
+```
+For any more details, please follow the procedure in [install-gpu-drivers](https://github.com/intel/intel-extension-for-tensorflow/blob/main/docs/install/install_for_xpu.md#install-gpu-drivers)
+
+#### Quantizing the model on Intel CPU(Optional to install ITEX)
+Intel Extension for Tensorflow for Intel CPUs is experimental currently. It's not mandatory for quantizing the model on Intel CPUs.
+
+```shell
+pip install --upgrade intel-extension-for-tensorflow[cpu]
+```
+> **Note**: 
+> The version compatibility of stock Tensorflow and ITEX can be checked [here](https://github.com/intel/intel-extension-for-tensorflow#compatibility-table). Please make sure you have installed compatible Tensorflow and ITEX.
+
+## 2. Prepare pre-trained model
+
+The vgg16 checkpoint file comes from [models](https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models).
+We can get the pb file by convert the checkpoint file.
+
+  1. Download the checkpoint file from [here](https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models)
+  ```shell
+  wget http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz
+  tar -xvf vgg_16_2016_08_28.tar.gz
+  ```
+
+  2. Exporting the Inference Graph
+  ```shell
+  git clone https://github.com/tensorflow/models
+  cd models/research/slim
+  python export_inference_graph.py \
+          --alsologtostderr \
+          --model_name=vgg_16 \
+          --output_file=/tmp/vgg_16_inf_graph.pb
+  ```
+  Make sure to use intel-tensorflow v1.15, and pip install tf_slim.
+  #### Install Intel Tensorflow 1.15 up2
+  Check your python version and use pip install 1.15.0 up2 from links below:
+  https://storage.googleapis.com/intel-optimized-tensorflow/intel_tensorflow-1.15.0up2-cp36-cp36m-manylinux2010_x86_64.whl                
+  https://storage.googleapis.com/intel-optimized-tensorflow/intel_tensorflow-1.15.0up2-cp37-cp37m-manylinux2010_x86_64.whl
+  https://storage.googleapis.com/intel-optimized-tensorflow/intel_tensorflow-1.15.0up2-cp35-cp35m-manylinux2010_x86_64.whl
+  > Please note: The ImageNet dataset has 1001, the **VGG** and **ResNet V1** final layers have only 1000 outputs rather than 1001. So we need add the `--labels_offset=1` flag in the inference graph exporting command.
+
+  3. Use [Netron](https://lutzroeder.github.io/netron/) to get the input/output layer name of inference graph pb, for vgg_16 the output layer name is `vgg_16/fc8/squeezed`
+
+  4. Freezing the exported Graph, please use the tool `freeze_graph.py` in [tensorflow v1.15.2](https://github.com/tensorflow/tensorflow/blob/v1.15.2/tensorflow/python/tools/freeze_graph.py) repo 
+  ```shell
+  python freeze_graph.py \
+          --input_graph=/tmp/vgg_16_inf_graph.pb \
+          --input_checkpoint=./vgg_16.ckpt \
+          --input_binary=true \
+          --output_graph=./frozen_vgg16.pb \
+          --output_node_names=vgg_16/fc8/squeezed
+  ```
+
+## 3. Prepare Dataset
+
+  TensorFlow [models](https://github.com/tensorflow/models) repo provides [scripts and instructions](https://github.com/tensorflow/models/tree/master/research/slim#an-automated-script-for-processing-imagenet-data) to download, process and convert the ImageNet dataset to the TF records format.
+  We also prepared related scripts in `examples/3.x_api/tensorflow/cv` directory. To download the raw images, the user must create an account with image-net.org. If you have downloaded the raw data and preprocessed the validation data by moving the images into the appropriate sub-directory based on the label (synset) of the image. we can use below command ro convert it to tf records format.
+
+  ```shell
+  cd examples/3.x_api/tensorflow/cv
+  # convert validation subset
+  bash prepare_dataset.sh --output_dir=./vgg16/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
+  # convert train subset
+  bash prepare_dataset.sh --output_dir=./vgg16/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/train/ --subset=train
+  ```
+> **Note**: 
+> The raw ImageNet dataset resides in JPEG files should be in the following directory structure. Taking validation set as an example:<br>
+> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/PATH/TO/img_raw/val/n01440764/ILSVRC2012_val_00000293.JPEG<br>
+> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/PATH/TO/img_raw/val/n01440764/ILSVRC2012_val_00000543.JPEG<br>
+> where 'n01440764' is the unique synset label associated with these images.
+
+# Run
+
+## 1 Quantization
+
+  ```shell
+  bash run_quant.sh --input_model=/PATH/TO/frozen_vgg16.pb \
+      --output_model=./nc_vgg16.pb --dataset_location=/path/to/ImageNet/
+  ```
+
+## 2. Benchmark
+  ```shell
+  bash run_benchmark.sh --input_model=./nc_vgg16.pb --mode=accuracy --dataset_location=/path/to/ImageNet/ --batch_size=32
+  bash run_benchmark.sh --input_model=./nc_vgg16.pb --mode=performance --dataset_location=/path/to/ImageNet/ --batch_size=1
+  ```
diff --git a/examples/3.x_api/tensorflow/cv/vgg16/quantization/ptq/data_process.py b/examples/3.x_api/tensorflow/cv/vgg16/quantization/ptq/data_process.py
new file mode 100644
index 00000000000..17b4d9cec5e
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/vgg16/quantization/ptq/data_process.py
@@ -0,0 +1,581 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2024 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import os
+import collections
+
+import numpy as np
+import tensorflow as tf
+
+from abc import abstractmethod
+from neural_compressor.common import logger
+from neural_compressor.tensorflow.utils.data import default_collate
+
+class ParseDecodeImagenet:
+    """Parse features in Example proto.
+
+    Returns:
+        tuple of parsed image and label
+    """
+
+    def __call__(self, sample):
+        """Parse features in example."""
+        # Dense features in Example proto.
+        feature_map = {
+            "image/encoded": tf.io.FixedLenFeature([], dtype=tf.string, default_value=""),
+            "image/class/label": tf.io.FixedLenFeature([1], dtype=tf.int64, default_value=-1),
+        }
+
+        sparse_float32 = tf.io.VarLenFeature(dtype=tf.float32)
+        # Sparse features in Example proto.
+        feature_map.update(
+            {
+                k: sparse_float32
+                for k in [
+                    "image/object/bbox/xmin",
+                    "image/object/bbox/ymin",
+                    "image/object/bbox/xmax",
+                    "image/object/bbox/ymax",
+                ]
+            }
+        )
+
+        features = tf.io.parse_single_example(serialized=sample, features=feature_map)
+        label = tf.cast(features["image/class/label"], dtype=tf.int32)
+        image = features["image/encoded"]
+        image = tf.image.decode_jpeg(image, channels=3, fancy_upscaling=False, dct_method="INTEGER_FAST")
+        return (image, label)
+
+
+class ResizeCropImagenet(object):
+    """Combination of a series of transforms which is applicable to images in Imagenet.
+
+    Args:
+        height (int): Height of the result
+        width (int): Width of the result
+        random_crop (bool, default=False): whether to random crop
+        resize_side (int, default=256):desired shape after resize operation
+        random_flip_left_right (bool, default=False): whether to random flip left and right
+        mean_value (list, default=[0.0,0.0,0.0]):means for each channel
+        scale (float, default=1.0):std value
+
+    Returns:
+        tuple of processed image and label
+    """
+
+    def __init__(
+        self,
+        height,
+        width,
+        random_crop=False,
+        resize_side=256,
+        resize_method="bilinear",
+        random_flip_left_right=False,
+        mean_value=[0.0, 0.0, 0.0],
+        scale=1.0,
+        data_format="channels_last",
+        subpixels="RGB",
+    ):
+        """Initialize `TensorflowResizeCropImagenetTransform` class."""
+        self.height = height
+        self.width = width
+        self.mean_value = mean_value
+        self.scale = scale
+        self.random_crop = random_crop
+        self.random_flip_left_right = random_flip_left_right
+        self.resize_side = resize_side
+        self.resize_method = resize_method
+        self.data_format = data_format
+        self.subpixels = subpixels
+
+    # sample is (images, labels)
+    def __call__(self, sample):
+        """Convert `TensorflowResizeCropImagenetTransform` feature."""
+        image, label = sample
+        shape = tf.shape(input=image)
+
+        height = (
+            tf.cast(shape[0], dtype=tf.float32)
+            if self.data_format == "channels_last"
+            else tf.cast(shape[1], dtype=tf.float32)
+        )
+        width = (
+            tf.cast(shape[1], dtype=tf.float32)
+            if self.data_format == "channels_last"
+            else tf.cast(shape[2], dtype=tf.float32)
+        )
+        scale = tf.cond(
+            pred=tf.greater(height, width),
+            true_fn=lambda: self.resize_side / width,
+            false_fn=lambda: self.resize_side / height,
+        )
+
+        scale = tf.cast(scale, dtype=tf.float32)
+        new_height = tf.cast(tf.math.rint(height * scale), dtype=tf.int32)
+        new_width = tf.cast(tf.math.rint(width * scale), dtype=tf.int32)
+
+        if self.subpixels == "BGR" and self.data_format == "channels_first":
+            # 'RGB'->'BGR'
+            image = tf.cond(
+                tf.equal(tf.rank(image), 3),
+                lambda: tf.experimental.numpy.moveaxis(image[::-1, ...], 0, -1),
+                lambda: tf.experimental.numpy.moveaxis(image[:, ::-1, ...], 1, -1),
+            )
+        elif self.subpixels == "BGR":
+            # 'RGB'->'BGR'
+            image = image[..., ::-1]
+        image = tf.expand_dims(image, 0)
+        image = tf.image.resize(image, [new_height, new_width], method=self.resize_method)
+        image = tf.squeeze(image)
+        shape = tf.shape(input=image)
+        if self.random_crop:
+            y0 = tf.random.uniform(shape=[], minval=0, maxval=(shape[0] - self.height + 1), dtype=tf.dtypes.int32)
+            x0 = tf.random.uniform(shape=[], minval=0, maxval=(shape[1] - self.width + 1), dtype=tf.dtypes.int32)
+        else:
+            y0 = (shape[0] - self.height) // 2
+            x0 = (shape[1] - self.width) // 2
+
+        image = tf.image.crop_to_bounding_box(image, y0, x0, self.height, self.width)
+        image.set_shape([self.height, self.width, 3])
+        if self.random_flip_left_right:
+            image = tf.image.random_flip_left_right(image)
+        means = tf.broadcast_to(self.mean_value, tf.shape(input=image))
+        image = (image - means) * self.scale
+        return (image, label)
+
+
+class ComposeTransform(object):
+    """Composes several transforms together.
+
+    Args:
+        transform_list (list of Transform objects):  list of transforms to compose
+
+    Returns:
+        sample (tuple): tuple of processed image and label
+    """
+
+    def __init__(self, transform_list):
+        """Initialize `ComposeTransform` class."""
+        self.transform_list = transform_list
+
+    def __call__(self, sample):
+        """Call transforms in transform_list."""
+        for transform in self.transform_list:
+            sample = transform(sample)
+        return sample
+
+
+class LabelShift(object):
+    """Convert label to label - label_shift.
+
+    Args:
+        label_shift(int, default=0): number of label shift
+
+    Returns:
+        tuple of processed image and label
+    """
+
+    def __init__(self, label_shift=0):
+        """Initialize `LabelShift` class."""
+        self.label_shift = label_shift
+
+    def __call__(self, sample):
+        """Convert label to label_shift."""
+        images, labels = sample
+        if isinstance(labels, np.ndarray):
+            labels = labels - self.label_shift
+        elif isinstance(labels, list):
+            if isinstance(labels[0], tuple):
+                labels = [tuple(np.array(label) - self.label_shift) for label in labels]
+            elif isinstance(labels[0], np.ndarray):
+                labels = [label - self.label_shift for label in labels]
+            else:
+                labels = np.array(labels) - self.label_shift
+                labels = labels.tolist()
+        else:
+            labels = np.array(labels) - self.label_shift
+        return images, labels
+
+
+class ImageRecordDataset(object):
+    """Tensorflow imageNet database in tf record format.
+
+    Please arrange data in this way:
+        root/validation-000-of-100
+        root/validation-001-of-100
+        ...
+        root/validation-099-of-100
+    The file name needs to follow this pattern: '* - * -of- *'
+
+    Args: root (str): Root directory of dataset.
+          transform (transform object, default=None):  transform to process input data.
+          filter (Filter objects, default=None): filter out examples according
+                                                 to specific conditions.
+    """
+
+    """Configuration for Imagenet dataset."""
+
+    def __new__(cls, root, transform=None, filter=None):
+        """Build a new object of TensorflowImageRecord class."""
+        from tensorflow.python.platform import gfile  # pylint: disable=no-name-in-module
+
+        glob_pattern = os.path.join(root, "*-*-of-*")
+        file_names = gfile.Glob(glob_pattern)
+        if not file_names:
+            raise ValueError("Found no files in --root matching: {}".format(glob_pattern))
+
+        # pylint: disable=no-name-in-module
+        from tensorflow.python.data.experimental import parallel_interleave
+
+        ds = tf.data.TFRecordDataset.list_files(file_names, shuffle=False)
+        ds = ds.apply(parallel_interleave(tf.data.TFRecordDataset, cycle_length=len(file_names)))
+
+        if transform is not None:
+            transform.transform_list.insert(0, ParseDecodeImagenet())
+        else:
+            transform = ParseDecodeImagenet()
+        ds = ds.map(transform, num_parallel_calls=None)
+        ds = ds.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)  # this number can be tuned
+        return ds
+
+
+class BaseMetric(object):
+    """The base class of Metric."""
+
+    def __init__(self, metric, single_output=False, hvd=None):
+        """Initialize the basic metric.
+
+        Args:
+            metric: The metric class.
+            single_output: Whether the output is single or not, defaults to False.
+            hvd: The Horovod class for distributed training, defaults to None.
+        """
+        self._metric_cls = metric
+        self._single_output = single_output
+        self._hvd = hvd
+
+    def __call__(self, *args, **kwargs):
+        """Evaluate the model predictions, and the reference.
+
+        Returns:
+            The class itself.
+        """
+        self._metric = self._metric_cls(*args, **kwargs)
+        return self
+
+    @abstractmethod
+    def update(self, preds, labels=None, sample_weight=None):
+        """Update the state that need to be evaluated.
+
+        Args:
+            preds: The prediction result.
+            labels: The reference. Defaults to None.
+            sample_weight: The sampling weight. Defaults to None.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def reset(self):
+        """Clear the predictions and labels.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def result(self):
+        """Evaluate the difference between predictions and labels.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @property
+    def metric(self):
+        """Return its metric class.
+
+        Returns:
+            The metric class.
+        """
+        return self._metric_cls
+
+    @property
+    def hvd(self):
+        """Return its hvd class.
+
+        Returns:
+            The hvd class.
+        """
+        return self._hvd
+
+    @hvd.setter
+    def hvd(self, hvd):
+        """Set its hvd.
+
+        Args:
+            hvd: The Horovod class for distributed training.
+        """
+        self._hvd = hvd
+
+
+class TopKMetric(BaseMetric):
+    """Compute Top-k Accuracy classification score for Tensorflow model.
+
+    This metric computes the number of times where the correct label is among
+    the top k labels predicted.
+
+    Attributes:
+        k (int): The number of most likely outcomes considered to find the correct label.
+        num_correct: The number of predictions that were correct classified.
+        num_sample: The total number of predictions.
+    """
+
+    def __init__(self, k=1):
+        """Initialize the k, number of samples and correct predictions.
+
+        Args:
+            k: The number of most likely outcomes considered to find the correct label.
+        """
+        self.k = k
+        self.num_correct = 0
+        self.num_sample = 0
+
+    def update(self, preds, labels, sample_weight=None):
+        """Add the predictions and labels.
+
+        Args:
+            preds: The predictions.
+            labels: The labels corresponding to the predictions.
+            sample_weight: The sample weight.
+        """
+        preds, labels = TopKMetric._topk_shape_validate(preds, labels)
+
+        labels = labels.reshape([len(labels)])
+        with tf.Graph().as_default() as acc_graph:
+            topk = tf.nn.in_top_k(
+                predictions=tf.constant(preds, dtype=tf.float32), targets=tf.constant(labels, dtype=tf.int32), k=self.k
+            )
+            fp32_topk = tf.cast(topk, tf.float32)
+            correct_tensor = tf.reduce_sum(input_tensor=fp32_topk)
+
+            with tf.compat.v1.Session() as acc_sess:
+                correct = acc_sess.run(correct_tensor)
+
+        self.num_sample += len(labels)
+        self.num_correct += correct
+
+    def reset(self):
+        """Reset the number of samples and correct predictions."""
+        self.num_correct = 0
+        self.num_sample = 0
+
+    def result(self):
+        """Compute the top-k score.
+
+        Returns:
+            The top-k score.
+        """
+        if self.num_sample == 0:
+            logger.warning("Sample num during evaluation is 0.")
+            return 0
+        elif getattr(self, "_hvd", None) is not None:  # pragma: no cover
+            allgather_num_correct = sum(self._hvd.allgather_object(self.num_correct))
+            allgather_num_sample = sum(self._hvd.allgather_object(self.num_sample))
+            return allgather_num_correct / allgather_num_sample
+        return self.num_correct / self.num_sample
+
+    @staticmethod
+    def _topk_shape_validate(preds, labels):
+        # preds shape can be Nxclass_num or class_num(N=1 by default)
+        # it's more suitable for 'Accuracy' with preds shape Nx1(or 1) output from argmax
+        if isinstance(preds, int):
+            preds = [preds]
+            preds = np.array(preds)
+        elif isinstance(preds, np.ndarray):
+            preds = np.array(preds)
+        elif isinstance(preds, list):
+            preds = np.array(preds)
+            preds = preds.reshape((-1, preds.shape[-1]))
+
+        # consider labels just int value 1x1
+        if isinstance(labels, int):
+            labels = [labels]
+            labels = np.array(labels)
+        elif isinstance(labels, tuple):
+            labels = np.array([labels])
+            labels = labels.reshape((labels.shape[-1], -1))
+        elif isinstance(labels, list):
+            if isinstance(labels[0], int):
+                labels = np.array(labels)
+                labels = labels.reshape((labels.shape[0], 1))
+            elif isinstance(labels[0], tuple):
+                labels = np.array(labels)
+                labels = labels.reshape((labels.shape[-1], -1))
+            else:
+                labels = np.array(labels)
+        # labels most have 2 axis, 2 cases: N(or Nx1 sparse) or Nxclass_num(one-hot)
+        # only support 2 dimension one-shot labels
+        # or 1 dimension one-hot class_num will confuse with N
+
+        if len(preds.shape) == 1:
+            N = 1
+            class_num = preds.shape[0]
+            preds = preds.reshape([-1, class_num])
+        elif len(preds.shape) >= 2:
+            N = preds.shape[0]
+            preds = preds.reshape([N, -1])
+            class_num = preds.shape[1]
+
+        label_N = labels.shape[0]
+        assert label_N == N, "labels batch size should same with preds"
+        labels = labels.reshape([N, -1])
+        # one-hot labels will have 2 dimension not equal 1
+        if labels.shape[1] != 1:
+            labels = labels.argsort()[..., -1:]
+        return preds, labels
+    
+
+class TFDataLoader(object):  # pragma: no cover
+    """Tensorflow dataloader class.
+
+    In tensorflow1.x dataloader is coupled with the graph, but it also support feed_dict
+    method to do session run, this dataloader is designed to satisfy the usage of feed dict
+    in tf1.x. Although it's a general dataloader and can be used in MXNet and PyTorch.
+
+    Args:
+        dataset: obj. wrapper of needed data.
+        batch_size: int. batch size
+    """
+
+    def __init__(self, dataset, batch_size=1, last_batch="rollover"):
+        """Initialize `TFDataDataLoader` class."""
+        self.dataset = dataset
+        self.last_batch = last_batch
+        self.batch_size = batch_size
+        dataset = dataset.batch(batch_size)
+
+    def batch(self, batch_size, last_batch="rollover"):
+        """Dataset return data per batch."""
+        drop_last = False if last_batch == "rollover" else True
+        self.batch_size = batch_size
+        self.dataset = self.dataset.batch(batch_size, drop_last)
+
+    def __iter__(self):
+        """Iterate dataloader."""
+        return self._generate_dataloader(
+            self.dataset,
+            batch_size=self.batch_size,
+            last_batch=self.last_batch,
+        )
+
+    def _generate_dataloader(
+        self,
+        dataset,
+        batch_size=1,
+        last_batch="rollover",
+        collate_fn=None,
+        sampler=None,
+        batch_sampler=None,
+        num_workers=None,
+        pin_memory=None,
+        distributed=False,
+    ):
+        """Yield data."""
+        drop_last = False if last_batch == "rollover" else True
+
+        def check_dynamic_shape(element_spec):
+            if isinstance(element_spec, collections.abc.Sequence):
+                return any([check_dynamic_shape(ele) for ele in element_spec])
+            elif isinstance(element_spec, tf.TensorSpec):
+                return True if element_spec.shape.num_elements() is None else False
+            else:
+                raise ValueError("unrecognized element spec...")
+
+        def squeeze_output(output):
+            if isinstance(output, collections.abc.Sequence):
+                return [squeeze_output(ele) for ele in output]
+            elif isinstance(output, np.ndarray):
+                return np.squeeze(output, axis=0)
+            else:
+                raise ValueError("not supported output format....")
+
+        if tf.executing_eagerly():
+            index = 0
+            outputs = []
+            for iter_tensors in dataset:
+                samples = []
+                iter_inputs, iter_labels = iter_tensors[0], iter_tensors[1]
+                if isinstance(iter_inputs, tf.Tensor):
+                    samples.append(iter_inputs.numpy())
+                else:
+                    samples.append(tuple(iter_input.numpy() for iter_input in iter_inputs))
+                if isinstance(iter_labels, tf.Tensor):
+                    samples.append(iter_labels.numpy())
+                else:
+                    samples.append([np.array(l) for l in iter_labels])
+                index += 1
+                outputs.append(samples)
+                if index == batch_size:
+                    outputs = default_collate(outputs)
+                    yield outputs
+                    outputs = []
+                    index = 0
+            if len(outputs) > 0:
+                outputs = default_collate(outputs)
+                yield outputs
+        else:
+            try_single_batch = check_dynamic_shape(dataset.element_spec)
+            dataset = dataset.batch(1 if try_single_batch else batch_size, drop_last)
+            ds_iterator = tf.compat.v1.data.make_one_shot_iterator(dataset)
+            iter_tensors = ds_iterator.get_next()
+            data_config = tf.compat.v1.ConfigProto()
+            data_config.use_per_session_threads = 1
+            data_config.intra_op_parallelism_threads = 1
+            data_config.inter_op_parallelism_threads = 16
+            data_sess = tf.compat.v1.Session(config=data_config)
+            # pylint: disable=no-name-in-module
+            from tensorflow.python.framework.errors_impl import OutOfRangeError
+
+            while True:
+                if not try_single_batch:
+                    try:
+                        outputs = data_sess.run(iter_tensors)
+                        yield outputs
+                    except OutOfRangeError:
+                        data_sess.close()
+                        return
+                else:
+                    try:
+                        outputs = []
+                        for i in range(0, batch_size):
+                            outputs.append(squeeze_output(data_sess.run(iter_tensors)))
+                        outputs = default_collate(outputs)
+                        yield outputs
+                    except OutOfRangeError:
+                        if len(outputs) == 0:
+                            data_sess.close()
+                            return
+                        else:
+                            outputs = default_collate(outputs)
+                            yield outputs
+                            data_sess.close()
+                            return
diff --git a/examples/3.x_api/tensorflow/cv/vgg16/quantization/ptq/main.py b/examples/3.x_api/tensorflow/cv/vgg16/quantization/ptq/main.py
new file mode 100644
index 00000000000..ffe960e1b1e
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/vgg16/quantization/ptq/main.py
@@ -0,0 +1,146 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2023 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import time
+
+import tensorflow as tf
+import numpy as np
+
+from argparse import ArgumentParser
+from data_process import (
+    ImageRecordDataset, 
+    ComposeTransform, 
+    ResizeCropImagenet, 
+    LabelShift,
+    TFDataLoader,
+    TopKMetric
+)
+
+
+tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
+
+arg_parser = ArgumentParser(description='Parse args')
+arg_parser.add_argument('-g', "--input-graph",
+                        help='Specify the input graph for the transform tool',
+                        dest='input_graph')
+arg_parser.add_argument("--output-graph",
+                        help='Specify tune result model save dir',
+                        dest='output_graph')
+arg_parser.add_argument('--benchmark', dest='benchmark', action='store_true', help='run benchmark')
+arg_parser.add_argument('--mode', dest='mode', default='performance', help='benchmark mode')
+arg_parser.add_argument('--tune', dest='tune', action='store_true', help='use neural_compressor to tune.')
+arg_parser.add_argument('--dataset_location', dest='dataset_location',
+                          help='location of calibration dataset and evaluate dataset')
+arg_parser.add_argument('--batch_size', type=int, default=32, dest='batch_size', help='batch_size of benchmark')
+arg_parser.add_argument('--iters', type=int, default=100, dest='iters', help='interations')
+args = arg_parser.parse_args()
+
+def evaluate(model, eval_dataloader, metric, postprocess=None):
+    """Custom evaluate function to estimate the accuracy of the model.
+
+    Args:
+        model (tf.Graph_def): The input model graph
+        
+    Returns:
+        accuracy (float): evaluation result, the larger is better.
+    """
+    from neural_compressor.tensorflow import Model
+    model = Model(model)
+    input_tensor = model.input_tensor
+    output_tensor = model.output_tensor if len(model.output_tensor)>1 else \
+                        model.output_tensor[0]
+    iteration = -1
+    if args.benchmark and args.mode == 'performance':
+        iteration = args.iters
+
+    def eval_func(dataloader):
+        latency_list = []
+        for idx, (inputs, labels) in enumerate(dataloader):
+            # dataloader should keep the order and len of inputs same with input_tensor
+            inputs = np.array([inputs])
+            feed_dict = dict(zip(input_tensor, inputs))
+
+            start = time.time()
+            predictions = model.sess.run(output_tensor, feed_dict)
+            end = time.time()
+            if postprocess:
+                predictions, labels = postprocess((predictions, labels))
+            metric.update(predictions, labels)
+            latency_list.append(end-start)
+            if idx + 1 == iteration:
+                break
+        latency = np.array(latency_list).mean() / args.batch_size
+        return latency
+
+    latency = eval_func(eval_dataloader)
+    if args.benchmark and args.mode == 'performance':
+        print("Batch size = {}".format(args.batch_size))
+        print("Latency: {:.3f} ms".format(latency * 1000))
+        print("Throughput: {:.3f} images/sec".format(1. / latency))
+    acc = metric.result()
+    return acc
+
+class eval_classifier_optimized_graph:
+    """Evaluate image classifier with optimized TensorFlow graph."""
+
+    def run(self):
+        """This is neural_compressor function include tuning, export and benchmark option."""
+        from neural_compressor.common import set_random_seed
+        set_random_seed(9527)
+
+        if args.tune:
+            from neural_compressor.tensorflow import StaticQuantConfig, quantize_model
+
+            dataset = ImageRecordDataset(
+                root=args.dataset_location, 
+                transform=ComposeTransform(transform_list= [
+                    ResizeCropImagenet(height=224, width=224, mean_value=[123.68, 116.78, 103.94]),
+                    ]
+                )
+            )
+            calib_dataloader = TFDataLoader(dataset=dataset, batch_size=10)
+
+            quant_config = StaticQuantConfig()
+            q_model = quantize_model(args.input_graph, quant_config, calib_dataloader)
+            q_model.save(args.output_graph)
+
+        if args.benchmark:
+            dataset = ImageRecordDataset(
+                root=args.dataset_location, 
+                transform=ComposeTransform(transform_list= [
+                    ResizeCropImagenet(height=224, width=224, mean_value=[123.68, 116.78, 103.94]),
+                    ]
+                )
+            )
+            dataloader = TFDataLoader(dataset=dataset, batch_size=args.batch_size)
+
+            def eval(model):
+                top1 = TopKMetric(k=1)
+                postprocess = LabelShift(label_shift=1)
+                return evaluate(model, dataloader, top1, postprocess)
+
+            if args.mode == 'performance':
+                eval(args.input_graph)
+            elif args.mode == 'accuracy':
+                acc_result = eval(args.input_graph)
+                print("Batch size = %d" % dataloader.batch_size)
+                print("Accuracy: %.5f" % acc_result)
+
+if __name__ == "__main__":
+    evaluate_opt_graph = eval_classifier_optimized_graph()
+    evaluate_opt_graph.run()
diff --git a/examples/3.x_api/tensorflow/cv/vgg16/quantization/ptq/requirements.txt b/examples/3.x_api/tensorflow/cv/vgg16/quantization/ptq/requirements.txt
new file mode 100644
index 00000000000..2755e1a41ac
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/vgg16/quantization/ptq/requirements.txt
@@ -0,0 +1,2 @@
+tensorflow
+neural-compressor
diff --git a/examples/3.x_api/tensorflow/cv/vgg16/quantization/ptq/run_benchmark.sh b/examples/3.x_api/tensorflow/cv/vgg16/quantization/ptq/run_benchmark.sh
new file mode 100644
index 00000000000..8ecac837cf7
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/vgg16/quantization/ptq/run_benchmark.sh
@@ -0,0 +1,51 @@
+#!/bin/bash
+set -x
+
+function main {
+
+  init_params "$@"
+  run_benchmark
+
+}
+
+# init params
+function init_params {
+  batch_size=32
+  iters=100
+
+  for var in "$@"
+  do
+    case $var in
+      --input_model=*)
+          input_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --mode=*)
+          mode=$(echo $var |cut -f2 -d=)
+      ;;
+      --dataset_location=*)
+          dataset_location=$(echo $var |cut -f2 -d=)
+      ;;
+      --batch_size=*)
+          batch_size=$(echo $var |cut -f2 -d=)
+      ;;
+      --iters=*)
+          iters=$(echo $var |cut -f2 -d=)
+      ;;
+    esac
+  done
+
+}
+
+# run_tuning
+function run_benchmark {
+
+    python main.py \
+            --input-graph ${input_model} \
+            --mode ${mode} \
+            --dataset_location ${dataset_location} \
+            --batch_size ${batch_size} \
+            --benchmark \
+            --iters ${iters}
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/cv/vgg16/quantization/ptq/run_quant.sh b/examples/3.x_api/tensorflow/cv/vgg16/quantization/ptq/run_quant.sh
new file mode 100644
index 00000000000..6a9e1b859c9
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/vgg16/quantization/ptq/run_quant.sh
@@ -0,0 +1,39 @@
+#!/bin/bash
+set -x
+
+function main {
+  init_params "$@"
+  run_tuning
+
+}
+
+# init params
+function init_params {
+
+  for var in "$@"
+  do
+    case $var in
+      --input_model=*)
+          input_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --output_model=*)
+          output_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --dataset_location=*)
+          dataset_location=$(echo $var |cut -f2 -d=)
+      ;;
+    esac
+  done
+
+}
+
+# run_tuning
+function run_tuning {
+    python main.py \
+            --input-graph ${input_model} \
+            --output-graph ${output_model} \
+            --dataset_location ${dataset_location} \
+            --tune
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/image_recognition/vision_transformer/quantization/ptq/README.md b/examples/3.x_api/tensorflow/cv/vision_transformer/quantization/ptq/README.md
similarity index 77%
rename from examples/3.x_api/tensorflow/image_recognition/vision_transformer/quantization/ptq/README.md
rename to examples/3.x_api/tensorflow/cv/vision_transformer/quantization/ptq/README.md
index 7dcf3e7a363..0d4fa041690 100644
--- a/examples/3.x_api/tensorflow/image_recognition/vision_transformer/quantization/ptq/README.md
+++ b/examples/3.x_api/tensorflow/cv/vision_transformer/quantization/ptq/README.md
@@ -40,10 +40,10 @@ wget https://storage.googleapis.com/intel-optimized-tensorflow/models/2_11_0/HF-
 ## 3. Prepare Dataset
 
   TensorFlow [models](https://github.com/tensorflow/models) repo provides [scripts and instructions](https://github.com/tensorflow/models/tree/master/research/slim#an-automated-script-for-processing-imagenet-data) to download, process and convert the ImageNet dataset to the TF records format.
-  We also prepared related scripts in ` examples/tensorflow/image_recognition/tensorflow_models/imagenet_prepare` directory. To download the raw images, the user must create an account with image-net.org. If you have downloaded the raw data and preprocessed the validation data by moving the images into the appropriate sub-directory based on the label (synset) of the image. we can use below command ro convert it to tf records format.
+  We also prepared related scripts in `examples/3.x_api/tensorflow/cv` directory. To download the raw images, the user must create an account with image-net.org. If you have downloaded the raw data and preprocessed the validation data by moving the images into the appropriate sub-directory based on the label (synset) of the image. we can use below command ro convert it to tf records format.
 
   ```shell
-  cd examples/3.x_api/tensorflow/image_recognition/tensorflow_models/
+  cd examples/3.x_api/tensorflow/cv
   # convert validation subset
   bash prepare_dataset.sh --output_dir=./vision_transformer/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
   # convert train subset
@@ -60,7 +60,7 @@ wget https://storage.googleapis.com/intel-optimized-tensorflow/models/2_11_0/HF-
 ## 1. Quantization
 
 ```shell
-bash run_quant.sh --input_model=<path to HF-ViT-Base16-Img224-frozen.pb> --output_model=./output --dataset_location=<path to imagenet>
+bash run_quant.sh --input_model=./HF-ViT-Base16-Img224-frozen.pb --output_model=./output --dataset_location=<path to imagenet>
 ```
 
 
@@ -69,7 +69,7 @@ bash run_quant.sh --input_model=<path to HF-ViT-Base16-Img224-frozen.pb> --outpu
 ### Benchmark the fp32 model
 
 ```shell
-bash run_benchmark.sh --input_model=<path to HF-ViT-Base16-Img224-frozen.pb> --mode=accuracy --dataset_location=<path to imagenet> --batch_size=32
+bash run_benchmark.sh --input_model=./HF-ViT-Base16-Img224-frozen.pb --mode=accuracy --dataset_location=<path to imagenet> --batch_size=32
 ```
 
 ### Benchmark the int8 model
diff --git a/examples/3.x_api/tensorflow/image_recognition/vision_transformer/quantization/ptq/__init__.py b/examples/3.x_api/tensorflow/cv/vision_transformer/quantization/ptq/__init__.py
similarity index 100%
rename from examples/3.x_api/tensorflow/image_recognition/vision_transformer/quantization/ptq/__init__.py
rename to examples/3.x_api/tensorflow/cv/vision_transformer/quantization/ptq/__init__.py
diff --git a/examples/3.x_api/tensorflow/image_recognition/vision_transformer/quantization/ptq/data_process.py b/examples/3.x_api/tensorflow/cv/vision_transformer/quantization/ptq/data_process.py
similarity index 100%
rename from examples/3.x_api/tensorflow/image_recognition/vision_transformer/quantization/ptq/data_process.py
rename to examples/3.x_api/tensorflow/cv/vision_transformer/quantization/ptq/data_process.py
diff --git a/examples/3.x_api/tensorflow/image_recognition/vision_transformer/quantization/ptq/main.py b/examples/3.x_api/tensorflow/cv/vision_transformer/quantization/ptq/main.py
similarity index 99%
rename from examples/3.x_api/tensorflow/image_recognition/vision_transformer/quantization/ptq/main.py
rename to examples/3.x_api/tensorflow/cv/vision_transformer/quantization/ptq/main.py
index 49b4771c61a..92b2ea0fb2a 100644
--- a/examples/3.x_api/tensorflow/image_recognition/vision_transformer/quantization/ptq/main.py
+++ b/examples/3.x_api/tensorflow/cv/vision_transformer/quantization/ptq/main.py
@@ -146,7 +146,6 @@ def run(self):
             with tf.io.gfile.GFile(args.input_graph, "rb") as f:
                 sm.ParseFromString(f.read())
             graph_def = sm.meta_graphs[0].graph_def
-            postprocess = ShiftRescale()
 
             q_model = quantize_model(graph_def, quant_config, calib_dataloader)
             q_model.save(args.output_graph)
diff --git a/examples/3.x_api/tensorflow/image_recognition/vision_transformer/quantization/ptq/requirements.txt b/examples/3.x_api/tensorflow/cv/vision_transformer/quantization/ptq/requirements.txt
similarity index 100%
rename from examples/3.x_api/tensorflow/image_recognition/vision_transformer/quantization/ptq/requirements.txt
rename to examples/3.x_api/tensorflow/cv/vision_transformer/quantization/ptq/requirements.txt
diff --git a/examples/3.x_api/tensorflow/image_recognition/vision_transformer/quantization/ptq/run_benchmark.sh b/examples/3.x_api/tensorflow/cv/vision_transformer/quantization/ptq/run_benchmark.sh
similarity index 100%
rename from examples/3.x_api/tensorflow/image_recognition/vision_transformer/quantization/ptq/run_benchmark.sh
rename to examples/3.x_api/tensorflow/cv/vision_transformer/quantization/ptq/run_benchmark.sh
diff --git a/examples/3.x_api/tensorflow/cv/vision_transformer/quantization/ptq/run_quant.sh b/examples/3.x_api/tensorflow/cv/vision_transformer/quantization/ptq/run_quant.sh
new file mode 100644
index 00000000000..6a9e1b859c9
--- /dev/null
+++ b/examples/3.x_api/tensorflow/cv/vision_transformer/quantization/ptq/run_quant.sh
@@ -0,0 +1,39 @@
+#!/bin/bash
+set -x
+
+function main {
+  init_params "$@"
+  run_tuning
+
+}
+
+# init params
+function init_params {
+
+  for var in "$@"
+  do
+    case $var in
+      --input_model=*)
+          input_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --output_model=*)
+          output_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --dataset_location=*)
+          dataset_location=$(echo $var |cut -f2 -d=)
+      ;;
+    esac
+  done
+
+}
+
+# run_tuning
+function run_tuning {
+    python main.py \
+            --input-graph ${input_model} \
+            --output-graph ${output_model} \
+            --dataset_location ${dataset_location} \
+            --tune
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/README.md b/examples/3.x_api/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/README.md
index f82b696bd07..41a673fc834 100644
--- a/examples/3.x_api/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/README.md
+++ b/examples/3.x_api/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/README.md
@@ -74,7 +74,7 @@ bash prepare_dataset.sh --output_dir=./data
 ### Convert the dataset to TF Record format
 After the dataset is downloaded by either of ways above, the dataset should be converted to files of TF Record format.
 ```shell
-python create_tf_record.py --vocab_file=data/vocab.txt --predict_file=data/dev-v1.1.json --output_file=./data/eval.tf_record
+python create_tf_record.py --vocab_file=data/vocab.txt --predict_file=data/dev-v1.1.json --output_file=data/eval.tf_record
 ```
 
 # Run Command
@@ -82,11 +82,11 @@ python create_tf_record.py --vocab_file=data/vocab.txt --predict_file=data/dev-v
 
 ## Quantization
   ```shell
-  bash run_quant.sh --input_model=./fp32_bert_squad.pb --output_model=./bert_squad_int8.pb --dataset_location=/path/to/evaluation/dataset
+  bash run_quant.sh --input_model=./fp32_bert_squad.pb --output_model=./bert_squad_int8.pb --dataset_location=data
   ```
 
 ## Benchmark
   ```shell
-  bash run_benchmark.sh --input_model=./bert_squad_int8.pb --mode=accuracy --dataset_location=/path/to/evaluation/dataset --batch_size=64
-  bash run_benchmark.sh --input_model=./bert_squad_int8.pb --mode=performance --dataset_location=/path/to/evaluation/dataset --batch_size=64
+  bash run_benchmark.sh --input_model=./bert_squad_int8.pb --mode=accuracy --dataset_location=data --batch_size=64
+  bash run_benchmark.sh --input_model=./bert_squad_int8.pb --mode=performance --dataset_location=data --batch_size=64
   ```
\ No newline at end of file
diff --git a/examples/3.x_api/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/main.py b/examples/3.x_api/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/main.py
index b5403618f40..7f99b3507fc 100644
--- a/examples/3.x_api/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/main.py
+++ b/examples/3.x_api/tensorflow/nlp/bert_large_squad_model_zoo/quantization/ptq/main.py
@@ -134,8 +134,5 @@ def eval(model):
         q_model = quantize_model(model, quant_config, dataloader)
         q_model.save(FLAGS.output_model)
 
-        dataset = ModelZooBertDataset(root=data_path, label_file=label_path)
-        dataloader = ModelZooBertDataLoader(dataset=dataset, batch_size=FLAGS.batch_size)
-
 if __name__ == "__main__":
     tf.compat.v1.app.run()
diff --git a/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/README.md b/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/README.md
new file mode 100644
index 00000000000..83d17508bb0
--- /dev/null
+++ b/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/README.md
@@ -0,0 +1,187 @@
+Step-by-Step
+============
+
+This document is used to list steps of reproducing TensorFlow Intel® Neural Compressor tuning zoo result of DistilBERT base. This example can be run on Intel CPUs and GPUs.
+
+## Model Details
+This DistilBERT base model is based on the paper [*DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter*](https://arxiv.org/abs/1910.01108). \
+The [pretrained-model](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english?text=I+like+you.+I+love+you) thus used, was taken from [Hugging face model repository](https://huggingface.co/models). \
+The frozen model pb can be found at [Model Zoo for Intel® Architecture](https://github.com/IntelAI/models/tree/master/models/language_modeling/tensorflow/distilbert_base/inference).
+
+## Dataset Details
+We use a part of Stanford Sentiment Treebank corpus for our task. Specifically, the validation split present in the SST2 dataset in the hugging face [repository](https://huggingface.co/datasets/sst2). It contains 872 labeled English sentences. The details for downloading the dataset are given below. 
+
+## Prerequisite
+
+### 1. Install Intel® Neural Compressor
+```shell
+pip install neural-compressor
+```
+
+### 2. Install TensorFlow 2.11.dev202242
+Build a TensorFlow pip package from [intel-tensorflow spr_ww42 branch](https://github.com/Intel-tensorflow/tensorflow/tree/spr_ww42) and install it. How to build a TensorFlow pip package from source please refer to this [tutorial](https://www.tensorflow.org/install/source).
+
+### 3. Install Requirements
+```shell
+pip install -r requirements.txt
+```
+
+### 4. Install Intel® Extension for TensorFlow
+
+#### Quantizing the model on Intel GPU(Mandatory to install ITEX)
+Intel Extension for Tensorflow is mandatory to be installed for quantizing the model on Intel GPUs.
+
+```shell
+pip install --upgrade intel-extension-for-tensorflow[xpu]
+```
+Please refer to the [Installation Guides](https://dgpu-docs.intel.com/installation-guides/ubuntu/ubuntu-focal-dc.html) for latest Intel GPU driver installation.
+For any more details, please follow the procedure in [install-gpu-drivers](https://github.com/intel/intel-extension-for-tensorflow/blob/main/docs/install/install_for_xpu.md#install-gpu-drivers).
+
+#### Quantizing the model on Intel CPU(Optional to install ITEX)
+Intel Extension for Tensorflow for Intel CPUs is experimental currently. It's not mandatory for quantizing the model on Intel CPUs.
+
+```shell
+pip install --upgrade intel-extension-for-tensorflow[cpu]
+```
+
+> **Note**: 
+> The version compatibility of stock Tensorflow and ITEX can be checked [here](https://github.com/intel/intel-extension-for-tensorflow#compatibility-table). Please make sure you have installed compatible Tensorflow and ITEX.
+
+### 5. Download Dataset
+```shell
+python download_dataset.py --path_to_save_dataset <enter path to save dataset>
+```
+
+### 6. Download Model
+Download Frozen graph:
+```shell
+wget https://storage.googleapis.com/intel-optimized-tensorflow/models/2_10_0/distilbert_frozen_graph_fp32_final.pb
+```
+
+## Run Command
+### Run Tuning:
+```shell
+bash run_quant.sh \
+    --input_model=$INPUT_MODEL \
+    --dataset_location=$DATASET_DIR \
+    --output_model=$OUTPUT_MODEL \
+    --batch_size=$BATCH_SIZE \
+    --max_seq_length=$MAX_SEQ \
+    --warmup_steps=$WARMUPS \
+    --num_inter=$INTER_THREADS \
+    --num_intra=$INTRA_THREADS
+```
+### Run Benchmark:
+```shell
+# performance mode: get performance
+bash run_benchmark.sh \
+    --input_model=$INPUT_MODEL \
+    --dataset_location=$DATASET_DIR \
+    --mode=performance \
+    --batch_size=$BATCH_SIZE \
+    --max_seq_length=$MAX_SEQ \
+    --iters=$ITERS \
+    --warmup_steps=$WARMUPS \
+    --num_inter=$INTER_THREADS \
+    --num_intra=$INTRA_THREADS
+```
+
+```shell
+# accuracy mode: get accuracy
+bash run_benchmark.sh \
+    --input_model=$INPUT_MODEL \
+    --dataset_location=$DATASET_DIR \
+    --mode=accuracy \
+    --batch_size=$BATCH_SIZE \
+    --max_seq_length=$MAX_SEQ \
+    --warmup_steps=$WARMUPS \
+    --num_inter=$INTER_THREADS \
+    --num_intra=$INTRA_THREADS
+```
+
+Where (Default values are shown in the square brackets):
+   * $INPUT_MODEL ["./distilbert_base_fp32.pb"]-- The path to input FP32 frozen model .pb file to load
+   * $DATASET_DIR ["./sst2_validation_dataset"]-- The path to input dataset directory
+   * $OUTPUT_MODEL ["./output_distilbert_base_int8.pb"]-- The user-specified export path to the output INT8 quantized model
+   * $BATCH_SIZE [128]-- The batch size for model inference
+   * $MAX_SEQ [128]-- The maximum total sequence length after tokenization
+   * $ITERS [872]-- The number of iterations to run in benchmark mode, maximum value is 872
+   * $WARMUPS [10]-- The number of warmup steps before benchmarking the model, maximum value is 22
+   * $INTER_THREADS [2]-- The number of inter op parallelism thread to use, which can be set to the number of sockets
+   * $INTRA_THREADS [28]-- The number of intra op parallelism thread to use, which can be set to the number of physical core per socket
+
+
+### Run Smooth Quant to improve int8 accuracy
+
+#### Tuning
+```shell
+bash run_quant.sh \
+    --input_model=$INPUT_MODEL \
+    --dataset_location=$DATASET_DIR \
+    --output_model=$OUTPUT_MODEL \
+    --batch_size=$BATCH_SIZE \
+    --max_seq_length=$MAX_SEQ \
+    --warmup_steps=$WARMUPS \
+    --num_inter=$INTER_THREADS \
+    --num_intra=$INTRA_THREADS \
+    --sq=True
+```
+
+
+Details of enabling Intel® Neural Compressor on DistilBERT base for TensorFlow
+=========================
+
+This is a tutorial of how to enable DistilBERT base model with Intel® Neural Compressor.
+## User Code Analysis
+1. User specifies fp32 *model*, calibration dataloader *q_dataloader*, evaluation dataloader *eval_dataloader* and metric.
+
+2. User specifies fp32 *model*, calibration dataloader *q_dataloader* and a custom *eval_func* which encapsulates the evaluation dataloader and metric by itself.
+
+For DistilBERT base, we applied the latter one. The task is to implement the *q_dataloader* and *eval_func*.
+
+
+### q_dataloader Part Adaption
+Below dataloader class uses generator function to provide the model with input.
+
+```python
+class Dataloader(object):
+    def __init__(self, data_location, batch_size, steps):
+        self.batch_size = batch_size
+        self.data_location = data_location
+        self.num_batch = math.ceil(steps / batch_size)
+
+    def __iter__(self):
+        return self.generate_dataloader(self.data_location).__iter__()
+
+    def __len__(self):
+        return self.num_batch
+
+    def generate_dataloader(self, data_location):
+        dataset = load_dataset(data_location)
+        for batch_id in range(self.num_batch):
+            feed_dict, labels = create_feed_dict_and_labels(dataset, batch_id, self.num_batch)
+            yield feed_dict, labels
+```
+
+### Code Update
+After prepare step is done, we add the code for quantization tuning to generate quantized model.
+
+#### Tune
+```python
+    from neural_compressor.tensorflow import StaticQuantConfig, SmoothQuantConfig, quantize_model
+
+    quant_config = SmoothQuantConfig(alpha=0.6) if ARGS.sq else StaticQuantConfig()
+    q_model = quantize_model(graph, quant_config, self.dataloader)
+    try:
+        q_model.save(ARGS.output_graph)
+    except Exception as e:
+        tf.compat.v1.logging.error("Failed to save model due to {}".format(str(e)))
+```
+#### Benchmark
+```python
+    if ARGS.mode == 'performance':
+        self.eval_func(graph)
+    elif ARGS.mode == 'accuracy':
+        accuracy = self.eval_func(graph)
+        logger.info("Accuracy: {:.4f}".format(accuracy))
+```
diff --git a/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/download_dataset.py b/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/download_dataset.py
new file mode 100644
index 00000000000..b92cf39874f
--- /dev/null
+++ b/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/download_dataset.py
@@ -0,0 +1,38 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2022 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+''' 
+Script to download and save dataset
+'''
+from datasets import load_dataset
+from argparse import ArgumentParser
+import os
+
+def main():
+    arg_parser = ArgumentParser(description="Download and save dataset")
+    arg_parser.add_argument("-p", "--path_to_save_dataset", type=str,
+                             help="path to save the dataset",
+                             default="./")
+    args = arg_parser.parse_args()
+    dataset = load_dataset("glue", "sst2", split= "validation")
+    path = os.path.join(args.path_to_save_dataset, "sst2_validation_dataset")
+    dataset.save_to_disk(path)
+    print("Dataset saved in location: {}".format(path))
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/main.py b/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/main.py
new file mode 100644
index 00000000000..e0fce11736e
--- /dev/null
+++ b/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/main.py
@@ -0,0 +1,324 @@
+
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2023 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+'''DistilBERT base inference, implementation adapted from Hugging Face Library https://huggingface.co/'''
+import time
+import os
+import math
+
+import tensorflow as tf
+import numpy as np
+
+from argparse import ArgumentParser
+from transformers import AutoTokenizer
+from datasets import load_from_disk
+from tensorflow.core.protobuf import saved_model_pb2
+from tensorflow.python.client import timeline
+
+from neural_compressor.common import logger
+from neural_compressor.tensorflow.utils import dump_elapsed_time
+
+
+
+def boolean_string(s):
+    if s not in {'False', 'True'}:
+        raise ValueError('Not a valid boolean string')
+    return s == 'True'
+
+arg_parser = ArgumentParser(description="Distilbert inference")
+arg_parser.add_argument("--task-name", type=str,
+                        help="Name of the task to run benchmark.",
+                        dest="task_name",
+                        default="sst2"
+                        )
+arg_parser.add_argument("-c", "--config", type=str,
+                        help="Quantization configuration file to load.",
+                        dest="config",
+                        default="distilbert_base.yaml"
+                        )
+arg_parser.add_argument("-g", "--in-graph", type=str,
+                        help="Full path to the input graph.",
+                        dest="input_graph",
+                        default=None
+                        )
+arg_parser.add_argument("--data-location", type=str,
+                        help="Path to the dataset.",
+                        dest="data_location"
+                        )
+arg_parser.add_argument("-o", "--output-graph", type=str,
+                        help="The output path of quantized graph.",
+                        dest="output_graph",
+                        default="output_distilbert_base_int8.pb"
+                        )
+arg_parser.add_argument("-m", "--mode", type=str,
+                        choices=['performance', 'accuracy'],
+                        help="One of two options: 'performance'/'accuracy'.",
+                        dest="mode",
+                        default="performance"
+                        )
+arg_parser.add_argument("--tune", type=boolean_string,
+                        help="whether to apply quantization",
+                        dest="tune",
+                        default=False
+                        )
+arg_parser.add_argument('--sq', type=boolean_string, dest='sq', help='smooth quantization', default=False)
+arg_parser.add_argument("--benchmark", type=boolean_string,
+                        help="whether to do benchmark",
+                        dest="benchmark",
+                        default=False
+                        )
+arg_parser.add_argument('-e', "--num-inter-threads", type=int,
+                        help="The number of inter-thread.",
+                        dest="num_inter_threads",
+                        default=2
+                        )
+arg_parser.add_argument('-a', "--num-intra-threads", type=int,
+                        help="The number of intra-thread.",
+                        dest="num_intra_threads",
+                        default=28
+                        )
+arg_parser.add_argument("--pad-to-max-length", type=boolean_string,
+                        help="Padding option.",
+                        dest="pad_to_max_length",
+                        default=True
+                        )
+arg_parser.add_argument("--warmup-steps", type=int,
+                        help="Number of warmup steps.",
+                        dest="warmup_steps",
+                        default=10
+                        )
+arg_parser.add_argument("--max-seq-length", type=int,
+                        help="Maximum total sequence length after tokenization.",
+                        dest="max_seq_length",
+                        default=128
+                        )
+arg_parser.add_argument("--steps", type=int,
+                        help="Number of steps.",
+                        dest="steps",
+                        default=872
+                        )
+arg_parser.add_argument("--batch-size", type=int,
+                        help="Inference batch-size.",
+                        dest="batch_size",
+                        default=128
+                        )
+arg_parser.add_argument("--profile", dest='profile',
+                        type=boolean_string, help="profile",
+                        default=False)
+
+ARGS = arg_parser.parse_args()
+MAX_STEPS = 872
+MAX_WARMUP_STEPS = 22
+
+def create_feed_dict_and_labels(dataset, batch_id= None, num_batch= None, idx= None):
+    """Return the input dictionary for the given batch."""
+    if idx is None:
+        start_idx = batch_id * ARGS.batch_size
+        if batch_id == num_batch - 1:
+            end_idx = ARGS.steps
+        else:
+            end_idx = start_idx + ARGS.batch_size
+        input_ids = np.array(dataset["input_ids"])[start_idx:end_idx, :]
+        attention_mask = np.array(dataset["attention_mask"])[start_idx:end_idx, :]
+        feed_dict = {"input_ids:0": input_ids,
+                     "attention_mask:0": attention_mask,
+        }
+        labels = np.array(dataset["label"])[start_idx: end_idx]
+    else:
+        input_ids = np.array(dataset["input_ids"])[idx, :].reshape(1, -1)
+        attention_mask = np.array(dataset["attention_mask"])[idx, :].reshape(1, -1)
+        feed_dict = {"input_ids:0": input_ids,
+                     "attention_mask:0": attention_mask,
+        }
+        labels = np.array(dataset["label"])[idx]
+    return feed_dict, labels
+
+def load_dataset(data_location):
+    def preprocess_function(examples):
+        """Tokenize the texts."""
+        sentence1_key, sentence2_key = "sentence", None
+        args = (
+            (examples[sentence1_key],) if sentence2_key is None
+            else (examples[sentence1_key], examples[sentence2_key])
+        )
+        result = tokenizer(*args, padding="max_length",
+                            max_length=ARGS.max_seq_length,
+                            truncation=True
+        )
+        return result
+
+    # Load dataset (only validation split for inference)
+    dataset = load_from_disk(data_location)
+    # Load tokenizer
+    tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
+    # Set max sequence length
+    if ARGS.max_seq_length > tokenizer.model_max_length:
+        logger.info(f"The max sequence length passed ({ARGS.max_seq_length}) \
+                    is larger than the max supported by model \
+                   ({tokenizer.model_max_length}).Using max_seq_length =  \
+                   {tokenizer.model_max_length}")
+    ARGS.max_seq_length = min(ARGS.max_seq_length, tokenizer.model_max_length)
+    # Tokenize the dataset
+    dataset = dataset.map(preprocess_function, batched=True)
+    return dataset
+
+class Dataloader(object):
+    def __init__(self, data_location, batch_size, steps):
+        self.batch_size = batch_size
+        self.data_location = data_location
+        self.num_batch = math.ceil(steps / batch_size)
+
+    def __iter__(self):
+        return self.generate_dataloader(self.data_location).__iter__()
+
+    def __len__(self):
+        return self.num_batch
+
+    def generate_dataloader(self, data_location):
+        dataset = load_dataset(data_location)
+        for batch_id in range(self.num_batch):
+            feed_dict, labels = create_feed_dict_and_labels(dataset, batch_id, self.num_batch)
+            yield feed_dict, labels
+
+class Distilbert_base(object):
+    def __init__(self):
+        self.validate_args()
+        self.dataset = load_dataset(ARGS.data_location)
+        self.dataloader = Dataloader(ARGS.data_location, ARGS.batch_size, ARGS.steps)
+
+    def validate_args(self):
+        if ARGS.warmup_steps > MAX_WARMUP_STEPS:
+            logger.warning("Warmup steps greater than max possible value of 22." + \
+                           " Setting to max value of ", MAX_WARMUP_STEPS)
+            ARGS.warmup_steps = MAX_WARMUP_STEPS
+        if ARGS.tune or ARGS.sq or (ARGS.benchmark and ARGS.mode == "accuracy"):
+            ARGS.steps = MAX_STEPS
+        elif ARGS.benchmark:
+            if ARGS.steps > (MAX_STEPS - MAX_WARMUP_STEPS):
+                logger.warning("Steps greater than max possible value of {}.".format(MAX_STEPS - MAX_WARMUP_STEPS))
+                logger.warning("Setting to max value of {}".format(MAX_STEPS - MAX_WARMUP_STEPS))
+                ARGS.steps = MAX_STEPS - MAX_WARMUP_STEPS
+        if not ARGS.data_location:
+            raise SystemExit("Missing dataset path.")
+
+    def load_graph(self):
+        """Load the frozen model."""
+        graph_def = tf.compat.v1.GraphDef()
+        sm = saved_model_pb2.SavedModel()
+        with tf.io.gfile.GFile(ARGS.input_graph, "rb") as f:
+            try:
+                content = f.read()
+                graph_def.ParseFromString(content)
+            except Exception:
+                sm.ParseFromString(content)
+                graph_def = sm.meta_graphs[0].graph_def
+        with tf.Graph().as_default() as graph:
+            tf.import_graph_def(graph_def, name="")
+        logger.info("Loaded graph from: " + ARGS.input_graph)
+        return graph
+
+    def get_correct_predictions(self, preds, label_ids):
+        """Evaluate the predictions.
+
+        return the total number of correct predictions.
+        """
+        preds = np.argmax(preds, axis=1)
+        correct_preds = 0
+        for pred, label in zip(preds, label_ids):
+            if pred == label:
+                correct_preds += 1
+        return correct_preds
+
+    @dump_elapsed_time(customized_msg="Customized eval_func")
+    def eval_func(self, graph):
+        # Set the config for running
+        config = tf.compat.v1.ConfigProto()
+        config.intra_op_parallelism_threads=ARGS.num_intra_threads
+        config.inter_op_parallelism_threads=ARGS.num_inter_threads
+        run_options = tf.compat.v1.RunOptions(trace_level=tf.compat.v1.RunOptions.FULL_TRACE)
+        run_metadata = tf.compat.v1.RunMetadata()
+
+        output = graph.get_tensor_by_name('Identity:0')
+        total_time = 0
+        accuracy = 0
+        logger.info("Started warmup for {} steps...".format(ARGS.warmup_steps))
+        start_step_idx = MAX_STEPS - MAX_WARMUP_STEPS
+        with tf.compat.v1.Session(graph=graph, config=config) as sess:
+            # Warm up
+            for step in range(start_step_idx, start_step_idx + ARGS.warmup_steps):
+                feed_dict, _ = create_feed_dict_and_labels(self.dataset, idx=step)
+                _ = sess.run(output, feed_dict= feed_dict)
+            logger.info("Warmup completed.")
+            # Inference
+            logger.info("Starting inference for {} steps...".format(ARGS.steps))
+            total_correct_predictions = 0
+            iter = 0
+            for feed_dict, labels in self.dataloader:
+                iter += 1
+                start_time = time.time()
+                if ARGS.profile:
+                    pred = sess.run(output, feed_dict=feed_dict, options=run_options, run_metadata=run_metadata)
+                else:
+                    pred = sess.run(output, feed_dict=feed_dict)
+                run_time = time.time() - start_time
+                if ARGS.tune or ARGS.sq or (ARGS.benchmark and ARGS.mode == "accuracy"):
+                    total_correct_predictions += self.get_correct_predictions(pred, labels)
+                total_time += run_time
+                # save profiling file
+                if ARGS.profile and iter == int(self.dataloader.num_batch / 2):
+                        trace = timeline.Timeline(step_stats=run_metadata.step_stats)
+                        model_dir = str(os.path.dirname(os.path.realpath(__file__))) + '/timeline'
+                        if not os.path.exists(model_dir):
+                            try:
+                                os.makedirs(model_dir)
+                            except:
+                                pass
+                        profiling_file = model_dir + '/timeline-' + str(iter + 1) + '-' + str(os.getpid()) + '.json'
+                        with open(profiling_file, 'w') as trace_file:
+                            trace_file.write(trace.generate_chrome_trace_format(show_memory=False))
+        time_per_batch = total_time / float(ARGS.steps / ARGS.batch_size)
+        accuracy = total_correct_predictions / ARGS.steps
+        if ARGS.benchmark and ARGS.mode == 'performance':
+            logger.info("Latency: {:.4f} ms".format(time_per_batch * 1000))
+            logger.info("Throughput: {:.4f} sentences/sec".format(self.dataloader.batch_size / time_per_batch))
+        return accuracy
+
+    def run(self):
+        graph = self.load_graph()
+        if ARGS.tune or ARGS.sq:
+            from neural_compressor.tensorflow import StaticQuantConfig, SmoothQuantConfig, quantize_model
+
+            quant_config = SmoothQuantConfig(alpha=0.6) if ARGS.sq else StaticQuantConfig()
+            q_model = quantize_model(graph, quant_config, self.dataloader)
+            try:
+                q_model.save(ARGS.output_graph)
+            except Exception as e:
+                tf.compat.v1.logging.error("Failed to save model due to {}".format(str(e)))
+        elif ARGS.benchmark:
+            assert ARGS.mode == 'performance' or ARGS.mode == 'accuracy', \
+            "Benchmark only supports performance or accuracy mode."
+            if ARGS.mode == 'performance':
+                self.eval_func(graph)
+            elif ARGS.mode == 'accuracy':
+                accuracy = self.eval_func(graph)
+                logger.info("Accuracy: {:.4f}".format(accuracy))
+
+if __name__ == "__main__":
+    distilbert_ob = Distilbert_base()
+    distilbert_ob.run()
diff --git a/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/requirements.txt b/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/requirements.txt
new file mode 100644
index 00000000000..e60cdb323dd
--- /dev/null
+++ b/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/requirements.txt
@@ -0,0 +1,7 @@
+datasets>=2.6.1
+scikit-learn>=1.1.2
+scipy>=1.9.3
+sklearn==0.0
+tokenizers==0.13.1
+transformers>=4.31.0
+intel-tensorflow>=2.12.0
diff --git a/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/run_benchmark.sh b/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/run_benchmark.sh
new file mode 100644
index 00000000000..fed5b300182
--- /dev/null
+++ b/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/run_benchmark.sh
@@ -0,0 +1,88 @@
+#!/bin/bash
+# set -x
+
+function main {
+
+  init_params "$@"
+
+  run_benchmark
+
+}
+
+# init params
+function init_params {
+  # set default value
+  input_model="./distilbert_base_fp32.pb"
+  dataset_location="./sst2_validation_dataset"
+  mode="performance"
+  batch_size=128
+  max_seq_length=128
+  iters=872
+  warmup_steps=10
+  num_inter=2
+  num_intra=28
+  benchmark=True
+  profile=False
+
+  for var in "$@"
+  do
+    case $var in
+      --input_model=*)
+          input_model=$(echo "$var" |cut -f2 -d=)
+      ;;
+      --dataset_location=*)
+          dataset_location=$(echo "$var" |cut -f2 -d=)
+      ;;
+      --mode=*)
+          mode=$(echo $var |cut -f2 -d=)
+      ;;
+      --batch_size=*)
+          batch_size=$(echo $var |cut -f2 -d=)
+      ;;
+      --max_seq_length=*)
+          max_seq_length=$(echo ${var} |cut -f2 -d=)
+      ;;
+      --iters=*)
+          iters=$(echo ${var} |cut -f2 -d=)
+      ;;
+      --warmup_steps=*)
+          warmup_steps=$(echo ${var} |cut -f2 -d=)
+      ;;
+      --num_inter=*)
+         num_inter=$(echo ${var} |cut -f2 -d=)
+      ;;
+      --num_intra=*)
+         num_intra=$(echo ${var} |cut -f2 -d=)
+      ;;
+      --benchmark=*)
+         benchmark=$(echo ${var} |cut -f2 -d=)
+      ;;
+      --profile=*)
+         profile=$(echo ${var} |cut -f2 -d=)
+      ;;
+    esac
+  done
+
+}
+
+# run_benchmark
+function run_benchmark {
+    cmd="
+        python main.py \
+            --in-graph=${input_model} \
+            --data-location=${dataset_location} \
+            --benchmark=${benchmark} \
+            --profile=${profile} \
+            --mode=${mode} \
+            --steps=${iters} \
+            --warmup-steps=${warmup_steps} \
+            --batch-size=${batch_size} \
+            --max-seq-length=${max_seq_length} \
+            --num-inter-threads=${num_inter} \
+            --num-intra-threads=${num_intra}
+        "
+    echo $cmd
+    eval $cmd
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/run_quant.sh b/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/run_quant.sh
new file mode 100644
index 00000000000..31571c5ff4c
--- /dev/null
+++ b/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/run_quant.sh
@@ -0,0 +1,83 @@
+#!/bin/bash
+# set -x
+
+function main {
+
+  init_params "$@"
+
+  run_tuning
+
+}
+
+# init params
+function init_params {
+  # set default value
+  input_model="./distilbert_base_fp32.pb"
+  dataset_location="./sst2_validation_dataset"
+  output_model="./output_distilbert_base_int8.pb"
+  batch_size=128
+  max_seq_length=128
+  warmup_steps=10
+  num_inter=2
+  num_intra=28
+  tune=True
+  sq=False
+
+  for var in "$@"
+  do
+    case $var in
+      --input_model=*)
+          input_model=$(echo ${var} |cut -f2 -d=)
+      ;;
+      --dataset_location=*)
+          dataset_location=$(echo ${var} |cut -f2 -d=)
+      ;;
+      --output_model=*)
+          output_model=$(echo ${var} |cut -f2 -d=)
+      ;;
+      --batch_size=*)
+          batch_size=$(echo ${var} |cut -f2 -d=)
+      ;;
+      --max_seq_length=*)
+          max_seq_length=$(echo ${var} |cut -f2 -d=)
+      ;;
+      --warmup_steps=*)
+          warmup_steps=$(echo ${var} |cut -f2 -d=)
+      ;;
+      --num_inter=*)
+         num_inter=$(echo ${var} |cut -f2 -d=)
+      ;;
+      --num_intra=*)
+         num_intra=$(echo ${var} |cut -f2 -d=)
+      ;;
+      --tune=*)
+         tune=$(echo ${var} |cut -f2 -d=)
+      ;;
+      --sq=*)
+         sq=$(echo ${var} |cut -f2 -d=)
+      ;;
+    esac
+  done
+
+}
+
+# run_tuning
+function run_tuning {
+    cmd="
+        python main.py \
+            --in-graph=${input_model} \
+            --data-location=${dataset_location} \
+            --output-graph=${output_model} \
+            --tune=${tune} \
+            --sq=${sq} \
+            --warmup-steps=${warmup_steps} \
+            --batch-size=${batch_size} \
+            --max-seq-length=${max_seq_length} \
+            --num-inter-threads=${num_inter} \
+            --num-intra-threads=${num_intra}
+        "
+    echo $cmd
+    eval $cmd
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/README.md b/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/README.md
new file mode 100644
index 00000000000..9aad1dda2a9
--- /dev/null
+++ b/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/README.md
@@ -0,0 +1,132 @@
+Step-by-Step
+============
+
+This document is used to list steps of reproducing TensorFlow Intel® Neural Compressor tuning zoo result of Transformer-LT. This example can run on Intel CPUs and GPUs.
+
+## Prerequisite
+
+### 1. Installation
+```shell
+# Install Intel® Neural Compressor
+pip install neural-compressor
+```
+
+### 2. Install Tensorflow
+```shell
+pip install tensorflow
+```
+> Note: Validated TensorFlow [Version](/docs/source/installation_guide.md#validated-software-environment).
+
+### 3. Install Intel Extension for Tensorflow
+
+#### Quantizing the model on Intel GPU(Mandatory to install ITEX)
+Intel Extension for Tensorflow is mandatory to be installed for quantizing the model on Intel GPUs.
+
+```shell
+pip install --upgrade intel-extension-for-tensorflow[xpu]
+```
+Please refer to the [Installation Guides](https://dgpu-docs.intel.com/installation-guides/ubuntu/ubuntu-focal-dc.html) for latest Intel GPU driver installation.
+For any more details, please follow the procedure in [install-gpu-drivers](https://github.com/intel/intel-extension-for-tensorflow/blob/main/docs/install/install_for_xpu.md#install-gpu-drivers).
+
+#### Quantizing the model on Intel CPU(Optional to install ITEX)
+Intel Extension for Tensorflow for Intel CPUs is experimental currently. It's not mandatory for quantizing the model on Intel CPUs.
+
+```shell
+pip install --upgrade intel-extension-for-tensorflow[cpu]
+```
+
+> **Note**: 
+> The version compatibility of stock Tensorflow and ITEX can be checked [here](https://github.com/intel/intel-extension-for-tensorflow#compatibility-table). Please make sure you have installed compatible Tensorflow and ITEX.
+
+### 4. Prepare Dataset & Pretrained model
+
+```shell
+wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v2_2_0/transformer-lt-official-fp32-inference.tar.gz
+tar -zxvf transformer-lt-official-fp32-inference.tar.gz
+cd transformer-lt-official-fp32-inference
+tar -zxvf transformer_lt_official_fp32_pretrained_model.tar.gz
+```
+
+Dataset is in data folder, pretrained model is in graph folder.
+
+#### Automatic dataset & model download
+Run the `prepare_dataset_model.sh` script located in `examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq`.
+
+```shell
+cd examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq
+bash prepare_dataset_model.sh
+```
+
+## Run Command
+
+```shell
+python main.py --input_graph=/path/to/fp32_graphdef.pb --inputs_file=/path/to/newstest2014.en --reference_file=/path/to/newstest2014.de --vocab_file=/path/to/vocab.txt --tune
+```
+
+Details of enabling Intel® Neural Compressor on transformer-lt for Tensorflow.
+=========================
+
+This is a tutorial of how to enable transformer-lt model with Intel® Neural Compressor.
+## User Code Analysis
+1. User specifies fp32 *model*, calibration dataset *q_dataloader*, evaluation dataset *eval_dataloader* and metric in tuning.metric field of model-specific yaml config file.
+
+2. User specifies fp32 *model*, calibration dataset *q_dataloader* and a custom *eval_func* which encapsulates the evaluation dataset and metric by itself.
+
+For transformer-lt, we applied the latter one because we don't have dataset and metric for transformer-lt. The task is to implement the *q_dataloader* and *eval_func*.
+
+
+### q_dataloader Part Adaption
+Below dataset class uses getitem to provide the model with input.
+
+```python
+class Dataset(object):
+    def __init__(self, *args):
+        # initialize dataset related info here
+        ...
+
+    def __getitem__(self, index):
+        data = self.batch[index]
+        label = self.ref_lines[index]
+        return data[0], label
+
+    def __len__(self):
+        return len(self.batch)
+```
+
+### Evaluation Part Adaption
+We evaluate the model with BLEU score, its source: https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/bleu_hook.py
+
+Here we set the input tensor and output tensors name into *inputs* and *outputs* args.
+In this case we calibrate and quantize the model, and use our calibration dataloader initialized from a 'Dataset' object.
+
+### Code update
+After prepare step is done, we add tune code to generate quantized model.
+
+#### Tune
+```python
+    from neural_compressor.tensorflow import StaticQuantConfig, quantize_model, Model
+
+    dataset = Dataset(FLAGS.inputs_file, FLAGS.reference_file, FLAGS.vocab_file)
+    calib_dataloader = BaseDataLoader(dataset=dataset, batch_size=FLAGS.batch_size, collate_fn=collate_fn)
+
+    quant_config = StaticQuantConfig()
+    model = Model(graph)
+    model.input_tensor_names = ['input_tensor']
+    model.output_tensor_names = ['model/Transformer/strided_slice_19']
+    q_model = quantize_model(model, quant_config, calib_dataloader)
+    try:
+        q_model.save(FLAGS.output_model)
+    except Exception as e:
+        print("Failed to save model due to {}".format(str(e)))
+```
+#### Benchmark
+```python
+    if FLAGS.benchmark:
+        assert FLAGS.mode == 'performance' or FLAGS.mode == 'accuracy', \
+        "Benchmark only supports performance or accuracy mode."
+        eval_func(graph)
+    elif FLAGS.mode == 'accuracy':
+        acc = eval_func(graph)
+        print('Accuracy is {:.3f}'.format(acc))
+```
+The Intel® Neural Compressor quantization.fit() function will return a best quantized model under time constraint.
diff --git a/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/main.py b/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/main.py
new file mode 100644
index 00000000000..d4d1ff54055
--- /dev/null
+++ b/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/main.py
@@ -0,0 +1,261 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2023 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+#
+import re
+import six
+import sys
+import time
+import numpy as np
+import unicodedata
+import pandas as pd
+from absl import app
+import tensorflow as tf
+from argparse import ArgumentParser
+
+from utils import metrics
+from utils import tokenizer
+from utils.tokenizer import Subtokenizer
+from neural_compressor.tensorflow.utils import BaseDataLoader
+
+flags = tf.compat.v1.flags
+FLAGS = flags.FLAGS
+
+flags.DEFINE_integer("batch_size", 64,
+                     "run batch size")
+
+flags.DEFINE_string("input_graph", None,
+                    "The path of input model file.")
+
+flags.DEFINE_string("inputs_file", None,
+                    "File saved to an output file.")
+
+flags.DEFINE_string("reference_file", None,
+                    "File containing reference translation.")
+
+flags.DEFINE_string("vocab_file", None,
+                    "Path to subtoken vocabulary file.")
+
+flags.DEFINE_string("output_model", None,
+                    "The output model of the quantized model.")
+
+flags.DEFINE_bool('tune', False,
+                    'whether to tune the model')
+
+flags.DEFINE_bool('benchmark', False, 
+                    'whether to benchmark the model')
+
+flags.DEFINE_string("mode", 'performance',
+                     "One of three options: 'performance'/'accuracy'.")
+
+flags.DEFINE_integer("iters", 100,
+                     "The iteration used for benchmark.")
+
+class UnicodeRegex(object):
+    def __init__(self):
+        punctuation = self.property_chars("P")
+        self.nondigit_punct_re = re.compile(r"([^\d])([" + punctuation + r"])")
+        self.punct_nondigit_re = re.compile(r"([" + punctuation + r"])([^\d])")
+        self.symbol_re = re.compile("([" + self.property_chars("S") + "])")
+
+    def property_chars(self, prefix):
+        return "".join(six.unichr(x) for x in range(sys.maxunicode)
+                    if unicodedata.category(six.unichr(x)).startswith(prefix))
+
+uregex = UnicodeRegex()
+
+def bleu_tokenize(string):
+    string = uregex.nondigit_punct_re.sub(r"\1 \2 ", string)
+    string = uregex.punct_nondigit_re.sub(r" \1 \2", string)
+    string = uregex.symbol_re.sub(r" \1 ", string)
+    return string.split()
+
+class bleu(object):
+    def __init__(self):
+        self.translations = []
+        self.labels = []
+
+    def reset(self):
+        self.translations = []
+        self.labels = []
+
+    def update(self, pred, label):
+        if len(label) != len(pred):
+            raise ValueError("Reference and translation files have different number "
+                             "of lines. If training only a few steps (100-200), the "
+                             "translation may be empty.")
+        label = [x.lower() for x in label]
+        pred = [x.lower() for x in pred]
+        label = [bleu_tokenize(x) for x in label]
+        pred = [bleu_tokenize(x) for x in pred]
+        self.labels.extend(label)
+        self.translations.extend(pred)
+
+    def result(self):
+        return metrics.compute_bleu(self.labels, self.translations) * 100
+
+def collate_fn(batch):
+    """Puts each data field into a pd frame with outer dimension batch size"""
+    elem = batch[0]
+    if isinstance(elem, tuple):
+        batch = zip(*batch)
+        return [collate_fn(samples) for samples in batch]
+    elif isinstance(elem, np.ndarray):
+        return [list(elem) for elem in batch]
+    elif isinstance(elem, str):
+        return batch
+    else:
+        return pd.DataFrame(batch).fillna(0).values.astype(np.int32)
+
+def load_graph(file_name):
+    tf.compat.v1.logging.info('Loading graph from: ' + file_name)
+    with tf.io.gfile.GFile(file_name, "rb") as f:
+        graph_def = tf.compat.v1.GraphDef()
+        graph_def.ParseFromString(f.read())
+    with tf.Graph().as_default() as graph:
+        tf.import_graph_def(graph_def, name='')
+    return graph
+
+def eval_func(infer_graph, iteration=-1):
+    if isinstance(infer_graph, tf.compat.v1.GraphDef):
+        graph = tf.Graph()
+        with graph.as_default():
+            tf.import_graph_def(infer_graph, name='')
+        infer_graph = graph
+
+    subtokenizer = Subtokenizer(FLAGS.vocab_file)
+    input_tensor = infer_graph.get_tensor_by_name('input_tensor:0')
+    output_tensor = infer_graph.get_tensor_by_name(\
+        'model/Transformer/strided_slice_19:0')
+
+    ds = Dataset(FLAGS.inputs_file, FLAGS.reference_file, FLAGS.vocab_file)
+    dataloader = DataLoader(framework='tensorflow', dataset=ds,
+                                batch_size=FLAGS.batch_size, collate_fn=collate_fn)
+
+    config = tf.compat.v1.ConfigProto()
+    config.use_per_session_threads = 1
+    config.inter_op_parallelism_threads = 1
+    sess = tf.compat.v1.Session(graph=infer_graph, config=config)
+    iteration=-1
+    time_list = []
+    bleu_eval = bleu()
+    predictions = []
+    labels = []
+    warmup = 10
+    if FLAGS.benchmark and FLAGS.mode == 'performance':
+       iteration = FLAGS.iters
+       assert iteration >= warmup, 'iteration must be larger than warmup'
+
+    for idx, (input_data, label) in enumerate(dataloader):
+        if idx < iteration or iteration == -1:
+            time_start = time.time()
+            out = sess.run([output_tensor], {input_tensor: input_data})
+            duration = time.time() - time_start
+            time_list.append(duration)
+            predictions.append(out)
+            labels.extend(label)
+        else:
+            break
+
+    latency = np.array(time_list[warmup: ]).mean() / FLAGS.batch_size
+    if FLAGS.benchmark and FLAGS.mode == 'performance':
+        print('Batch size = {}'.format(FLAGS.batch_size))
+        print('Latency: {:.3f} ms'.format(latency * 1000))
+        print('Throughput: {:.3f} items/sec'.format(1./ latency))
+
+    # only calculate accuracy when running out all predictions
+    if iteration == -1:
+        decode = []
+        for i,tr in enumerate(predictions):
+            for j,itr in enumerate(tr):
+                for k, otr in enumerate(itr):
+                    try:
+                        index = list(otr).index(tokenizer.EOS_ID)
+                        decode.append(subtokenizer.decode(otr[:index]))
+                    except:
+                        decode.append(subtokenizer.decode(otr))
+        bleu_eval.update(decode, labels)
+        return bleu_eval.result()
+
+class Dataset(object):
+    def __init__(self, inputs_file, reference_file, vocab_file):
+        with tf.io.gfile.GFile(inputs_file) as f:
+            records = f.read().split("\n")
+            inputs = [record.strip() for record in records]
+            if not inputs[-1]:
+                inputs.pop()
+
+        self.ref_lines = tokenizer.native_to_unicode(
+            tf.io.gfile.GFile(reference_file).read()).strip().splitlines()
+
+        subtokenizer = Subtokenizer(vocab_file)
+        self.batch = []
+        token_lens=[]
+        for i, line in enumerate(inputs):
+            enc = subtokenizer.encode(line, add_eos=True)
+            token_lens.append((i, len(enc)))
+
+        sorted_by_token_input_lens = sorted(token_lens, key=lambda x: x[1], reverse=True)
+
+        sorted_inputs = [None] * len(sorted_by_token_input_lens)
+        sorted_keys = [0] * len(sorted_by_token_input_lens)
+
+        lines = []
+        for i, (index, _) in enumerate(sorted_by_token_input_lens):
+            sorted_inputs[i] = inputs[index]
+            sorted_keys[index] = i
+            enc=subtokenizer.encode(sorted_inputs[i], add_eos=True)
+            lines.append([enc])
+        for i in sorted_keys:
+            self.batch.append(lines[i])
+
+    def __getitem__(self, index):
+        data = self.batch[index]
+        label = self.ref_lines[index]
+        return data[0], label
+
+    def __len__(self):
+        return len(self.batch)
+
+def main(_):
+    graph = load_graph(FLAGS.input_graph)
+    if FLAGS.tune:
+        from neural_compressor.tensorflow import StaticQuantConfig, quantize_model, Model
+
+        dataset = Dataset(FLAGS.inputs_file, FLAGS.reference_file, FLAGS.vocab_file)
+        calib_dataloader = BaseDataLoader(dataset=dataset, batch_size=FLAGS.batch_size, collate_fn=collate_fn)
+
+        quant_config = StaticQuantConfig()
+        model = Model(graph)
+        model.input_tensor_names = ['input_tensor']
+        model.output_tensor_names = ['model/Transformer/strided_slice_19']
+        q_model = quantize_model(model, quant_config, calib_dataloader)
+        try:
+            q_model.save(FLAGS.output_model)
+        except Exception as e:
+            print("Failed to save model due to {}".format(str(e)))
+
+    if FLAGS.benchmark:
+        assert FLAGS.mode == 'performance' or FLAGS.mode == 'accuracy', \
+        "Benchmark only supports performance or accuracy mode."
+        eval_func(graph)
+    elif FLAGS.mode == 'accuracy':
+        acc = eval_func(graph)
+        print('Accuracy is {:.3f}'.format(acc))
+
+if __name__ == "__main__":
+    tf.compat.v1.app.run()
diff --git a/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/prepare_dataset_model.sh b/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/prepare_dataset_model.sh
new file mode 100644
index 00000000000..3d47dbad80c
--- /dev/null
+++ b/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/prepare_dataset_model.sh
@@ -0,0 +1,62 @@
+#!/bin/bash
+# set -x
+
+DATA_DIR="../data"
+MODEL_DIR="../model"
+
+help()
+{
+    cat <<- EOF
+    Desc: Prepare bert dataset
+    -h --help                   help info
+    --data_dir                  Output data directory
+                                default: './data'
+    --model_dir                 Output model directory
+                                default: './model'
+EOF
+    exit 0
+}
+
+function main {
+    init_params "$@"
+    prepare
+}
+
+# init params
+function init_params {
+    for var in "$@"
+    do
+        case $var in
+            --data_dir=*)
+                DATA_DIR=$(echo $var |cut -f2 -d=)
+            ;;
+            --model_dir=*)
+                MODEL_DIR=$(echo $var |cut -f2 -d=)
+            ;;
+            -h|--help) help
+            ;;
+            *)
+            echo "Error: No such parameter: ${var}"
+            exit 1
+            ;;
+        esac
+    done
+}
+
+# prepare data and model
+function prepare {
+    if [ ! -d ${DATA_DIR} ]; then
+        echo '${DATA_DIR} already exists, please check...'
+    fi
+    if [ ! -d ${MODEL_DIR} ]; then
+        echo '${MODEL_DIR} already exists, please check...'
+    fi
+    wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v2_2_0/transformer-lt-official-fp32-inference.tar.gz
+    tar -zxvf transformer-lt-official-fp32-inference.tar.gz
+    cd transformer-lt-official-fp32-inference
+    tar -zxvf transformer_lt_official_fp32_pretrained_model.tar.gz
+    mv transformer_lt_official_fp32_pretrained_model/data ${DATA_DIR}
+    mv transformer_lt_official_fp32_pretrained_model/graph ${MODEL_DIR}
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/run_benchmark.sh b/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/run_benchmark.sh
new file mode 100644
index 00000000000..87bc4c7d5c1
--- /dev/null
+++ b/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/run_benchmark.sh
@@ -0,0 +1,64 @@
+#!/bin/bash
+# set -x
+
+function main {
+
+  init_params "$@"
+
+  run_benchmark
+
+}
+
+# init params
+function init_params {
+  iters=100
+  for var in "$@"
+  do
+    case $var in
+      --dataset_location=*)
+          dataset_location=$(echo "$var" |cut -f2 -d=)
+      ;;
+      --input_model=*)
+          input_model=$(echo "$var" |cut -f2 -d=)
+      ;;
+      --mode=*)
+          mode=$(echo $var |cut -f2 -d=)
+      ;;
+      --batch_size=*)
+          batch_size=$(echo $var |cut -f2 -d=)
+      ;;
+      --iters=*)
+          iters=$(echo ${var} |cut -f2 -d=)
+      ;;
+    esac
+  done
+
+}
+
+function define_mode {
+    
+    if [[ ${mode} == "accuracy" ]]; then
+        mode="accuracy"
+    elif [[ ${mode} == "performance" ]]; then
+        mode="performance"
+    else
+        echo "Error: No such mode: ${mode}"
+        exit 1
+    fi
+}
+        
+# run_benchmark
+function run_benchmark {
+    python main.py \
+      --input_graph=${input_model} \
+      --inputs_file=${dataset_location}/newstest2014.en \
+      --reference_file=${dataset_location}/newstest2014.de \
+      --vocab_file=${dataset_location}/vocab.txt \
+      --benchmark \
+      --mode=${mode} \
+      --iters=${iters} \
+      --batch_size=${batch_size} 
+}
+
+main "$@"
+
diff --git a/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/run_quant.sh b/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/run_quant.sh
new file mode 100644
index 00000000000..2f2075cf346
--- /dev/null
+++ b/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/run_quant.sh
@@ -0,0 +1,43 @@
+#!/bin/bash
+# set -x
+
+function main {
+
+  init_params "$@"
+
+  run_tuning
+
+}
+
+# init params
+function init_params {
+  for var in "$@"
+  do
+    case $var in
+      --dataset_location=*)
+          dataset_location=$(echo "$var" |cut -f2 -d=)
+      ;;
+      --input_model=*)
+          input_model=$(echo "$var" |cut -f2 -d=)
+      ;;
+      --output_model=*)
+          output_model=$(echo "$var" |cut -f2 -d=)
+      ;;
+    esac
+  done
+
+}
+
+# run_tuning
+function run_tuning {
+    python main.py \
+      --input_graph=${input_model} \
+      --inputs_file=${dataset_location}/newstest2014.en \
+      --reference_file=${dataset_location}/newstest2014.de \
+      --vocab_file=${dataset_location}/vocab.txt \
+      --output_model=${output_model} \
+      --tune
+}
+
+main "$@"
+
diff --git a/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/utils/__init__.py b/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/utils/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/utils/metrics.py b/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/utils/metrics.py
new file mode 100644
index 00000000000..3e41f985c63
--- /dev/null
+++ b/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/utils/metrics.py
@@ -0,0 +1,490 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the 'License');
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an 'AS IS' BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Functions for calculating loss, accuracy, and other model metrics.
+
+Metrics:
+ - Padded loss, accuracy, and negative log perplexity. Source:
+     https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/metrics.py
+ - BLEU approximation. Source:
+     https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/bleu_hook.py
+ - ROUGE score. Source:
+     https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/rouge.py
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import math
+
+import numpy as np
+import six
+from six.moves import xrange  # pylint: disable=redefined-builtin
+import tensorflow as tf
+
+
+def _pad_tensors_to_same_length(x, y):
+  """Pad x and y so that the results have the same length (second dimension)."""
+  with tf.name_scope("pad_to_same_length"):
+    x_length = tf.shape(x)[1]
+    y_length = tf.shape(y)[1]
+
+    max_length = tf.maximum(x_length, y_length)
+
+    x = tf.pad(x, [[0, 0], [0, max_length - x_length], [0, 0]])
+    y = tf.pad(y, [[0, 0], [0, max_length - y_length]])
+    return x, y
+
+
+def padded_cross_entropy_loss(logits, labels, smoothing, vocab_size):
+  """Calculate cross entropy loss while ignoring padding.
+
+  Args:
+    logits: Tensor of size [batch_size, length_logits, vocab_size]
+    labels: Tensor of size [batch_size, length_labels]
+    smoothing: Label smoothing constant, used to determine the on and off values
+    vocab_size: int size of the vocabulary
+  Returns:
+    Returns the cross entropy loss and weight tensors: float32 tensors with
+      shape [batch_size, max(length_logits, length_labels)]
+  """
+  with tf.name_scope("loss", values=[logits, labels]):
+    logits, labels = _pad_tensors_to_same_length(logits, labels)
+
+    # Calculate smoothing cross entropy
+    with tf.name_scope("smoothing_cross_entropy", values=[logits, labels]):
+      confidence = 1.0 - smoothing
+      low_confidence = (1.0 - confidence) / tf.to_float(vocab_size - 1)
+      soft_targets = tf.one_hot(
+          tf.cast(labels, tf.int32),
+          depth=vocab_size,
+          on_value=confidence,
+          off_value=low_confidence)
+      xentropy = tf.nn.softmax_cross_entropy_with_logits_v2(
+          logits=logits, labels=soft_targets)
+
+      # Calculate the best (lowest) possible value of cross entropy, and
+      # subtract from the cross entropy loss.
+      normalizing_constant = -(
+          confidence * tf.log(confidence) + tf.to_float(vocab_size - 1) *
+          low_confidence * tf.log(low_confidence + 1e-20))
+      xentropy -= normalizing_constant
+
+    weights = tf.to_float(tf.not_equal(labels, 0))
+    return xentropy * weights, weights
+
+
+def _convert_to_eval_metric(metric_fn):
+  """Wrap a metric fn that returns scores and weights as an eval metric fn.
+
+  The input metric_fn returns values for the current batch. The wrapper
+  aggregates the return values collected over all of the batches evaluated.
+
+  Args:
+    metric_fn: function that returns scores and weights for the current batch's
+      logits and predicted labels.
+
+  Returns:
+    function that aggregates the scores and weights from metric_fn.
+  """
+  def problem_metric_fn(*args):
+    """Returns an aggregation of the metric_fn's returned values."""
+    (scores, weights) = metric_fn(*args)
+
+    # The tf.metrics.mean function assures correct aggregation.
+    return tf.metrics.mean(scores, weights)
+  return problem_metric_fn
+
+
+def get_eval_metrics(logits, labels, params):
+  """Return dictionary of model evaluation metrics."""
+  metrics = {
+      "accuracy": _convert_to_eval_metric(padded_accuracy)(logits, labels),
+      "accuracy_top5": _convert_to_eval_metric(padded_accuracy_top5)(
+          logits, labels),
+      "accuracy_per_sequence": _convert_to_eval_metric(
+          padded_sequence_accuracy)(logits, labels),
+      "neg_log_perplexity": _convert_to_eval_metric(padded_neg_log_perplexity)(
+          logits, labels, params["vocab_size"]),
+  }
+
+  if not params["use_tpu"]:
+    # TPU does not support tf.py_func
+    metrics.update({
+        "approx_bleu_score": _convert_to_eval_metric(
+            bleu_score)(logits, labels),
+        "rouge_2_fscore": _convert_to_eval_metric(
+            rouge_2_fscore)(logits, labels),
+        "rouge_L_fscore": _convert_to_eval_metric(
+            rouge_l_fscore)(logits, labels),
+    })
+
+  # Prefix each of the metric names with "metrics/". This allows the metric
+  # graphs to display under the "metrics" category in TensorBoard.
+  metrics = {"metrics/%s" % k: v for k, v in six.iteritems(metrics)}
+  return metrics
+
+
+def padded_accuracy(logits, labels):
+  """Percentage of times that predictions matches labels on non-0s."""
+  with tf.variable_scope("padded_accuracy", values=[logits, labels]):
+    logits, labels = _pad_tensors_to_same_length(logits, labels)
+    weights = tf.to_float(tf.not_equal(labels, 0))
+    outputs = tf.to_int32(tf.argmax(logits, axis=-1))
+    padded_labels = tf.to_int32(labels)
+    return tf.to_float(tf.equal(outputs, padded_labels)), weights
+
+
+def padded_accuracy_topk(logits, labels, k):
+  """Percentage of times that top-k predictions matches labels on non-0s."""
+  with tf.variable_scope("padded_accuracy_topk", values=[logits, labels]):
+    logits, labels = _pad_tensors_to_same_length(logits, labels)
+    weights = tf.to_float(tf.not_equal(labels, 0))
+    effective_k = tf.minimum(k, tf.shape(logits)[-1])
+    _, outputs = tf.nn.top_k(logits, k=effective_k)
+    outputs = tf.to_int32(outputs)
+    padded_labels = tf.to_int32(labels)
+    padded_labels = tf.expand_dims(padded_labels, axis=-1)
+    padded_labels += tf.zeros_like(outputs)  # Pad to same shape.
+    same = tf.to_float(tf.equal(outputs, padded_labels))
+    same_topk = tf.reduce_sum(same, axis=-1)
+    return same_topk, weights
+
+
+def padded_accuracy_top5(logits, labels):
+  return padded_accuracy_topk(logits, labels, 5)
+
+
+def padded_sequence_accuracy(logits, labels):
+  """Percentage of times that predictions matches labels everywhere (non-0)."""
+  with tf.variable_scope("padded_sequence_accuracy", values=[logits, labels]):
+    logits, labels = _pad_tensors_to_same_length(logits, labels)
+    weights = tf.to_float(tf.not_equal(labels, 0))
+    outputs = tf.to_int32(tf.argmax(logits, axis=-1))
+    padded_labels = tf.to_int32(labels)
+    not_correct = tf.to_float(tf.not_equal(outputs, padded_labels)) * weights
+    axis = list(range(1, len(outputs.get_shape())))
+    correct_seq = 1.0 - tf.minimum(1.0, tf.reduce_sum(not_correct, axis=axis))
+    return correct_seq, tf.constant(1.0)
+
+
+def padded_neg_log_perplexity(logits, labels, vocab_size):
+  """Average log-perplexity excluding padding 0s. No smoothing."""
+  num, den = padded_cross_entropy_loss(logits, labels, 0, vocab_size)
+  return -num, den
+
+
+def bleu_score(logits, labels):
+  """Approximate BLEU score computation between labels and predictions.
+
+  An approximate BLEU scoring method since we do not glue word pieces or
+  decode the ids and tokenize the output. By default, we use ngram order of 4
+  and use brevity penalty. Also, this does not have beam search.
+
+  Args:
+    logits: Tensor of size [batch_size, length_logits, vocab_size]
+    labels: Tensor of size [batch-size, length_labels]
+
+  Returns:
+    bleu: int, approx bleu score
+  """
+  predictions = tf.to_int32(tf.argmax(logits, axis=-1))
+  # TODO: Look into removing use of py_func
+  bleu = tf.py_func(compute_bleu, (labels, predictions), tf.float32)
+  return bleu, tf.constant(1.0)
+
+
+def _get_ngrams_with_counter(segment, max_order):
+  """Extracts all n-grams up to a given maximum order from an input segment.
+
+  Args:
+    segment: text segment from which n-grams will be extracted.
+    max_order: maximum length in tokens of the n-grams returned by this
+        methods.
+
+  Returns:
+    The Counter containing all n-grams upto max_order in segment
+    with a count of how many times each n-gram occurred.
+  """
+  ngram_counts = collections.Counter()
+  for order in xrange(1, max_order + 1):
+    for i in xrange(0, len(segment) - order + 1):
+      ngram = tuple(segment[i:i + order])
+      ngram_counts[ngram] += 1
+  return ngram_counts
+
+
+def compute_bleu(reference_corpus, translation_corpus, max_order=4,
+                 use_bp=True):
+  """Computes BLEU score of translated segments against one or more references.
+
+  Args:
+    reference_corpus: list of references for each translation. Each
+        reference should be tokenized into a list of tokens.
+    translation_corpus: list of translations to score. Each translation
+        should be tokenized into a list of tokens.
+    max_order: Maximum n-gram order to use when computing BLEU score.
+    use_bp: boolean, whether to apply brevity penalty.
+
+  Returns:
+    BLEU score.
+  """
+  reference_length = 0
+  translation_length = 0
+  bp = 1.0
+  geo_mean = 0
+
+  matches_by_order = [0] * max_order
+  possible_matches_by_order = [0] * max_order
+  precisions = []
+
+  for (references, translations) in zip(reference_corpus, translation_corpus):
+    reference_length += len(references)
+    translation_length += len(translations)
+    ref_ngram_counts = _get_ngrams_with_counter(references, max_order)
+    translation_ngram_counts = _get_ngrams_with_counter(translations, max_order)
+
+    overlap = dict((ngram,
+                    min(count, translation_ngram_counts[ngram]))
+                   for ngram, count in ref_ngram_counts.items())
+
+    for ngram in overlap:
+      matches_by_order[len(ngram) - 1] += overlap[ngram]
+    for ngram in translation_ngram_counts:
+      possible_matches_by_order[len(ngram) - 1] += translation_ngram_counts[
+          ngram]
+
+  precisions = [0] * max_order
+  smooth = 1.0
+
+  for i in xrange(0, max_order):
+    if possible_matches_by_order[i] > 0:
+      precisions[i] = float(matches_by_order[i]) / possible_matches_by_order[i]
+      if matches_by_order[i] > 0:
+        precisions[i] = float(matches_by_order[i]) / possible_matches_by_order[
+            i]
+      else:
+        smooth *= 2
+        precisions[i] = 1.0 / (smooth * possible_matches_by_order[i])
+    else:
+      precisions[i] = 0.0
+
+  if max(precisions) > 0:
+    p_log_sum = sum(math.log(p) for p in precisions if p)
+    geo_mean = math.exp(p_log_sum / max_order)
+
+  if use_bp:
+    ratio = translation_length / reference_length
+    bp = math.exp(1 - 1. / ratio) if ratio < 1.0 else 1.0
+  bleu = geo_mean * bp
+  return np.float32(bleu)
+
+
+def rouge_2_fscore(logits, labels):
+  """ROUGE-2 F1 score computation between labels and predictions.
+
+  This is an approximate ROUGE scoring method since we do not glue word pieces
+  or decode the ids and tokenize the output.
+
+  Args:
+    logits: tensor, model predictions
+    labels: tensor, gold output.
+
+  Returns:
+    rouge2_fscore: approx rouge-2 f1 score.
+  """
+  predictions = tf.to_int32(tf.argmax(logits, axis=-1))
+  # TODO: Look into removing use of py_func
+  rouge_2_f_score = tf.py_func(rouge_n, (predictions, labels), tf.float32)
+  return rouge_2_f_score, tf.constant(1.0)
+
+
+def _get_ngrams(n, text):
+  """Calculates n-grams.
+
+  Args:
+    n: which n-grams to calculate
+    text: An array of tokens
+
+  Returns:
+    A set of n-grams
+  """
+  ngram_set = set()
+  text_length = len(text)
+  max_index_ngram_start = text_length - n
+  for i in range(max_index_ngram_start + 1):
+    ngram_set.add(tuple(text[i:i + n]))
+  return ngram_set
+
+
+def rouge_n(eval_sentences, ref_sentences, n=2):
+  """Computes ROUGE-N f1 score of two text collections of sentences.
+
+  Source: https://www.microsoft.com/en-us/research/publication/
+  rouge-a-package-for-automatic-evaluation-of-summaries/
+
+  Args:
+    eval_sentences: Predicted sentences.
+    ref_sentences: Sentences from the reference set
+    n: Size of ngram.  Defaults to 2.
+
+  Returns:
+    f1 score for ROUGE-N
+  """
+  f1_scores = []
+  for eval_sentence, ref_sentence in zip(eval_sentences, ref_sentences):
+    eval_ngrams = _get_ngrams(n, eval_sentence)
+    ref_ngrams = _get_ngrams(n, ref_sentence)
+    ref_count = len(ref_ngrams)
+    eval_count = len(eval_ngrams)
+
+    # Count the overlapping ngrams between evaluated and reference
+    overlapping_ngrams = eval_ngrams.intersection(ref_ngrams)
+    overlapping_count = len(overlapping_ngrams)
+
+    # Handle edge case. This isn't mathematically correct, but it's good enough
+    if eval_count == 0:
+      precision = 0.0
+    else:
+      precision = float(overlapping_count) / eval_count
+    if ref_count == 0:
+      recall = 0.0
+    else:
+      recall = float(overlapping_count) / ref_count
+    f1_scores.append(2.0 * ((precision * recall) / (precision + recall + 1e-8)))
+
+  # return overlapping_count / reference_count
+  return np.mean(f1_scores, dtype=np.float32)
+
+
+def rouge_l_fscore(predictions, labels):
+  """ROUGE scores computation between labels and predictions.
+
+  This is an approximate ROUGE scoring method since we do not glue word pieces
+  or decode the ids and tokenize the output.
+
+  Args:
+    predictions: tensor, model predictions
+    labels: tensor, gold output.
+
+  Returns:
+    rouge_l_fscore: approx rouge-l f1 score.
+  """
+  outputs = tf.to_int32(tf.argmax(predictions, axis=-1))
+  rouge_l_f_score = tf.py_func(rouge_l_sentence_level, (outputs, labels),
+                               tf.float32)
+  return rouge_l_f_score, tf.constant(1.0)
+
+
+def rouge_l_sentence_level(eval_sentences, ref_sentences):
+  """Computes ROUGE-L (sentence level) of two collections of sentences.
+
+  Source: https://www.microsoft.com/en-us/research/publication/
+  rouge-a-package-for-automatic-evaluation-of-summaries/
+
+  Calculated according to:
+  R_lcs = LCS(X,Y)/m
+  P_lcs = LCS(X,Y)/n
+  F_lcs = ((1 + beta^2)*R_lcs*P_lcs) / (R_lcs + (beta^2) * P_lcs)
+
+  where:
+  X = reference summary
+  Y = Candidate summary
+  m = length of reference summary
+  n = length of candidate summary
+
+  Args:
+    eval_sentences: The sentences that have been picked by the summarizer
+    ref_sentences: The sentences from the reference set
+
+  Returns:
+    A float: F_lcs
+  """
+
+  f1_scores = []
+  for eval_sentence, ref_sentence in zip(eval_sentences, ref_sentences):
+    m = float(len(ref_sentence))
+    n = float(len(eval_sentence))
+    lcs = _len_lcs(eval_sentence, ref_sentence)
+    f1_scores.append(_f_lcs(lcs, m, n))
+  return np.mean(f1_scores, dtype=np.float32)
+
+
+def _len_lcs(x, y):
+  """Returns the length of the Longest Common Subsequence between two seqs.
+
+  Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence
+
+  Args:
+    x: sequence of words
+    y: sequence of words
+
+  Returns
+    integer: Length of LCS between x and y
+  """
+  table = _lcs(x, y)
+  n, m = len(x), len(y)
+  return table[n, m]
+
+
+def _lcs(x, y):
+  """Computes the length of the LCS between two seqs.
+
+  The implementation below uses a DP programming algorithm and runs
+  in O(nm) time where n = len(x) and m = len(y).
+  Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence
+
+  Args:
+    x: collection of words
+    y: collection of words
+
+  Returns:
+    Table of dictionary of coord and len lcs
+  """
+  n, m = len(x), len(y)
+  table = dict()
+  for i in range(n + 1):
+    for j in range(m + 1):
+      if i == 0 or j == 0:
+        table[i, j] = 0
+      elif x[i - 1] == y[j - 1]:
+        table[i, j] = table[i - 1, j - 1] + 1
+      else:
+        table[i, j] = max(table[i - 1, j], table[i, j - 1])
+  return table
+
+
+def _f_lcs(llcs, m, n):
+  """Computes the LCS-based F-measure score.
+
+  Source: http://research.microsoft.com/en-us/um/people/cyl/download/papers/
+  rouge-working-note-v1.3.1.pdf
+
+  Args:
+    llcs: Length of LCS
+    m: number of words in reference summary
+    n: number of words in candidate summary
+
+  Returns:
+    Float. LCS-based F-measure score
+  """
+  r_lcs = llcs / m
+  p_lcs = llcs / n
+  beta = p_lcs / (r_lcs + 1e-12)
+  num = (1 + (beta ** 2)) * r_lcs * p_lcs
+  denom = r_lcs + ((beta ** 2) * p_lcs)
+  f_lcs = num / (denom + 1e-12)
+  return f_lcs
diff --git a/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/utils/tokenizer.py b/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/utils/tokenizer.py
new file mode 100644
index 00000000000..33f144b23fd
--- /dev/null
+++ b/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/utils/tokenizer.py
@@ -0,0 +1,620 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Defines Subtokenizer class to encode and decode strings."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import re
+import sys
+import unicodedata
+
+import numpy as np
+import six
+from six.moves import xrange  # pylint: disable=redefined-builtin
+import tensorflow as tf
+
+PAD = "<pad>"
+PAD_ID = 0
+EOS = "<EOS>"
+EOS_ID = 1
+RESERVED_TOKENS = [PAD, EOS]
+
+# Set of characters that will be used in the function _escape_token() (see func
+# docstring for more details).
+# This set is added to the alphabet list to ensure that all escaped tokens can
+# be encoded.
+_ESCAPE_CHARS = set(u"\\_u;0123456789")
+# Regex for the function _unescape_token(), the inverse of _escape_token().
+# This is used to find "\u", "\\", and "\###;" substrings in the token.
+_UNESCAPE_REGEX = re.compile(r"\\u|\\\\|\\([0-9]+);")
+
+_UNDEFINED_UNICODE = u"\u3013"
+
+# Set contains all letter and number characters.
+_ALPHANUMERIC_CHAR_SET = set(
+    six.unichr(i) for i in xrange(sys.maxunicode)
+    if (unicodedata.category(six.unichr(i)).startswith("L") or
+        unicodedata.category(six.unichr(i)).startswith("N")))
+
+# min_count is the minimum number of times a subtoken must appear in the data
+# before before it is added to the vocabulary. The value is found using binary
+# search to obtain the target vocabulary size.
+_MIN_MIN_COUNT = 1     # min value to use when binary searching for min_count
+_MAX_MIN_COUNT = 1000  # max value to use when binary searching for min_count
+
+
+class Subtokenizer(object):
+  """Encodes and decodes strings to/from integer IDs."""
+
+  def __init__(self, vocab_file, reserved_tokens=None):
+    """Initializes class, creating a vocab file if data_files is provided."""
+    tf.compat.v1.logging.info("Initializing Subtokenizer from file %s." %
+                              vocab_file)
+
+    if reserved_tokens is None:
+      reserved_tokens = RESERVED_TOKENS
+
+    self.subtoken_list = _load_vocab_file(vocab_file, reserved_tokens)
+    self.alphabet = _generate_alphabet_dict(self.subtoken_list)
+    self.subtoken_to_id_dict = _list_to_index_dict(self.subtoken_list)
+
+    self.max_subtoken_length = 0
+    for subtoken in self.subtoken_list:
+      self.max_subtoken_length = max(self.max_subtoken_length, len(subtoken))
+
+    # Create cache to speed up subtokenization
+    self._cache_size = 2 ** 20
+    self._cache = [(None, None)] * self._cache_size
+
+  @staticmethod
+  def init_from_files(
+      vocab_file, files, target_vocab_size, threshold, min_count=None,
+      file_byte_limit=1e6, reserved_tokens=None, correct_strip=True):
+    """Create subtoken vocabulary based on files, and save vocab to file.
+
+    Args:
+      vocab_file: String name of vocab file to store subtoken vocabulary.
+      files: List of file paths that will be used to generate vocabulary.
+      target_vocab_size: target vocabulary size to generate.
+      threshold: int threshold of vocabulary size to accept.
+      min_count: int minimum count to use for generating the vocabulary. The min
+        count is the minimum number of times a subtoken should appear in the
+        files before it is added to the vocabulary. If set to none, this value
+        is found using binary search.
+      file_byte_limit: (Default 1e6) Maximum number of bytes of sample text that
+        will be drawn from the files.
+      reserved_tokens: List of string tokens that are guaranteed to be at the
+        beginning of the subtoken vocabulary list.
+      correct_strip: Whether to convert text to unicode before strip.
+
+    Returns:
+      Subtokenizer object
+    """
+    if reserved_tokens is None:
+      reserved_tokens = RESERVED_TOKENS
+
+    if tf.io.gfile.exists(vocab_file):
+      tf.compat.v1.logging.info("Vocab file already exists (%s)" % vocab_file)
+    else:
+      tf.compat.v1.logging.info("Begin steps to create subtoken vocabulary...")
+      token_counts = _count_tokens(files, file_byte_limit, correct_strip)
+      alphabet = _generate_alphabet_dict(token_counts)
+      subtoken_list = _generate_subtokens_with_target_vocab_size(
+          token_counts, alphabet, target_vocab_size, threshold, min_count,
+          reserved_tokens)
+      tf.compat.v1.logging.info("Generated vocabulary with %d subtokens." %
+                                len(subtoken_list))
+      _save_vocab_file(vocab_file, subtoken_list)
+    return Subtokenizer(vocab_file)
+
+  def encode(self, raw_string, add_eos=False):
+    """Encodes a string into a list of int subtoken ids."""
+    ret = []
+    tokens = _split_string_to_tokens(native_to_unicode(raw_string))
+    for token in tokens:
+      ret.extend(self._token_to_subtoken_ids(token))
+    if add_eos:
+      ret.append(EOS_ID)
+    return ret
+
+  def _token_to_subtoken_ids(self, token):
+    """Encode a single token into a list of subtoken ids."""
+    cache_location = hash(token) % self._cache_size
+    cache_key, cache_value = self._cache[cache_location]
+    if cache_key == token:
+      return cache_value
+
+    ret = _split_token_to_subtokens(
+        _escape_token(token, self.alphabet), self.subtoken_to_id_dict,
+        self.max_subtoken_length)
+    ret = [self.subtoken_to_id_dict[subtoken_id] for subtoken_id in ret]
+
+    self._cache[cache_location] = (token, ret)
+    return ret
+
+  def decode(self, subtokens):
+    """Converts list of int subtokens ids into a string."""
+    if isinstance(subtokens, np.ndarray):
+      # Note that list(subtokens) converts subtokens to a python list, but the
+      # items remain as np.int32. This converts both the array and its items.
+      subtokens = subtokens.tolist()
+
+    if not subtokens:
+      return ""
+
+    assert isinstance(subtokens, list) and isinstance(subtokens[0], int), (
+        "Subtokens argument passed into decode() must be a list of integers.")
+
+    return _unicode_to_native(
+        _join_tokens_to_string(self._subtoken_ids_to_tokens(subtokens)))
+
+  def _subtoken_ids_to_tokens(self, subtokens):
+    """Convert list of int subtoken ids to a list of string tokens."""
+    escaped_tokens = "".join([
+        self.subtoken_list[s] for s in subtokens
+        if s < len(self.subtoken_list)])
+    escaped_tokens = escaped_tokens.split("_")
+
+    # All tokens in the vocabulary list have been escaped (see _escape_token())
+    # so each token must be unescaped when decoding.
+    ret = []
+    for token in escaped_tokens:
+      if token:
+        ret.append(_unescape_token(token))
+    return ret
+
+
+def _save_vocab_file(vocab_file, subtoken_list):
+  """Save subtokens to file."""
+  with tf.io.gfile.GFile(vocab_file, mode="w") as f:
+    for subtoken in subtoken_list:
+      f.write("'%s'\n" % _unicode_to_native(subtoken))
+
+
+def _load_vocab_file(vocab_file, reserved_tokens=None):
+  """Load vocabulary while ensuring reserved tokens are at the top."""
+  if reserved_tokens is None:
+    reserved_tokens = RESERVED_TOKENS
+
+  subtoken_list = []
+  with tf.io.gfile.GFile(vocab_file, mode="r") as f:
+    for line in f:
+      subtoken = native_to_unicode(line.strip())
+      subtoken = subtoken[1:-1]  # Remove surrounding single-quotes
+      if subtoken in reserved_tokens:
+        continue
+      subtoken_list.append(native_to_unicode(subtoken))
+  return reserved_tokens + subtoken_list
+
+
+def native_to_unicode(s):
+  """Convert string to unicode (required in Python 2)."""
+  try:               # Python 2
+    return s if isinstance(s, unicode) else s.decode("utf-8")
+  except NameError:  # Python 3
+    return s
+
+
+def _unicode_to_native(s):
+  """Convert string from unicode to native format (required in Python 2)."""
+  try:               # Python 2
+    return s.encode("utf-8") if isinstance(s, unicode) else s
+  except NameError:  # Python 3
+    return s
+
+
+def _split_string_to_tokens(text):
+  """Splits text to a list of string tokens."""
+  if not text:
+    return []
+  ret = []
+  token_start = 0
+  # Classify each character in the input string
+  is_alnum = [c in _ALPHANUMERIC_CHAR_SET for c in text]
+  for pos in xrange(1, len(text)):
+    if is_alnum[pos] != is_alnum[pos - 1]:
+      token = text[token_start:pos]
+      if token != u" " or token_start == 0:
+        ret.append(token)
+      token_start = pos
+  final_token = text[token_start:]
+  ret.append(final_token)
+  return ret
+
+
+def _join_tokens_to_string(tokens):
+  """Join a list of string tokens into a single string."""
+  token_is_alnum = [t[0] in _ALPHANUMERIC_CHAR_SET for t in tokens]
+  ret = []
+  for i, token in enumerate(tokens):
+    if i > 0 and token_is_alnum[i - 1] and token_is_alnum[i]:
+      ret.append(u" ")
+    ret.append(token)
+  return "".join(ret)
+
+
+def _escape_token(token, alphabet):
+  r"""Replace characters that aren't in the alphabet and append "_" to token.
+
+  Apply three transformations to the token:
+    1. Replace underline character "_" with "\u", and backslash "\" with "\\".
+    2. Replace characters outside of the alphabet with "\###;", where ### is the
+       character's Unicode code point.
+    3. Appends "_" to mark the end of a token.
+
+  Args:
+    token: unicode string to be escaped
+    alphabet: list of all known characters
+
+  Returns:
+    escaped string
+  """
+  token = token.replace(u"\\", u"\\\\").replace(u"_", u"\\u")
+  ret = [c if c in alphabet and c != u"\n" else r"\%d;" % ord(c) for c in token]
+  return u"".join(ret) + "_"
+
+
+def _unescape_token(token):
+  r"""Replaces escaped characters in the token with their unescaped versions.
+
+  Applies inverse transformations as _escape_token():
+    1. Replace "\u" with "_", and "\\" with "\".
+    2. Replace "\###;" with the unicode character the ### refers to.
+
+  Args:
+    token: escaped string
+
+  Returns:
+    unescaped string
+  """
+
+  def match(m):
+    r"""Returns replacement string for matched object.
+
+    Matched objects contain one of the strings that matches the regex pattern:
+      r"\\u|\\\\|\\([0-9]+);"
+    The strings can be '\u', '\\', or '\###;' (### is any digit number).
+
+    m.group(0) refers to the entire matched string ('\u', '\\', or '\###;').
+    m.group(1) refers to the first parenthesized subgroup ('###').
+
+    m.group(0) exists for all match objects, while m.group(1) exists only for
+    the string '\###;'.
+
+    This function looks to see if m.group(1) exists. If it doesn't, then the
+    matched string must be '\u' or '\\' . In this case, the corresponding
+    replacement ('_' and '\') are returned. Note that in python, a single
+    backslash is written as '\\', and double backslash as '\\\\'.
+
+    If m.group(1) exists, then use the integer in m.group(1) to return a
+    unicode character.
+
+    Args:
+      m: match object
+
+    Returns:
+      String to replace matched object with.
+    """
+    # Check if the matched strings are '\u' or '\\'.
+    if m.group(1) is None:
+      return u"_" if m.group(0) == u"\\u" else u"\\"
+
+    # If m.group(1) exists, try and return unicode character.
+    try:
+      return six.unichr(int(m.group(1)))
+    except (ValueError, OverflowError) as _:
+      return _UNDEFINED_UNICODE
+
+  # Use match function to replace escaped substrings in the token.
+  return _UNESCAPE_REGEX.sub(match, token)
+
+
+def _count_tokens(files, file_byte_limit=1e6, correct_strip=True):
+  """Return token counts of words in the files.
+
+  Samples file_byte_limit bytes from each file, and counts the words that appear
+  in the samples. The samples are semi-evenly distributed across the file.
+
+  Args:
+    files: List of filepaths
+    file_byte_limit: Max number of bytes that will be read from each file.
+    correct_strip: Whether to convert text to unicode before strip. This affects
+      vocabulary generation for PY2. Sets correct_strip to False in PY2 to
+      reproduce previous common public result. Sets correct_strip to True will
+      let PY2 and PY3 get a consistent vocabulary.
+
+  Returns:
+    Dictionary mapping tokens to the number of times they appear in the sampled
+    lines from the files.
+  """
+  token_counts = collections.defaultdict(int)
+
+  for filepath in files:
+    with tf.io.gfile.GFile(filepath, mode="r") as reader:
+      file_byte_budget = file_byte_limit
+      counter = 0
+      lines_to_skip = int(reader.size() / (file_byte_budget * 2))
+      for line in reader:
+        if counter < lines_to_skip:
+          counter += 1
+        else:
+          if file_byte_budget < 0:
+            break
+          if correct_strip:
+            line = native_to_unicode(line)
+          line = line.strip()
+          file_byte_budget -= len(line)
+          counter = 0
+
+          # Add words to token counts
+          for token in _split_string_to_tokens(native_to_unicode(line)):
+            token_counts[token] += 1
+  return token_counts
+
+
+def _list_to_index_dict(lst):
+  """Create dictionary mapping list items to their indices in the list."""
+  return {item: n for n, item in enumerate(lst)}
+
+
+def _split_token_to_subtokens(token, subtoken_dict, max_subtoken_length):
+  """Splits a token into subtokens defined in the subtoken dict."""
+  ret = []
+  start = 0
+  token_len = len(token)
+  while start < token_len:
+    # Find the longest subtoken, so iterate backwards.
+    for end in xrange(min(token_len, start + max_subtoken_length), start, -1):
+      subtoken = token[start:end]
+      if subtoken in subtoken_dict:
+        ret.append(subtoken)
+        start = end
+        break
+    else:  # Did not break
+      # If there is no possible encoding of the escaped token then one of the
+      # characters in the token is not in the alphabet. This should be
+      # impossible and would be indicative of a bug.
+      raise ValueError("Was unable to split token \"%s\" into subtokens." %
+                       token)
+  return ret
+
+
+def _generate_subtokens_with_target_vocab_size(
+    token_counts, alphabet, target_size, threshold, min_count=None,
+    reserved_tokens=None):
+  """Generate subtoken vocabulary close to the target size."""
+  if reserved_tokens is None:
+    reserved_tokens = RESERVED_TOKENS
+
+  if min_count is not None:
+    tf.compat.v1.logging.info(
+        "Using min_count=%d to generate vocab with target size %d" %
+        (min_count, target_size))
+    return _generate_subtokens(
+        token_counts, alphabet, min_count, reserved_tokens=reserved_tokens)
+
+  def bisect(min_val, max_val):
+    """Recursive function to binary search for subtoken vocabulary."""
+    cur_count = (min_val + max_val) // 2
+    tf.compat.v1.logging.info("Binary search: trying min_count=%d (%d %d)" %
+                              (cur_count, min_val, max_val))
+    subtoken_list = _generate_subtokens(
+        token_counts, alphabet, cur_count, reserved_tokens=reserved_tokens)
+
+    val = len(subtoken_list)
+    tf.compat.v1.logging.info(
+        "Binary search: min_count=%d resulted in %d tokens" % (cur_count, val))
+
+    within_threshold = abs(val - target_size) < threshold
+    if within_threshold or min_val >= max_val or cur_count < 2:
+      return subtoken_list
+    if val > target_size:
+      other_subtoken_list = bisect(cur_count + 1, max_val)
+    else:
+      other_subtoken_list = bisect(min_val, cur_count - 1)
+
+    # Return vocabulary dictionary with the closest number of tokens.
+    other_val = len(other_subtoken_list)
+    if abs(other_val - target_size) < abs(val - target_size):
+      return other_subtoken_list
+    return subtoken_list
+
+  tf.compat.v1.logging.info("Finding best min_count to get target size of %d" %
+                            target_size)
+  return bisect(_MIN_MIN_COUNT, _MAX_MIN_COUNT)
+
+
+def _generate_alphabet_dict(iterable, reserved_tokens=None):
+  """Create set of characters that appear in any element in the iterable."""
+  if reserved_tokens is None:
+    reserved_tokens = RESERVED_TOKENS
+  alphabet = {c for token in iterable for c in token}
+  alphabet |= {c for token in reserved_tokens for c in token}
+  alphabet |= _ESCAPE_CHARS  # Add escape characters to alphabet set.
+  return alphabet
+
+
+def _count_and_gen_subtokens(
+    token_counts, alphabet, subtoken_dict, max_subtoken_length):
+  """Count number of times subtokens appear, and generate new subtokens.
+
+  Args:
+    token_counts: dict mapping tokens to the number of times they appear in the
+      original files.
+    alphabet: list of allowed characters. Used to escape the tokens, which
+      guarantees that all tokens can be split into subtokens.
+    subtoken_dict: dict mapping subtokens to ids.
+    max_subtoken_length: maximum length of subtoken in subtoken_dict.
+
+  Returns:
+    A defaultdict mapping subtokens to the number of times they appear in the
+    tokens. The dict may contain new subtokens.
+  """
+  subtoken_counts = collections.defaultdict(int)
+  for token, count in six.iteritems(token_counts):
+    token = _escape_token(token, alphabet)
+    subtokens = _split_token_to_subtokens(
+        token, subtoken_dict, max_subtoken_length)
+
+    # Generate new subtokens by taking substrings from token.
+    start = 0
+    for subtoken in subtokens:
+      for end in xrange(start + 1, len(token) + 1):
+        new_subtoken = token[start:end]
+        subtoken_counts[new_subtoken] += count
+      start += len(subtoken)
+
+  return subtoken_counts
+
+
+def _filter_and_bucket_subtokens(subtoken_counts, min_count):
+  """Return a bucketed list of subtokens that are filtered by count.
+
+  Args:
+    subtoken_counts: defaultdict mapping subtokens to their counts
+    min_count: int count used to filter subtokens
+
+  Returns:
+    List of subtoken sets, where subtokens in set i have the same length=i.
+  """
+  # Create list of buckets, where subtokens in bucket i have length i.
+  subtoken_buckets = []
+  for subtoken, count in six.iteritems(subtoken_counts):
+    if count < min_count:  # Filter out subtokens that don't appear enough
+      continue
+    while len(subtoken_buckets) <= len(subtoken):
+      subtoken_buckets.append(set())
+    subtoken_buckets[len(subtoken)].add(subtoken)
+  return subtoken_buckets
+
+
+def _gen_new_subtoken_list(
+    subtoken_counts, min_count, alphabet, reserved_tokens=None):
+  """Generate candidate subtokens ordered by count, and new max subtoken length.
+
+  Add subtokens to the candidate list in order of length (longest subtokens
+  first). When a subtoken is added, the counts of each of its prefixes are
+  decreased. Prefixes that don't appear much outside the subtoken are not added
+  to the candidate list.
+
+  For example:
+    subtoken being added to candidate list: 'translate'
+    subtoken_counts: {'translate':10, 't':40, 'tr':16, 'tra':12, ...}
+    min_count: 5
+
+  When 'translate' is added, subtoken_counts is updated to:
+    {'translate':0, 't':30, 'tr':6, 'tra': 2, ...}
+
+  The subtoken 'tra' will not be added to the candidate list, because it appears
+  twice (less than min_count) outside of 'translate'.
+
+  Args:
+    subtoken_counts: defaultdict mapping str subtokens to int counts
+    min_count: int minimum count requirement for subtokens
+    alphabet: set of characters. Each character is added to the subtoken list to
+      guarantee that all tokens can be encoded.
+    reserved_tokens: list of tokens that will be added to the beginning of the
+      returned subtoken list.
+
+  Returns:
+    List of candidate subtokens in decreasing count order, and maximum subtoken
+    length
+  """
+  if reserved_tokens is None:
+    reserved_tokens = RESERVED_TOKENS
+
+  # Create a list of (count, subtoken) for each candidate subtoken.
+  subtoken_candidates = []
+
+  # Use bucketted list to iterate through subtokens in order of length.
+  # subtoken_buckets[i] = set(subtokens), where each subtoken has length i.
+  subtoken_buckets = _filter_and_bucket_subtokens(subtoken_counts, min_count)
+  max_subtoken_length = len(subtoken_buckets) - 1
+
+  # Go through the list in reverse order to consider longer subtokens first.
+  for subtoken_len in xrange(max_subtoken_length, 0, -1):
+    for subtoken in subtoken_buckets[subtoken_len]:
+      count = subtoken_counts[subtoken]
+
+      # Possible if this subtoken is a prefix of another token.
+      if count < min_count:
+        continue
+
+      # Ignore alphabet/reserved tokens, which will be added manually later.
+      if subtoken not in alphabet and subtoken not in reserved_tokens:
+        subtoken_candidates.append((count, subtoken))
+
+      # Decrement count of the subtoken's prefixes (if a longer subtoken is
+      # added, its prefixes lose priority to be added).
+      for end in xrange(1, subtoken_len):
+        subtoken_counts[subtoken[:end]] -= count
+
+  # Add alphabet subtokens (guarantees that all strings are encodable).
+  subtoken_candidates.extend((subtoken_counts.get(a, 0), a) for a in alphabet)
+
+  # Order subtoken candidates by decreasing count.
+  subtoken_list = [t for _, t in sorted(subtoken_candidates, reverse=True)]
+
+  # Add reserved tokens to beginning of the list.
+  subtoken_list = reserved_tokens + subtoken_list
+  return subtoken_list, max_subtoken_length
+
+
+def _generate_subtokens(
+    token_counts, alphabet, min_count, num_iterations=4,
+    reserved_tokens=None):
+  """Create a list of subtokens in decreasing order of frequency.
+
+  Args:
+    token_counts: dict mapping str tokens -> int count
+    alphabet: set of characters
+    min_count: int minimum number of times a subtoken must appear before it is
+      added to the vocabulary.
+    num_iterations: int number of iterations to generate new tokens.
+    reserved_tokens: list of tokens that will be added to the beginning to the
+      returned subtoken list.
+
+  Returns:
+    Sorted list of subtokens (most frequent first)
+  """
+  if reserved_tokens is None:
+    reserved_tokens = RESERVED_TOKENS
+
+  # Use alphabet set to create initial list of subtokens
+  subtoken_list = reserved_tokens + list(alphabet)
+  max_subtoken_length = 1
+
+  # On each iteration, segment all words using the subtokens defined in
+  # subtoken_dict, count how often the resulting subtokens appear, and update
+  # the dictionary with subtokens w/ high enough counts.
+  for i in xrange(num_iterations):
+    tf.compat.v1.logging.info("\tGenerating subtokens: iteration %d" % i)
+    # Generate new subtoken->id dictionary using the new subtoken list.
+    subtoken_dict = _list_to_index_dict(subtoken_list)
+
+    # Create dict mapping subtoken->count, with additional subtokens created
+    # from substrings taken from the tokens.
+    subtoken_counts = _count_and_gen_subtokens(
+        token_counts, alphabet, subtoken_dict, max_subtoken_length)
+
+    # Generate new list of subtokens sorted by subtoken count.
+    subtoken_list, max_subtoken_length = _gen_new_subtoken_list(
+        subtoken_counts, min_count, alphabet, reserved_tokens)
+
+    tf.compat.v1.logging.info("\tVocab size: %d" % len(subtoken_list))
+  return subtoken_list
diff --git a/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/utils/tokenizer_test.py b/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/utils/tokenizer_test.py
new file mode 100644
index 00000000000..f757389f30d
--- /dev/null
+++ b/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/utils/tokenizer_test.py
@@ -0,0 +1,182 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Test Subtokenizer and string helper methods."""
+
+import collections
+import tempfile
+
+import tensorflow as tf  # pylint: disable=g-bad-import-order
+
+from official.transformer.utils import tokenizer
+
+
+class SubtokenizerTest(tf.test.TestCase):
+
+  def _init_subtokenizer(self, vocab_list):
+    temp_file = tempfile.NamedTemporaryFile(delete=False)
+    with tf.io.gfile.GFile(temp_file.name, "w") as w:
+      for subtoken in vocab_list:
+        w.write("'%s'" % subtoken)
+        w.write("\n")
+    return tokenizer.Subtokenizer(temp_file.name, reserved_tokens=[])
+
+  def test_encode(self):
+    vocab_list = ["123_", "test", "ing_"]
+    subtokenizer = self._init_subtokenizer(vocab_list)
+    s = "testing 123"
+    encoded_list = subtokenizer.encode(s)
+    self.assertEqual([1, 2, 0], encoded_list)
+
+  def test_decode(self):
+    vocab_list = ["123_", "test", "ing_"]
+    subtokenizer = self._init_subtokenizer(vocab_list)
+    encoded_list = [1, 2, 0]  # testing 123
+    decoded_str = subtokenizer.decode(encoded_list)
+    self.assertEqual("testing 123", decoded_str)
+
+  def test_subtoken_ids_to_tokens(self):
+    vocab_list = ["123_", "test", "ing_"]
+    subtokenizer = self._init_subtokenizer(vocab_list)
+    encoded_list = [1, 2, 0]  # testing 123
+    token_list = subtokenizer._subtoken_ids_to_tokens(encoded_list)
+    self.assertEqual([u"testing", u"123"], token_list)
+
+
+class StringHelperTest(tf.test.TestCase):
+
+  def test_split_string_to_tokens(self):
+    text = "test? testing 123."
+
+    tokens = tokenizer._split_string_to_tokens(text)
+    self.assertEqual(["test", "? ", "testing", "123", "."], tokens)
+
+  def test_join_tokens_to_string(self):
+    tokens = ["test", "? ", "testing", "123", "."]
+
+    s = tokenizer._join_tokens_to_string(tokens)
+    self.assertEqual("test? testing 123.", s)
+
+  def test_escape_token(self):
+    token = u"abc_\\4"
+    alphabet = set("abc_\\u;")
+
+    escaped_token = tokenizer._escape_token(token, alphabet)
+    self.assertEqual("abc\\u\\\\\\52;_", escaped_token)
+
+  def test_unescape_token(self):
+    escaped_token = u"Underline: \\u, Backslash: \\\\, Unicode: \\52;"
+
+    unescaped_token = tokenizer._unescape_token(escaped_token)
+    self.assertEqual(
+        "Underline: _, Backslash: \\, Unicode: 4", unescaped_token)
+
+  def test_list_to_index_dict(self):
+    lst = ["test", "strings"]
+
+    d = tokenizer._list_to_index_dict(lst)
+    self.assertDictEqual({"test": 0, "strings": 1}, d)
+
+  def test_split_token_to_subtokens(self):
+    token = "abc"
+    subtoken_dict = {"a": 0, "b": 1, "c": 2, "ab": 3}
+    max_subtoken_length = 2
+
+    subtokens = tokenizer._split_token_to_subtokens(
+        token, subtoken_dict, max_subtoken_length)
+    self.assertEqual(["ab", "c"], subtokens)
+
+  def test_generate_alphabet_dict(self):
+    s = ["testing", "123"]
+    reserved_tokens = ["???"]
+
+    alphabet = tokenizer._generate_alphabet_dict(s, reserved_tokens)
+    self.assertIn("?", alphabet)
+    self.assertIn("t", alphabet)
+    self.assertIn("e", alphabet)
+    self.assertIn("s", alphabet)
+    self.assertIn("i", alphabet)
+    self.assertIn("n", alphabet)
+    self.assertIn("g", alphabet)
+    self.assertIn("1", alphabet)
+    self.assertIn("2", alphabet)
+    self.assertIn("3", alphabet)
+
+  def test_count_and_gen_subtokens(self):
+    token_counts = {"abc": 5}
+    alphabet = set("abc_")
+    subtoken_dict = {"a": 0, "b": 1, "c": 2, "_": 3}
+    max_subtoken_length = 2
+
+    subtoken_counts = tokenizer._count_and_gen_subtokens(
+        token_counts, alphabet, subtoken_dict, max_subtoken_length)
+
+    self.assertIsInstance(subtoken_counts, collections.defaultdict)
+    self.assertDictEqual(
+        {"a": 5, "b": 5, "c": 5, "_": 5, "ab": 5, "bc": 5, "c_": 5,
+         "abc": 5, "bc_": 5, "abc_": 5}, subtoken_counts)
+
+  def test_filter_and_bucket_subtokens(self):
+    subtoken_counts = collections.defaultdict(
+        int, {"a": 2, "b": 4, "c": 1, "ab": 6, "ac": 3, "abbc": 5})
+    min_count = 3
+
+    subtoken_buckets = tokenizer._filter_and_bucket_subtokens(
+        subtoken_counts, min_count)
+
+    self.assertEqual(len(subtoken_buckets[0]), 0)
+    self.assertEqual(set("b"), subtoken_buckets[1])
+    self.assertEqual(set(["ab", "ac"]), subtoken_buckets[2])
+    self.assertEqual(len(subtoken_buckets[3]), 0)
+    self.assertEqual(set(["abbc"]), subtoken_buckets[4])
+
+  def test_gen_new_subtoken_list(self):
+    subtoken_counts = collections.defaultdict(
+        int, {"translate": 10, "t": 40, "tr": 16, "tra": 12})
+    min_count = 5
+    alphabet = set("translate")
+    reserved_tokens = ["reserved", "tokens"]
+
+    subtoken_list, max_token_length = tokenizer._gen_new_subtoken_list(
+        subtoken_counts, min_count, alphabet, reserved_tokens)
+
+    # Check that "tra" isn"t in the list (its count should be decremented to 2,
+    # so it should not be added to the canddiate list).
+    self.assertNotIn("tra", subtoken_list)
+
+    self.assertIn("tr", subtoken_list)
+    self.assertIn("t", subtoken_list)
+
+    self.assertEqual(len("translate"), max_token_length)
+
+  def test_generate_subtokens(self):
+    token_counts = {"ab": 1, "bc": 3, "abc": 5}
+    alphabet = set("abc_")
+    min_count = 100
+    num_iterations = 1
+    reserved_tokens = ["reserved", "tokens"]
+
+    vocab_list = tokenizer._generate_subtokens(
+        token_counts, alphabet, min_count, num_iterations, reserved_tokens)
+
+    # Check that reserved tokens are at the front of the list
+    self.assertEqual(vocab_list[:2], reserved_tokens)
+
+    # Check that each character in alphabet is in the vocab list
+    for c in alphabet:
+      self.assertIn(c, vocab_list)
+
+
+if __name__ == "__main__":
+  tf.test.main()
diff --git a/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/README.md b/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/README.md
new file mode 100644
index 00000000000..a2c4fbfbbb8
--- /dev/null
+++ b/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/README.md
@@ -0,0 +1,130 @@
+Step-by-Step
+============
+
+This document is used to list steps of reproducing TensorFlow Object Detection models tuning results. This example can run on Intel CPUs and GPUs.
+
+# Prerequisite
+
+
+## 1. Environment
+Recommend python 3.9 or higher version.
+
+### Install Intel® Neural Compressor
+```shell
+pip install neural-compressor
+```
+
+### Install Intel Tensorflow
+```shell
+pip install tensorflow
+```
+> Note: Validated TensorFlow [Version](/docs/source/installation_guide.md#validated-software-environment).
+
+### Installation Dependency packages
+```shell
+cd examples/3.x_api/tensorflow/object_detection
+pip install -r requirements.txt
+cd faster_rcnn_resnet50/quantization/ptq
+```
+
+### Install Protocol Buffer Compiler
+
+`Protocol Buffer Compiler` in version higher than 3.0.0 is necessary ingredient for automatic COCO dataset preparation. To install please follow
+[Protobuf installation instructions](https://grpc.io/docs/protoc-installation/#install-using-a-package-manager).
+
+### Install Intel Extension for Tensorflow
+
+#### Quantizing the model on Intel GPU(Mandatory to install ITEX)
+Intel Extension for Tensorflow is mandatory to be installed for quantizing the model on Intel GPUs.
+
+```shell
+pip install --upgrade intel-extension-for-tensorflow[xpu]
+```
+For any more details, please follow the procedure in [install-gpu-drivers](https://github.com/intel/intel-extension-for-tensorflow/blob/main/docs/install/install_for_xpu.md#install-gpu-drivers)
+
+#### Quantizing the model on Intel CPU(Optional to install ITEX)
+Intel Extension for Tensorflow for Intel CPUs is experimental currently. It's not mandatory for quantizing the model on Intel CPUs.
+
+```shell
+pip install --upgrade intel-extension-for-tensorflow[cpu]
+```
+
+> **Note**: 
+> The version compatibility of stock Tensorflow and ITEX can be checked [here](https://github.com/intel/intel-extension-for-tensorflow#compatibility-table). Please make sure you have installed compatible Tensorflow and ITEX.
+
+## 2. Prepare Model
+
+```shell
+wget https://storage.googleapis.com/intel-optimized-tensorflow/models/faster_rcnn_resnet50_fp32_coco_pretrained_model.tar.gz
+tar -xvf faster_rcnn_resnet50_fp32_coco_pretrained_model.tar.gz
+```
+
+## 3. Prepare Dataset
+
+### Automatic dataset download
+
+> **_Note: `prepare_dataset.sh` script works with TF version 1.x._**
+
+Run the `prepare_dataset.sh` script located in `examples/3.x_api/tensorflow/object_detection`.
+
+Usage:
+```shell
+cd examples/3.x_api/tensorflow/object_detection
+. prepare_dataset.sh
+cd faster_rcnn_resnet50/quantization/ptq
+```
+
+This script will download the *train*, *validation* and *test* COCO datasets. Furthermore it will convert them to
+tensorflow records using the `https://github.com/tensorflow/models.git` dedicated script.
+
+### Manual dataset download
+Download CoCo Dataset from [Official Website](https://cocodataset.org/#download).
+
+
+# Run
+
+## 1. Quantization
+  
+  ```shell
+  # The cmd of running faster_rcnn_resnet50
+  bash run_quant.sh --input_model=./faster_rcnn_resnet50_fp32_coco_pretrained_model/frozen_inference_graph.pb --output_model=./tensorflow-faster_rcnn_resnet50-tune.pb --dataset_location=/path/to/dataset/coco_val.record
+  ```
+
+## 2. Benchmark
+  ```shell
+  bash run_benchmark.sh --input_model=./tensorflow-faster_rcnn_resnet50-tune.pb  --dataset_location=/path/to/dataset/coco_val.record --mode=performance
+  ```
+
+Details of enabling Intel® Neural Compressor on faster_rcnn_resnet50 for Tensorflow.
+=========================
+
+This is a tutorial of how to enable faster_rcnn_resnet50 model with Intel® Neural Compressor.
+## User Code Analysis
+User specifies fp32 *model*, calibration dataset *q_dataloader* and a custom *eval_func* which encapsulates the evaluation dataset and metric by itself.
+
+For faster_rcnn_resnet50, we applied the latter one because our philosophy is to enable the model with minimal changes. Hence we need to make two changes on the original code. The first one is to implement the q_dataloader and make necessary changes to *eval_func*.
+
+### Code update
+
+After prepare step is done, we just need update main.py like below.
+```python
+    if args.tune:
+        from neural_compressor.tensorflow import StaticQuantConfig, quantize_model, Model
+
+        quant_config = StaticQuantConfig()
+        model = Model(args.input_graph)
+        model.input_tensor_names = ['image_tensor']
+        model.output_tensor_names = ["num_detections", "detection_boxes", "detection_scores", "detection_classes"]
+        q_model = quantize_model(model, quant_config, calib_dataloader)
+        q_model.save(args.output_model)
+            
+    if args.benchmark:
+        if args.mode == 'performance':
+            evaluate(args.input_graph)
+        else:
+            accuracy = evaluate(args.input_graph)
+            print('Batch size = %d' % args.batch_size)
+            print("Accuracy: %.5f" % accuracy)
+```
+
+The quantization.fit() function will return a best quantized model during timeout constrain.
diff --git a/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/coco_tools.py b/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/coco_tools.py
new file mode 100644
index 00000000000..2f9369798df
--- /dev/null
+++ b/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/coco_tools.py
@@ -0,0 +1,694 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2021 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Wrappers for third party pycocotools to be used within object_detection.
+
+Note that nothing in this file is tensorflow related and thus cannot
+be called directly as a slim metric, for example.
+
+TODO(jonathanhuang): wrap as a slim metric in metrics.py
+
+
+Usage example: given a set of images with ids in the list image_ids
+and corresponding lists of numpy arrays encoding groundtruth (boxes and classes)
+and detections (boxes, scores and classes), where elements of each list
+correspond to detections/annotations of a single image,
+then evaluation (in multi-class mode) can be invoked as follows:
+
+  groundtruth_dict = coco_tools.ExportGroundtruthToCOCO(
+      image_ids, groundtruth_boxes_list, groundtruth_classes_list,
+      max_num_classes, output_path=None)
+  detections_list = coco_tools.ExportDetectionsToCOCO(
+      image_ids, detection_boxes_list, detection_scores_list,
+      detection_classes_list, output_path=None)
+  groundtruth = coco_tools.COCOWrapper(groundtruth_dict)
+  detections = groundtruth.LoadAnnotations(detections_list)
+  evaluator = coco_tools.COCOEvalWrapper(groundtruth, detections,
+                                         agnostic_mode=False)
+  metrics = evaluator.ComputeMetrics()
+"""
+
+import copy
+import time
+from collections import OrderedDict
+from typing import Any, Dict, List, Set, Union
+
+import numpy as np
+from pycocotools import coco, cocoeval, mask
+
+from neural_compressor.utils import logger
+
+
+class COCOWrapper(coco.COCO):
+    """Wrapper for the pycocotools COCO class.
+
+    Attributes:
+      dataset: a dictionary holding bounding box annotations in the COCO format.
+      detection_type: type of detections being wrapped. Can be one of ['bbox',
+        'segmentation']
+    """
+
+    def __init__(self, dataset: Dict[str, Any], detection_type: str = "bbox"):
+        """Construct a COCOWrapper.
+
+        See http://mscoco.org/dataset/#format for a description of the format.
+        By default, the coco.COCO class constructor reads from a JSON file.
+        This function duplicates the same behavior but loads from a dictionary,
+        allowing us to perform evaluation without writing to external storage.
+
+        Args:
+          dataset: a dictionary holding bounding box annotations in the COCO format.
+          detection_type: type of detections being wrapped. Can be one of ['bbox',
+            'segmentation']
+
+        Raises:
+          ValueError: if detection_type is unsupported.
+        """
+        supported_detection_types = ["bbox", "segmentation"]
+        if detection_type not in supported_detection_types:
+            raise ValueError(
+                "Unsupported detection type: {}. "
+                "Supported values are: {}".format(detection_type, supported_detection_types)
+            )
+        self._detection_type = detection_type
+        coco.COCO.__init__(self)
+        self.dataset = dataset
+        self.createIndex()
+
+    def LoadAnnotations(self, annotations: list) -> coco.COCO:
+        """Load annotations dictionary into COCO datastructure.
+
+        See http://mscoco.org/dataset/#format for a description of the annotations
+        format.  As above, this function replicates the default behavior of the API
+        but does not require writing to external storage.
+
+        Args:
+          annotations: python list holding object detection results where each
+            detection is encoded as a dict with required keys ['image_id',
+            'category_id', 'score'] and one of ['bbox', 'segmentation'] based on
+            `detection_type`.
+
+        Returns:
+          a coco.COCO datastructure holding object detection annotations results
+
+        Raises:
+          ValueError: if (1) annotations is not a list or annotations do not
+            correspond to the images contained in self.
+        """
+        results = coco.COCO()
+        results.dataset["images"] = [img for img in self.dataset["images"]]
+
+        logger.info("Load and prepare annotation results.")
+        tic = time.time()
+
+        if not isinstance(annotations, list):
+            raise ValueError("annotations is not a list of objects")
+        annotation_img_ids = [ann["image_id"] for ann in annotations]
+        if set(annotation_img_ids) != (set(annotation_img_ids) & set(self.getImgIds())):
+            raise ValueError("Results do not correspond to current coco set")
+        results.dataset["categories"] = copy.deepcopy(self.dataset["categories"])
+        if self._detection_type == "bbox":
+            for idx, ann in enumerate(annotations):
+                bb = ann["bbox"]
+                ann["area"] = bb[2] * bb[3]
+                ann["id"] = idx + 1
+                ann["iscrowd"] = 0
+        elif self._detection_type == "segmentation":
+            for idx, ann in enumerate(annotations):
+                ann["area"] = mask.area(ann["segmentation"])
+                ann["bbox"] = mask.toBbox(ann["segmentation"])
+                ann["id"] = idx + 1
+                ann["iscrowd"] = 0
+        logger.info("DONE (t=%0.2fs)", (time.time() - tic))
+
+        results.dataset["annotations"] = annotations
+        results.createIndex()
+        return results
+
+
+class COCOEvalWrapper(cocoeval.COCOeval):
+    """Wrapper for the pycocotools COCOeval class.
+
+    To evaluate, create two objects (groundtruth_dict and detections_list)
+    using the conventions listed at http://mscoco.org/dataset/#format.
+    Then call evaluation as follows:
+
+      groundtruth = coco_tools.COCOWrapper(groundtruth_dict)
+      detections = groundtruth.LoadAnnotations(detections_list)
+      evaluator = coco_tools.COCOEvalWrapper(groundtruth, detections,
+                                             agnostic_mode=False)
+      metrics = evaluator.ComputeMetrics()
+    """
+
+    def __init__(
+        self,
+        groundtruth: coco.COCO = None,
+        detections: coco.COCO = None,
+        agnostic_mode=False,
+        iou_type: str = "bbox",
+        iou_thrs: Union[str, float] = None,
+        map_points=None,
+    ):
+        """Construct a COCOEvalWrapper.
+
+        Note that for the area-based metrics to be meaningful, detection and
+        groundtruth boxes must be in image coordinates measured in pixels.
+
+        Args:
+          groundtruth: a coco.COCO (or coco_tools.COCOWrapper) object holding
+            groundtruth annotations
+          detections: a coco.COCO (or coco_tools.COCOWrapper) object holding
+            detections
+          agnostic_mode: boolean (default: False).  If True, evaluation ignores
+            class labels, treating all detections as proposals.
+          iou_thrs: Minimal value for intersection over union that allows to
+                    make decision that prediction bounding box is true positive.
+                    You can specify one float value between 0 to 1 or
+                    string "05:0.05:0.95" for standard COCO thresholds.
+          iou_type: IOU type to use for evaluation. Supports `bbox` or `segm`.
+          map_points: The way to calculate mAP. 101 for 101-point interpolated AP, 11 for
+                          11-point interpolated AP, 0 for area under PR curve.
+        """
+        cocoeval.COCOeval.__init__(self, groundtruth, detections, iouType=iou_type)
+        if agnostic_mode:
+            self.params.useCats = 0
+        if iou_thrs == "0.5:0.05:0.95":
+            self.params.iouThrs = np.linspace(0.5, 0.95, int(np.round((0.95 - 0.5) / 0.05)) + 1, endpoint=True)
+        elif isinstance(iou_thrs, float):
+            self.params.iouThrs = [iou_thrs]
+
+        if map_points == 101:
+            self.params.recThrs = np.linspace(0.0, 1.00, int(np.round((1.00 - 0.0) / 0.01)) + 1, endpoint=True)
+        if map_points == 11:
+            self.params.recThrs = np.linspace(0.0, 1.00, int(np.round((1.00 - 0.0) / 0.1)) + 1, endpoint=True)
+        if map_points == 0:
+            self.params.recThrs = [-1]
+
+    def GetCategory(self, category_id: int) -> dict:
+        """Fetch dictionary holding category information given category id.
+
+        Args:
+          category_id: integer id
+
+        Returns:
+          dictionary holding 'id', 'name'.
+        """
+        return self.cocoGt.cats[category_id]
+
+    def GetAgnosticMode(self) -> bool:
+        """Return whether COCO Eval is configured to evaluate in agnostic mode."""
+        return self.params.useCats == 0
+
+    def GetCategoryIdList(self) -> List[int]:
+        """Return the list of IDs of all valid categories."""
+        return self.params.catIds
+
+    def accumulate(self, p: cocoeval.Params = None):
+        """Accumulate evaluation results per image and store it to self.eval.
+
+        Args:
+          p: input params for evaluation
+        """
+        print("Accumulating evaluation results...")
+        tic = time.time()
+        if not self.evalImgs:
+            print("Please run evaluate() first")
+        # allows input customized parameters
+        if p is None:
+            p = self.params
+        p.catIds = p.catIds if p.useCats == 1 else [-1]
+        T = len(p.iouThrs)
+        R = len(p.recThrs)
+        K = len(p.catIds) if p.useCats else 1
+        A = len(p.areaRng)
+        M = len(p.maxDets)
+        precision = -np.ones((T, R, K, A, M))  # -1 for the precision of absent categories
+        recall = -np.ones((T, K, A, M))
+        scores = -np.ones((T, R, K, A, M))
+
+        # create dictionary for future indexing
+        _pe = self._paramsEval
+        print("-pe", _pe)
+        catIds = _pe.catIds if _pe.useCats else [-1]
+        setK = set(catIds)
+        setA = set(map(tuple, _pe.areaRng))
+        setM = set(_pe.maxDets)
+        setI = set(_pe.imgIds)
+        # get inds to evaluate
+        k_list = [n for n, k in enumerate(p.catIds) if k in setK]
+        m_list = [m for n, m in enumerate(p.maxDets) if m in setM]
+        a_list = [n for n, a in enumerate(map(lambda x: tuple(x), p.areaRng)) if a in setA]
+        i_list = [n for n, i in enumerate(p.imgIds) if i in setI]
+        I0 = len(_pe.imgIds)
+        A0 = len(_pe.areaRng)
+        # retrieve E at each category, area range, and max number of detections
+        for k, k0 in enumerate(k_list):
+            Nk = k0 * A0 * I0
+            for a, a0 in enumerate(a_list):
+                Na = a0 * I0
+                for m, maxDet in enumerate(m_list):
+                    E = [self.evalImgs[Nk + Na + i] for i in i_list]
+                    E = [e for e in E if e is not None]
+                    if len(E) == 0:
+                        continue
+                    dtScores = np.concatenate([e["dtScores"][0:maxDet] for e in E])
+
+                    # different sorting method generates slightly different results.
+                    # mergesort is used to be consistent as Matlab implementation.
+                    inds = np.argsort(-dtScores, kind="mergesort")
+                    dtScoresSorted = dtScores[inds]
+
+                    dtm = np.concatenate([e["dtMatches"][:, 0:maxDet] for e in E], axis=1)[:, inds]
+                    dtIg = np.concatenate([e["dtIgnore"][:, 0:maxDet] for e in E], axis=1)[:, inds]
+                    gtIg = np.concatenate([e["gtIgnore"] for e in E])
+                    npig = np.count_nonzero(gtIg == 0)
+                    if npig == 0:
+                        continue
+                    tps = np.logical_and(dtm, np.logical_not(dtIg))
+                    fps = np.logical_and(np.logical_not(dtm), np.logical_not(dtIg))
+
+                    tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float32)
+                    fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float32)
+                    for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)):
+                        tp = np.array(tp)
+                        fp = np.array(fp)
+                        nd = len(tp)
+                        rc = tp / npig
+                        pr = tp / (fp + tp + np.spacing(1))
+
+                        # calculate precision
+                        if R == 1:
+                            rc = np.concatenate(([0.0], rc, [1.0]))
+                            pr = np.concatenate(([0.0], pr, [0.0]))
+
+                            # compute the precision envelope
+                            for i in range(pr.size - 1, 0, -1):
+                                pr[i - 1] = np.maximum(pr[i - 1], pr[i])
+
+                            # to calculate area under PR curve, look for points
+                            # where X axis (recall) changes value
+                            change_point = np.where(rc[1:] != rc[:-1])[0]
+                            # and sum (\Delta recall) * recall
+                            res = np.sum((rc[change_point + 1] - rc[change_point]) * pr[change_point + 1])
+                            precision[t, :, k, a, m] = np.array([res])
+                        else:
+                            q = np.zeros((R,))
+
+                            # numpy is slow without cython optimization for accessing elements
+                            # use python array gets significant speed improvement
+                            pr = pr.tolist()
+                            q = q.tolist()
+
+                            for i in range(nd - 1, 0, -1):
+                                if pr[i] > pr[i - 1]:
+                                    pr[i - 1] = pr[i]
+
+                            inds = np.searchsorted(rc, p.recThrs, side="left")
+                            try:
+                                for ri, pi in enumerate(inds):
+                                    q[ri] = pr[pi]
+                            except:
+                                pass
+                            precision[t, :, k, a, m] = np.array(q)
+
+                        # calculate recall
+                        if nd:
+                            recall[t, k, a, m] = rc[-1]
+                        else:
+                            recall[t, k, a, m] = 0
+
+                        # calculate score
+                        ss = np.zeros((R,))
+                        inds = np.searchsorted(rc, p.recThrs, side="left")
+                        try:
+                            for ri, pi in enumerate(inds):
+                                ss[ri] = dtScoresSorted[pi]
+                        except:
+                            pass
+                        scores[t, :, k, a, m] = np.array(ss)
+        # exit(0)
+        self.eval = {
+            "params": p,
+            "counts": [T, R, K, A, M],
+            "precision": precision,
+            "recall": recall,
+            "scores": scores,
+        }
+        toc = time.time()
+        print("DONE (t={:0.2f}s).".format(toc - tic))
+
+    def ComputeMetrics(
+        self, include_metrics_per_category: bool = False, all_metrics_per_category: bool = False
+    ):  # pragma: no cover
+        """Compute detection metrics.
+
+        Args:
+          include_metrics_per_category: Whether include metrics per category.
+          all_metrics_per_category: Whether include all the summery metrics for
+            each category in per_category_ap. Be careful with setting it to true if
+            you have more than handful of categories, because it will pollute
+            your mldash.
+
+        Returns:
+          A tuple of (summary_metrics, per_category_ap), in which
+            (1) summary_metrics is a dictionary holding:
+              'Precision/mAP': mean average precision over classes averaged over IOU
+                thresholds ranging from .5 to .95 with .05 increments;
+              'Precision/mAP@.50IOU': mean average precision at 50% IOU;
+              'Precision/mAP@.75IOU': mean average precision at 75% IOU;
+              'Precision/mAP (small)': mean average precision for small objects
+                (area < 32^2 pixels);
+              'Precision/mAP (medium)': mean average precision for medium sized
+                objects (32^2 pixels < area < 96^2 pixels);
+              'Precision/mAP (large)': mean average precision for large objects
+                (96^2 pixels < area < 10000^2 pixels);
+              'Recall/AR@1': average recall with 1 detection;
+              'Recall/AR@10': average recall with 10 detections;
+              'Recall/AR@100': average recall with 100 detections;
+              'Recall/AR@100 (small)': average recall for small objects with 100
+                detections;
+              'Recall/AR@100 (medium)': average recall for medium objects with 100
+                detections;
+              'Recall/AR@100 (large)': average recall for large objects with 100
+                detections;
+            and (2) per_category_ap is a dictionary holding category specific results with
+              keys of the form: 'Precision mAP ByCategory/category'
+              (without the supercategory part if no supercategories exist).
+
+          For backward compatibility 'PerformanceByCategory' is included in the
+            output regardless of all_metrics_per_category. If evaluating class-agnostic
+            mode, per_category_ap is an empty dictionary.
+
+        Raises:
+          ValueError: If category_stats does not exist.
+        """
+        self.evaluate()
+        self.accumulate()
+        self.summarize()
+
+        summary_metrics = OrderedDict(
+            [
+                ("Precision/mAP", self.stats[0]),
+                ("Precision/mAP@.50IOU", self.stats[1]),
+                ("Precision/mAP@.75IOU", self.stats[2]),
+                ("Precision/mAP (small)", self.stats[3]),
+                ("Precision/mAP (medium)", self.stats[4]),
+                ("Precision/mAP (large)", self.stats[5]),
+                ("Recall/AR@1", self.stats[6]),
+                ("Recall/AR@10", self.stats[7]),
+                ("Recall/AR@100", self.stats[8]),
+                ("Recall/AR@100 (small)", self.stats[9]),
+                ("Recall/AR@100 (medium)", self.stats[10]),
+                ("Recall/AR@100 (large)", self.stats[11]),
+            ]
+        )
+        if not include_metrics_per_category:
+            return summary_metrics, {}
+        if not hasattr(self, "category_stats"):
+            raise ValueError("Category stats do not exist")
+        per_category_ap = OrderedDict([])
+        if self.GetAgnosticMode():
+            return summary_metrics, per_category_ap
+        for category_index, category_id in enumerate(self.GetCategoryIdList()):
+            category = self.GetCategory(category_id)["name"]
+            # Kept for backward compatilbility
+            # pylint: disable=no-member
+            per_category_ap["PerformanceByCategory/mAP/{}".format(category)] = self.category_stats[0][category_index]
+            if all_metrics_per_category:
+                per_category_ap["Precision mAP ByCategory/{}".format(category)] = self.category_stats[0][category_index]
+                per_category_ap["Precision mAP@.50IOU ByCategory/{}".format(category)] = self.category_stats[1][
+                    category_index
+                ]
+                per_category_ap["Precision mAP@.75IOU ByCategory/{}".format(category)] = self.category_stats[2][
+                    category_index
+                ]
+                per_category_ap["Precision mAP (small) ByCategory/{}".format(category)] = self.category_stats[3][
+                    category_index
+                ]
+                per_category_ap["Precision mAP (medium) ByCategory/{}".format(category)] = self.category_stats[4][
+                    category_index
+                ]
+                per_category_ap["Precision mAP (large) ByCategory/{}".format(category)] = self.category_stats[5][
+                    category_index
+                ]
+                per_category_ap["Recall AR@1 ByCategory/{}".format(category)] = self.category_stats[6][category_index]
+                per_category_ap["Recall AR@10 ByCategory/{}".format(category)] = self.category_stats[7][category_index]
+                per_category_ap["Recall AR@100 ByCategory/{}".format(category)] = self.category_stats[8][category_index]
+                per_category_ap["Recall AR@100 (small) ByCategory/{}".format(category)] = self.category_stats[9][
+                    category_index
+                ]
+                per_category_ap["Recall AR@100 (medium) ByCategory/{}".format(category)] = self.category_stats[10][
+                    category_index
+                ]
+                per_category_ap["Recall AR@100 (large) ByCategory/{}".format(category)] = self.category_stats[11][
+                    category_index
+                ]
+
+        return summary_metrics, per_category_ap
+
+
+def _ConvertBoxToCOCOFormat(box):
+    """Convert a box in [ymin, xmin, ymax, xmax] format to COCO format.
+
+    This is a utility function for converting from our internal
+    [ymin, xmin, ymax, xmax] convention to the convention used by the COCO API
+    i.e., [xmin, ymin, width, height].
+
+    Args:
+      box: a numpy array in format of [ymin, xmin, ymax, xmax]
+
+    Returns:
+      A list of floats, in COCO format, representing [xmin, ymin, width, height]
+    """
+    return [float(box[1]), float(box[0]), float(box[3] - box[1]), float(box[2] - box[0])]
+
+
+def _RleCompress(masks):
+    """Compresses mask using Run-length encoding provided by pycocotools.
+
+    Args:
+      masks: uint8 numpy array of shape [mask_height, mask_width] with values in
+        {0, 1}.
+
+    Returns:
+      A pycocotools Run-length encoding of the mask.
+    """
+    return mask.encode(np.asfortranarray(masks))
+
+
+def ExportSingleImageGroundtruthToCoco(
+    image_id: Union[int, str],
+    next_annotation_id: int,
+    category_id_set: Set[str],
+    groundtruth_boxes: np.array,
+    groundtruth_classes: np.array,
+    groundtruth_masks: Union[np.array, None] = None,
+    groundtruth_is_crowd: Union[np.array, None] = None,
+) -> list:
+    """Export groundtruth of a single image to COCO format.
+
+    This function converts groundtruth detection annotations represented as numpy
+    arrays to dictionaries that can be ingested by the COCO evaluation API. Note
+    that the image_ids provided here must match the ones given to
+    ExportSingleImageDetectionsToCoco. We assume that boxes and classes are in
+    correspondence - that is: groundtruth_boxes[i, :], and
+    groundtruth_classes[i] are associated with the same groundtruth annotation.
+
+    In the exported result, "area" fields are always set to the area of the
+    groundtruth bounding box.
+
+    Args:
+      image_id: a unique image identifier either of type integer or string.
+      next_annotation_id: integer specifying the first id to use for the
+        groundtruth annotations. All annotations are assigned a continuous integer
+        id starting from this value.
+      category_id_set: A set of valid class ids. Groundtruth with classes not in
+        category_id_set are dropped.
+      groundtruth_boxes: numpy array (float32) with shape [num_gt_boxes, 4]
+      groundtruth_classes: numpy array (int) with shape [num_gt_boxes]
+      groundtruth_masks: optional uint8 numpy array of shape [num_detections,
+        image_height, image_width] containing detection_masks.
+      groundtruth_is_crowd: optional numpy array (int) with shape [num_gt_boxes]
+        indicating whether groundtruth boxes are crowd.
+
+    Returns:
+      A list of groundtruth annotations for a single image in the COCO format.
+
+    Raises:
+      ValueError: if (1) groundtruth_boxes and groundtruth_classes do not have the
+        right lengths or (2) if each of the elements inside these lists do not
+        have the correct shapes or (3) if image_ids are not integers
+    """
+    if len(groundtruth_classes.shape) != 1:
+        raise ValueError("groundtruth_classes is " "expected to be of rank 1.")
+    if len(groundtruth_boxes.shape) != 2:
+        raise ValueError("groundtruth_boxes is expected to be of " "rank 2.")
+    if groundtruth_boxes.shape[1] != 4:
+        raise ValueError("groundtruth_boxes should have " "shape[1] == 4.")
+    num_boxes = groundtruth_classes.shape[0]
+    if num_boxes != groundtruth_boxes.shape[0]:
+        raise ValueError(
+            "Corresponding entries in groundtruth_classes, "
+            "and groundtruth_boxes should have "
+            "compatible shapes (i.e., agree on the 0th dimension)."
+            "Classes shape: %d. Boxes shape: %d. Image ID: %s"
+            % (groundtruth_classes.shape[0], groundtruth_boxes.shape[0], image_id)
+        )
+    has_is_crowd = groundtruth_is_crowd is not None
+    if has_is_crowd and len(groundtruth_is_crowd.shape) != 1:
+        raise ValueError("groundtruth_is_crowd is expected to be of rank 1.")
+    groundtruth_list = []
+    for i in range(num_boxes):
+        if groundtruth_classes[i] in category_id_set:
+            iscrowd = groundtruth_is_crowd[i] if has_is_crowd else 0
+            export_dict = {
+                "id": next_annotation_id + i,
+                "image_id": image_id,
+                "category_id": int(groundtruth_classes[i]),
+                "bbox": list(_ConvertBoxToCOCOFormat(groundtruth_boxes[i, :])),
+                "area": float(
+                    (groundtruth_boxes[i, 2] - groundtruth_boxes[i, 0])
+                    * (groundtruth_boxes[i, 3] - groundtruth_boxes[i, 1])
+                ),
+                "iscrowd": iscrowd,
+            }
+            if groundtruth_masks is not None:
+                export_dict["segmentation"] = _RleCompress(groundtruth_masks[i])
+            groundtruth_list.append(export_dict)
+    return groundtruth_list
+
+
+def ExportSingleImageDetectionBoxesToCoco(
+    image_id: Union[int, str],
+    category_id_set: Set[int],
+    detection_boxes: np.array,
+    detection_scores: np.array,
+    detection_classes: np.array,
+) -> list:
+    """Export detections of a single image to COCO format.
+
+    This function converts detections represented as numpy arrays to dictionaries
+    that can be ingested by the COCO evaluation API. Note that the image_ids
+    provided here must match the ones given to the
+    ExporSingleImageDetectionBoxesToCoco. We assume that boxes, and classes are in
+    correspondence - that is: boxes[i, :], and classes[i]
+    are associated with the same groundtruth annotation.
+
+    Args:
+        image_id: unique image identifier either of type integer or string.
+        category_id_set: A set of valid class ids. Detections with classes not in
+          category_id_set are dropped.
+        detection_boxes: float numpy array of shape [num_detections, 4] containing
+          detection boxes.
+        detection_scores: float numpy array of shape [num_detections] containing
+          scored for the detection boxes.
+        detection_classes: integer numpy array of shape [num_detections] containing
+          the classes for detection boxes.
+
+    Returns:
+        A list of detection annotations for a single image in the COCO format.
+
+    Raises:
+        ValueError: if (1) detection_boxes, detection_scores and detection_classes
+        do not have the right lengths or (2) if each of the elements inside these
+        lists do not have the correct shapes or (3) if image_ids are not integers.
+    """
+    if len(detection_classes.shape) != 1 or len(detection_scores.shape) != 1:
+        raise ValueError("All entries in detection_classes and detection_scores" "expected to be of rank 1.")
+    if len(detection_boxes.shape) != 2:
+        raise ValueError("All entries in detection_boxes expected to be of " "rank 2.")
+    if detection_boxes.shape[1] != 4:
+        raise ValueError("All entries in detection_boxes should have " "shape[1] == 4.")
+    num_boxes = detection_classes.shape[0]
+    if not num_boxes == detection_boxes.shape[0] == detection_scores.shape[0]:
+        raise ValueError(
+            "Corresponding entries in detection_classes, "
+            "detection_scores and detection_boxes should have "
+            "compatible shapes (i.e., agree on the 0th dimension). "
+            "Classes shape: %d. Boxes shape: %d. "
+            "Scores shape: %d" % (detection_classes.shape[0], detection_boxes.shape[0], detection_scores.shape[0])
+        )
+    detections_list = []
+    for i in range(num_boxes):
+        if detection_classes[i] in category_id_set:
+            detections_list.append(
+                {
+                    "image_id": image_id,
+                    "category_id": int(detection_classes[i]),
+                    "bbox": list(_ConvertBoxToCOCOFormat(detection_boxes[i, :])),
+                    "score": float(detection_scores[i]),
+                }
+            )
+    return detections_list
+
+
+def ExportSingleImageDetectionMasksToCoco(
+    image_id: Union[str, int],
+    category_id_set: Set[int],
+    detection_masks: np.array,
+    detection_scores: np.array,
+    detection_classes: np.array,
+) -> list:
+    """Export detection masks of a single image to COCO format.
+
+    This function converts detections represented as numpy arrays to dictionaries
+    that can be ingested by the COCO evaluation API. We assume that
+    detection_masks, detection_scores, and detection_classes are in correspondence
+    - that is: detection_masks[i, :], detection_classes[i] and detection_scores[i]
+        are associated with the same annotation.
+
+    Args:
+        image_id: unique image identifier either of type integer or string.
+        category_id_set: A set of valid class ids. Detections with classes not in
+        category_id_set are dropped.
+        detection_masks: uint8 numpy array of shape [num_detections, image_height,
+        image_width] containing detection_masks.
+        detection_scores: float numpy array of shape [num_detections] containing
+        scores for detection masks.
+        detection_classes: integer numpy array of shape [num_detections] containing
+        the classes for detection masks.
+
+    Returns:
+        A list of detection mask annotations for a single image in the COCO format.
+
+    Raises:
+        ValueError: if (1) detection_masks, detection_scores and detection_classes
+        do not have the right lengths or (2) if each of the elements inside these
+        lists do not have the correct shapes or (3) if image_ids are not integers.
+    """
+    if len(detection_classes.shape) != 1 or len(detection_scores.shape) != 1:
+        raise ValueError("All entries in detection_classes and detection_scores" "expected to be of rank 1.")
+    num_boxes = detection_classes.shape[0]
+    if not num_boxes == len(detection_masks) == detection_scores.shape[0]:
+        raise ValueError(
+            "Corresponding entries in detection_classes, "
+            "detection_scores and detection_masks should have "
+            "compatible lengths and shapes "
+            "Classes length: %d.  Masks length: %d. "
+            "Scores length: %d" % (detection_classes.shape[0], len(detection_masks), detection_scores.shape[0])
+        )
+    detections_list = []
+    for i in range(num_boxes):
+        if detection_classes[i] in category_id_set:
+            detections_list.append(
+                {
+                    "image_id": image_id,
+                    "category_id": int(detection_classes[i]),
+                    "segmentation": _RleCompress(detection_masks[i]),
+                    "score": float(detection_scores[i]),
+                }
+            )
+    return detections_list
diff --git a/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/data_process.py b/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/data_process.py
new file mode 100644
index 00000000000..32e55adb3fd
--- /dev/null
+++ b/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/data_process.py
@@ -0,0 +1,655 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2024 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import os
+import cv2
+import collections
+
+import numpy as np
+import tensorflow as tf
+
+from abc import abstractmethod
+from neural_compressor.common import logger
+from neural_compressor.tensorflow.utils.data import default_collate
+
+interpolation_map = {
+    "nearest": cv2.INTER_NEAREST,
+    "bilinear": cv2.INTER_LINEAR,
+    "bicubic": cv2.INTER_CUBIC,
+}
+
+category_map = {
+    1: "person",
+    2: "bicycle",
+    3: "car",
+    4: "motorcycle",
+    5: "airplane",
+    6: "bus",
+    7: "train",
+    8: "truck",
+    9: "boat",
+    10: "traffic light",
+    11: "fire hydrant",
+    13: "stop sign",
+    14: "parking meter",
+    15: "bench",
+    16: "bird",
+    17: "cat",
+    18: "dog",
+    19: "horse",
+    20: "sheep",
+    21: "cow",
+    22: "elephant",
+    23: "bear",
+    24: "zebra",
+    25: "giraffe",
+    27: "backpack",
+    28: "umbrella",
+    31: "handbag",
+    32: "tie",
+    33: "suitcase",
+    34: "frisbee",
+    35: "skis",
+    36: "snowboard",
+    37: "sports ball",
+    38: "kite",
+    39: "baseball bat",
+    40: "baseball glove",
+    41: "skateboard",
+    42: "surfboard",
+    43: "tennis racket",
+    44: "bottle",
+    46: "wine glass",
+    47: "cup",
+    48: "fork",
+    49: "knife",
+    50: "spoon",
+    51: "bowl",
+    52: "banana",
+    53: "apple",
+    54: "sandwich",
+    55: "orange",
+    56: "broccoli",
+    57: "carrot",
+    58: "hot dog",
+    59: "pizza",
+    60: "donut",
+    61: "cake",
+    62: "chair",
+    63: "couch",
+    64: "potted plant",
+    65: "bed",
+    67: "dining table",
+    70: "toilet",
+    72: "tv",
+    73: "laptop",
+    74: "mouse",
+    75: "remote",
+    76: "keyboard",
+    77: "cell phone",
+    78: "microwave",
+    79: "oven",
+    80: "toaster",
+    81: "sink",
+    82: "refrigerator",
+    84: "book",
+    85: "clock",
+    86: "vase",
+    87: "scissors",
+    88: "teddy bear",
+    89: "hair drier",
+    90: "toothbrush",
+}
+
+class ComposeTransform(object):
+    """Composes several transforms together.
+
+    Args:
+        transform_list (list of Transform objects):  list of transforms to compose
+
+    Returns:
+        sample (tuple): tuple of processed image and label
+    """
+
+    def __init__(self, transform_list):
+        """Initialize `ComposeTransform` class."""
+        self.transform_list = transform_list
+
+    def __call__(self, sample):
+        """Call transforms in transform_list."""
+        for transform in self.transform_list:
+            sample = transform(sample)
+        return sample
+    
+
+class ResizeTFTransform(object):
+    """Resize the input image to the given size.
+
+    Args:
+        size (list or int): Size of the result
+        interpolation (str, default='bilinear'):Desired interpolation type,
+                                                support 'bilinear', 'nearest', 'bicubic'
+
+    Returns:
+        tuple of processed image and label
+    """
+
+    def __init__(self, size, interpolation="bilinear"):
+        """Initialize `ResizeTFTransform` class."""
+        if isinstance(size, int):
+            self.size = size, size
+        elif isinstance(size, list):
+            if len(size) == 1:
+                self.size = size[0], size[0]
+            elif len(size) == 2:
+                self.size = size[0], size[1]
+        self.interpolation = interpolation
+
+        if self.interpolation not in ["bilinear", "nearest", "bicubic"]:
+            raise ValueError("Unsupported interpolation type!")
+
+    def __call__(self, sample):
+        """Resize the input image in sample to the given size."""
+        image, label = sample
+        if isinstance(image, tf.Tensor):
+            image = tf.image.resize(image, self.size, method=self.interpolation)
+        else:
+            image = cv2.resize(image, self.size, interpolation=interpolation_map[self.interpolation])
+        return (image, label)
+
+
+class BaseMetric(object):
+    """The base class of Metric."""
+
+    def __init__(self, metric, single_output=False, hvd=None):
+        """Initialize the basic metric.
+
+        Args:
+            metric: The metric class.
+            single_output: Whether the output is single or not, defaults to False.
+            hvd: The Horovod class for distributed training, defaults to None.
+        """
+        self._metric_cls = metric
+        self._single_output = single_output
+        self._hvd = hvd
+
+    def __call__(self, *args, **kwargs):
+        """Evaluate the model predictions, and the reference.
+
+        Returns:
+            The class itself.
+        """
+        self._metric = self._metric_cls(*args, **kwargs)
+        return self
+
+    @abstractmethod
+    def update(self, preds, labels=None, sample_weight=None):
+        """Update the state that need to be evaluated.
+
+        Args:
+            preds: The prediction result.
+            labels: The reference. Defaults to None.
+            sample_weight: The sampling weight. Defaults to None.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def reset(self):
+        """Clear the predictions and labels.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def result(self):
+        """Evaluate the difference between predictions and labels.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @property
+    def metric(self):
+        """Return its metric class.
+
+        Returns:
+            The metric class.
+        """
+        return self._metric
+
+    @property
+    def hvd(self):
+        """Return its hvd class.
+
+        Returns:
+            The hvd class.
+        """
+        return self._hvd
+
+    @hvd.setter
+    def hvd(self, hvd):
+        """Set its hvd.
+
+        Args:
+            hvd: The Horovod class for distributed training.
+        """
+        self._hvd = hvd
+
+
+class COCOmAPv2(BaseMetric):
+    """Compute mean average precision of the detection task."""
+
+    def __init__(
+        self,
+        anno_path=None,
+        iou_thrs="0.5:0.05:0.95",
+        map_points=101,
+        map_key="DetectionBoxes_Precision/mAP",
+        output_index_mapping={"num_detections": -1, "boxes": 0, "scores": 1, "classes": 2},
+    ):
+        """Initialize the metric.
+
+        Args:
+            anno_path: The path of annotation file.
+            iou_thrs: Minimal value for intersection over union that allows to make decision
+              that prediction bounding box is true positive. You can specify one float value
+              between 0 to 1 or string "05:0.05:0.95" for standard COCO thresholds.
+            map_points: The way to calculate mAP. 101 for 101-point interpolated AP, 11 for
+              11-point interpolated AP, 0 for area under PR curve.
+            map_key: The key that mapping to pycocotools COCOeval.
+              Defaults to 'DetectionBoxes_Precision/mAP'.
+            output_index_mapping: The output index mapping.
+              Defaults to {'num_detections':-1, 'boxes':0, 'scores':1, 'classes':2}.
+        """
+        self.output_index_mapping = output_index_mapping
+
+        if anno_path:
+            import os
+            import yaml
+
+            assert os.path.exists(anno_path), "Annotation path does not exists!"
+            with open(anno_path, "r") as f:
+                label_map = yaml.safe_load(f.read())
+            self.category_map_reverse = {k: v for k, v in label_map.items()}
+        else:
+            # label: index
+            self.category_map_reverse = {v: k for k, v in category_map.items()}
+        self.image_ids = []
+        self.ground_truth_list = []
+        self.detection_list = []
+        self.annotation_id = 1
+        self.category_map = category_map
+        self.category_id_set = set([cat for cat in self.category_map])  # index
+        self.iou_thrs = iou_thrs
+        self.map_points = map_points
+        self.map_key = map_key
+
+    def update(self, predicts, labels, sample_weight=None):
+        """Add the predictions and labels.
+
+        Args:
+            predicts: The predictions.
+            labels: The labels corresponding to the predictions.
+            sample_weight: The sample weight. Defaults to None.
+        """
+        from coco_tools import ExportSingleImageDetectionBoxesToCoco, ExportSingleImageGroundtruthToCoco
+
+        detections = []
+        if "num_detections" in self.output_index_mapping and self.output_index_mapping["num_detections"] > -1:
+            for item in zip(*predicts):
+                detection = {}
+                num = int(item[self.output_index_mapping["num_detections"]])
+                detection["boxes"] = np.asarray(item[self.output_index_mapping["boxes"]])[0:num]
+                detection["scores"] = np.asarray(item[self.output_index_mapping["scores"]])[0:num]
+                detection["classes"] = np.asarray(item[self.output_index_mapping["classes"]])[0:num]
+                detections.append(detection)
+        else:
+            for item in zip(*predicts):
+                detection = {}
+                detection["boxes"] = np.asarray(item[self.output_index_mapping["boxes"]])
+                detection["scores"] = np.asarray(item[self.output_index_mapping["scores"]])
+                detection["classes"] = np.asarray(item[self.output_index_mapping["classes"]])
+                detections.append(detection)
+
+        bboxes, str_labels, int_labels, image_ids = labels
+        labels = []
+        if len(int_labels[0]) == 0:
+            for str_label in str_labels:
+                str_label = [x if type(x) == "str" else x.decode("utf-8") for x in str_label]
+                labels.append([self.category_map_reverse[x] for x in str_label])
+        elif len(str_labels[0]) == 0:
+            for int_label in int_labels:
+                labels.append([x for x in int_label])
+
+        for idx, image_id in enumerate(image_ids):
+            image_id = image_id if type(image_id) == "str" else image_id.decode("utf-8")
+            if image_id in self.image_ids:
+                continue
+            self.image_ids.append(image_id)
+
+            ground_truth = {}
+            ground_truth["boxes"] = np.asarray(bboxes[idx])
+            ground_truth["classes"] = np.asarray(labels[idx])
+
+            self.ground_truth_list.extend(
+                ExportSingleImageGroundtruthToCoco(
+                    image_id=image_id,
+                    next_annotation_id=self.annotation_id,
+                    category_id_set=self.category_id_set,
+                    groundtruth_boxes=ground_truth["boxes"],
+                    groundtruth_classes=ground_truth["classes"],
+                )
+            )
+            self.annotation_id += ground_truth["boxes"].shape[0]
+
+            self.detection_list.extend(
+                ExportSingleImageDetectionBoxesToCoco(
+                    image_id=image_id,
+                    category_id_set=self.category_id_set,
+                    detection_boxes=detections[idx]["boxes"],
+                    detection_scores=detections[idx]["scores"],
+                    detection_classes=detections[idx]["classes"],
+                )
+            )
+
+    def reset(self):
+        """Reset the prediction and labels."""
+        self.image_ids = []
+        self.ground_truth_list = []
+        self.detection_list = []
+        self.annotation_id = 1
+
+    def result(self):
+        """Compute mean average precision.
+
+        Returns:
+            The mean average precision score.
+        """
+        from coco_tools import COCOEvalWrapper, COCOWrapper
+
+        if len(self.ground_truth_list) == 0:
+            logger.warning("Sample num during evaluation is 0.")
+            return 0
+        else:
+            groundtruth_dict = {
+                "annotations": self.ground_truth_list,
+                "images": [{"id": image_id} for image_id in self.image_ids],
+                "categories": [{"id": k, "name": v} for k, v in self.category_map.items()],
+            }
+            coco_wrapped_groundtruth = COCOWrapper(groundtruth_dict)
+            coco_wrapped_detections = coco_wrapped_groundtruth.LoadAnnotations(self.detection_list)
+            box_evaluator = COCOEvalWrapper(
+                coco_wrapped_groundtruth,
+                coco_wrapped_detections,
+                agnostic_mode=False,
+                iou_thrs=self.iou_thrs,
+                map_points=self.map_points,
+            )
+            box_metrics, box_per_category_ap = box_evaluator.ComputeMetrics(
+                include_metrics_per_category=False, all_metrics_per_category=False
+            )
+            box_metrics.update(box_per_category_ap)
+            box_metrics = {"DetectionBoxes_" + key: value for key, value in iter(box_metrics.items())}
+
+            return box_metrics[self.map_key]
+
+
+class ParseDecodeCoco:  # pragma: no cover
+    """Helper function for TensorflowModelZooBertDataset.
+
+    Parse the features from sample.
+    """
+
+    def __call__(self, sample):
+        """Parse the sample data.
+
+        Args:
+            sample: Data to be parsed.
+        """
+        # Dense features in Example proto.
+        feature_map = {
+            "image/encoded": tf.compat.v1.FixedLenFeature([], dtype=tf.string, default_value=""),
+            "image/object/class/text": tf.compat.v1.VarLenFeature(dtype=tf.string),
+            "image/object/class/label": tf.compat.v1.VarLenFeature(dtype=tf.int64),
+            "image/source_id": tf.compat.v1.FixedLenFeature([], dtype=tf.string, default_value=""),
+        }
+        sparse_float32 = tf.compat.v1.VarLenFeature(dtype=tf.float32)
+        # Sparse features in Example proto.
+        feature_map.update(
+            {
+                k: sparse_float32
+                for k in [
+                    "image/object/bbox/xmin",
+                    "image/object/bbox/ymin",
+                    "image/object/bbox/xmax",
+                    "image/object/bbox/ymax",
+                ]
+            }
+        )
+
+        features = tf.io.parse_single_example(sample, feature_map)
+
+        xmin = tf.expand_dims(features["image/object/bbox/xmin"].values, 0)
+        ymin = tf.expand_dims(features["image/object/bbox/ymin"].values, 0)
+        xmax = tf.expand_dims(features["image/object/bbox/xmax"].values, 0)
+        ymax = tf.expand_dims(features["image/object/bbox/ymax"].values, 0)
+
+        bbox = tf.concat([ymin, xmin, ymax, xmax], 0)
+        # Force the variable number of bounding boxes into the shape
+        # [1, num_boxes, coords].
+        bbox = tf.expand_dims(bbox, 0)
+        bbox = tf.transpose(bbox, [0, 2, 1])
+
+        encoded_image = features["image/encoded"]
+        image_tensor = tf.image.decode_image(encoded_image, channels=3)
+        image_tensor.set_shape([None, None, 3])
+
+        str_label = features["image/object/class/text"].values
+        int_label = features["image/object/class/label"].values
+        image_id = features["image/source_id"]
+
+        return image_tensor, (bbox[0], str_label, int_label, image_id)
+
+
+class COCORecordDataset(object):
+    """Tensorflow COCO dataset in tf record format.
+
+    Root is a full path to tfrecord file, which contains the file name.
+    Please use Resize transform when batch_size > 1
+
+    Args: root (str): Root directory of dataset.
+          num_cores (int, default=28):The number of input Datasets to interleave from in parallel.
+          transform (transform object, default=None):  transform to process input data.
+          filter (Filter objects, default=None): filter out examples according
+                                                 to specific conditions.
+    """
+
+    def __new__(cls, root, num_cores=28, transform=None, filter=filter):
+        """Build a new object."""
+        record_iterator = tf.compat.v1.python_io.tf_record_iterator(root)
+        example = tf.train.SequenceExample()
+        for element in record_iterator:
+            example.ParseFromString(element)
+            break
+        feature = example.context.feature
+        if (
+            len(feature["image/object/class/text"].bytes_list.value) == 0
+            and len(feature["image/object/class/label"].int64_list.value) == 0
+        ):
+            raise ValueError(
+                "Tfrecord format is incorrect, please refer\
+                'https://github.com/tensorflow/models/blob/master/research/\
+                object_detection/dataset_tools/create_coco_tf_record.py' to\
+                create correct tfrecord"
+            )
+        # pylint: disable=no-name-in-module
+        from tensorflow.python.data.experimental import parallel_interleave
+
+        tfrecord_paths = [root]
+        ds = tf.data.TFRecordDataset.list_files(tfrecord_paths)
+        ds = ds.apply(
+            parallel_interleave(
+                tf.data.TFRecordDataset,
+                cycle_length=num_cores,
+                block_length=5,
+                sloppy=True,
+                buffer_output_elements=10000,
+                prefetch_input_elements=10000,
+            )
+        )
+        if transform is not None:
+            transform.transform_list.insert(0, ParseDecodeCoco())
+        else:
+            transform = ParseDecodeCoco()
+        ds = ds.map(transform, num_parallel_calls=None)
+        if filter is not None:
+            ds = ds.filter(filter)
+        ds = ds.prefetch(buffer_size=1000)
+        return ds
+
+
+class TFDataLoader(object):
+    """Tensorflow dataloader class.
+
+    In tensorflow1.x dataloader is coupled with the graph, but it also support feed_dict
+    method to do session run, this dataloader is designed to satisfy the usage of feed dict
+    in tf1.x. Although it's a general dataloader and can be used in MXNet and PyTorch.
+
+    Args:
+        dataset: obj. wrapper of needed data.
+        batch_size: int. batch size
+    """
+
+    def __init__(self, dataset, batch_size=1, last_batch="rollover"):
+        """Initialize `TFDataDataLoader` class."""
+        self.dataset = dataset
+        self.last_batch = last_batch
+        self.batch_size = batch_size
+        dataset = dataset.batch(batch_size)
+
+    def batch(self, batch_size, last_batch="rollover"):
+        """Dataset return data per batch."""
+        drop_last = False if last_batch == "rollover" else True
+        self.batch_size = batch_size
+        self.dataset = self.dataset.batch(batch_size, drop_last)
+
+    def __iter__(self):
+        """Iterate dataloader."""
+        return self._generate_dataloader(
+            self.dataset,
+            batch_size=self.batch_size,
+            last_batch=self.last_batch,
+        )
+
+    def _generate_dataloader(
+        self,
+        dataset,
+        batch_size=1,
+        last_batch="rollover",
+        collate_fn=None,
+        sampler=None,
+        batch_sampler=None,
+        num_workers=None,
+        pin_memory=None,
+        distributed=False,
+    ):
+        """Yield data."""
+        drop_last = False if last_batch == "rollover" else True
+
+        def check_dynamic_shape(element_spec):
+            if isinstance(element_spec, collections.abc.Sequence):
+                return any([check_dynamic_shape(ele) for ele in element_spec])
+            elif isinstance(element_spec, tf.TensorSpec):
+                return True if element_spec.shape.num_elements() is None else False
+            else:
+                raise ValueError("unrecognized element spec...")
+
+        def squeeze_output(output):
+            if isinstance(output, collections.abc.Sequence):
+                return [squeeze_output(ele) for ele in output]
+            elif isinstance(output, np.ndarray):
+                return np.squeeze(output, axis=0)
+            else:
+                raise ValueError("not supported output format....")
+
+        if tf.executing_eagerly():
+            index = 0
+            outputs = []
+            for iter_tensors in dataset:
+                samples = []
+                iter_inputs, iter_labels = iter_tensors[0], iter_tensors[1]
+                if isinstance(iter_inputs, tf.Tensor):
+                    samples.append(iter_inputs.numpy())
+                else:
+                    samples.append(tuple(iter_input.numpy() for iter_input in iter_inputs))
+                if isinstance(iter_labels, tf.Tensor):
+                    samples.append(iter_labels.numpy())
+                else:
+                    samples.append([np.array(l) for l in iter_labels])
+                index += 1
+                outputs.append(samples)
+                if index == batch_size:
+                    outputs = default_collate(outputs)
+                    yield outputs
+                    outputs = []
+                    index = 0
+            if len(outputs) > 0:
+                outputs = default_collate(outputs)
+                yield outputs
+        else:
+            try_single_batch = check_dynamic_shape(dataset.element_spec)
+            dataset = dataset.batch(1 if try_single_batch else batch_size, drop_last)
+            ds_iterator = tf.compat.v1.data.make_one_shot_iterator(dataset)
+            iter_tensors = ds_iterator.get_next()
+            data_config = tf.compat.v1.ConfigProto()
+            data_config.use_per_session_threads = 1
+            data_config.intra_op_parallelism_threads = 1
+            data_config.inter_op_parallelism_threads = 16
+            data_sess = tf.compat.v1.Session(config=data_config)
+            # pylint: disable=no-name-in-module
+            from tensorflow.python.framework.errors_impl import OutOfRangeError
+
+            while True:
+                if not try_single_batch:
+                    try:
+                        outputs = data_sess.run(iter_tensors)
+                        yield outputs
+                    except OutOfRangeError:
+                        data_sess.close()
+                        return
+                else:
+                    try:
+                        outputs = []
+                        for i in range(0, batch_size):
+                            outputs.append(squeeze_output(data_sess.run(iter_tensors)))
+                        outputs = default_collate(outputs)
+                        yield outputs
+                    except OutOfRangeError:
+                        if len(outputs) == 0:
+                            data_sess.close()
+                            return
+                        else:
+                            outputs = default_collate(outputs)
+                            yield outputs
+                            data_sess.close()
+                            return
diff --git a/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/main.py b/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/main.py
new file mode 100644
index 00000000000..277028064dc
--- /dev/null
+++ b/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/main.py
@@ -0,0 +1,128 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2023 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+#
+
+import time
+
+import numpy as np
+import tensorflow as tf
+
+from __future__ import division
+from argparse import ArgumentParser
+from data_process import(
+    COCOmAPv2,
+    COCORecordDataset,
+    ComposeTransform,
+    ResizeTFTransform,
+    TFDataLoader,
+)
+
+arg_parser = ArgumentParser(description='Parse args')
+
+arg_parser.add_argument('-g',
+                    "--input-graph",
+                    help='Specify the input graph.',
+                    dest='input_graph')
+arg_parser.add_argument('--config', type=str, default='')
+arg_parser.add_argument('--dataset_location', type=str, default='')
+arg_parser.add_argument('--output_model', type=str, default='')
+arg_parser.add_argument('--mode', type=str, default='performance')
+arg_parser.add_argument('--batch_size', type=int, default=10)
+arg_parser.add_argument('--iters', type=int, default=100, dest='iters', help='iterations')
+arg_parser.add_argument('--tune', action='store_true', default=False)
+arg_parser.add_argument('--benchmark', dest='benchmark',
+                    action='store_true', help='run benchmark')
+args = arg_parser.parse_args()
+
+def evaluate(model):
+    """Custom evaluate function to estimate the accuracy of the model.
+
+    Args:
+        model (tf.Graph): The input model graph.
+        
+    Returns:
+        accuracy (float): evaluation result, the larger is better.
+    """
+    from neural_compressor.tensorflow import Model
+    model = Model(model)
+    model.input_tensor_names = ["image_tensor:0"]
+    model.output_tensor_names = ["num_detections:0", "detection_boxes:0", \
+                                    "detection_scores:0", "detection_classes:0"]
+    input_tensor = model.input_tensor
+    output_tensor = model.output_tensor if len(model.output_tensor)>1 else \
+                        model.output_tensor[0]
+    warmup = 5
+    iteration = -1
+    if args.benchmark and args.mode == 'performance':
+        iteration = args.iters
+    metric = COCOmAPv2(output_index_mapping={'num_detections':0, 'boxes':1, 'scores':2, 'classes':3})
+
+    def eval_func(dataloader):
+        latency_list = []
+        for idx, (inputs, labels) in enumerate(dataloader):
+            # dataloader should keep the order and len of inputs same with input_tensor
+            inputs = np.array([inputs])
+            feed_dict = dict(zip(input_tensor, inputs))
+
+            start = time.time()
+            predictions = model.sess.run(output_tensor, feed_dict)
+            end = time.time()
+
+            metric.update(predictions, labels)
+            latency_list.append(end-start)
+            if idx + 1 == iteration:
+                break
+        latency = np.array(latency_list[warmup:]).mean() / args.batch_size
+        return latency
+
+    eval_dataset = COCORecordDataset(root=args.dataset_location, filter=None, \
+            transform=ComposeTransform(transform_list=[ResizeTFTransform(size=600)]))
+    eval_dataloader=TFDataLoader(dataset=eval_dataset, batch_size=args.batch_size)
+    latency = eval_func(eval_dataloader)
+    if args.benchmark and args.mode == 'performance':
+        print("Batch size = {}".format(args.batch_size))
+        print("Latency: {:.3f} ms".format(latency * 1000))
+        print("Throughput: {:.3f} images/sec".format(1. / latency))
+    acc = metric.result()
+    return acc 
+
+def main(_):
+    calib_dataset = COCORecordDataset(root=args.dataset_location, filter=None, \
+            transform=ComposeTransform(transform_list=[ResizeTFTransform(size=600)]))
+    calib_dataloader = TFDataLoader(dataset=calib_dataset, batch_size=args.batch_size)
+
+    if args.tune:
+        from neural_compressor.tensorflow import StaticQuantConfig, quantize_model, Model
+
+        quant_config = StaticQuantConfig()
+        model = Model(args.input_graph)
+        model.input_tensor_names = ['image_tensor']
+        model.output_tensor_names = ["num_detections", "detection_boxes", "detection_scores", "detection_classes"]
+        q_model = quantize_model(model, quant_config, calib_dataloader)
+        q_model.save(args.output_model)
+            
+    if args.benchmark:
+        if args.mode == 'performance':
+            evaluate(args.input_graph)
+        else:
+            accuracy = evaluate(args.input_graph)
+            print('Batch size = %d' % args.batch_size)
+            print("Accuracy: %.5f" % accuracy)
+
+if __name__ == "__main__":
+	tf.compat.v1.app.run()
diff --git a/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/run_benchmark.sh b/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/run_benchmark.sh
new file mode 100644
index 00000000000..6c2115f58ff
--- /dev/null
+++ b/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/run_benchmark.sh
@@ -0,0 +1,51 @@
+#!/bin/bash
+set -x
+
+function main {
+
+  init_params "$@"
+  run_benchmark
+
+}
+
+# init params
+function init_params {
+  batch_size=32
+  iters=100
+  for var in "$@"
+  do
+    case $var in
+      --input_model=*)
+          input_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --mode=*)
+          mode=$(echo $var |cut -f2 -d=)
+      ;;
+      --dataset_location=*)
+          dataset_location=$(echo "$var" |cut -f2 -d=)
+      ;;
+      --batch_size=*)
+          batch_size=$(echo $var |cut -f2 -d=)
+      ;;
+      --iters=*)
+          iters=$(echo $var |cut -f2 -d=)
+      ;;
+    esac
+  done
+
+}
+
+
+# run_tuning
+function run_benchmark {
+
+    python  main.py \
+            --input-graph ${input_model} \
+            --mode ${mode} \
+            --dataset_location "${dataset_location}" \
+            --batch_size ${batch_size} \
+            --iters ${iters} \
+            --benchmark
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/run_quant.sh b/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/run_quant.sh
new file mode 100644
index 00000000000..559d695f768
--- /dev/null
+++ b/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/run_quant.sh
@@ -0,0 +1,41 @@
+#!/bin/bash
+set -x
+
+function main {
+
+  init_params "$@"
+
+  run_tuning
+
+}
+
+# init params
+function init_params {
+
+  for var in "$@"
+  do
+    case $var in
+      --input_model=*)
+          input_model=$(echo "$var" |cut -f2 -d=)
+      ;;
+      --output_model=*)
+          output_model=$(echo "$var" |cut -f2 -d=)
+      ;;
+      --dataset_location=*)
+          dataset_location=$(echo "$var" |cut -f2 -d=)
+      ;;
+    esac
+  done
+
+}
+
+# run_tuning
+function run_tuning {
+  python  main.py \
+          --input-graph "${input_model}" \
+          --output_model "${output_model}" \
+          --dataset_location "${dataset_location}" \
+          --tune
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/README.md b/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/README.md
new file mode 100644
index 00000000000..3091b712180
--- /dev/null
+++ b/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/README.md
@@ -0,0 +1,140 @@
+Step-by-Step
+============
+
+This document is used to list steps of reproducing TensorFlow Object Detection models tuning results. This example can run on Intel CPUs and GPUs.
+
+# Prerequisite
+
+
+## 1. Environment
+Recommend python 3.6 or higher version.
+
+### Install Intel® Neural Compressor
+```shell
+pip install neural-compressor
+```
+
+### Install Intel Tensorflow
+```shell
+pip install intel-tensorflow
+```
+> Note: Validated TensorFlow [Version](/docs/source/installation_guide.md#validated-software-environment).
+
+### Installation Dependency packages
+```shell
+cd examples/3.x_api/tensorflow/object_detection
+pip install -r requirements.txt
+cd mask_rcnn_inception_v2/quantization/ptq
+```
+
+### Install Protocol Buffer Compiler
+
+`Protocol Buffer Compiler` in version higher than 3.0.0 is necessary ingredient for automatic COCO dataset preparation. To install please follow
+[Protobuf installation instructions](https://grpc.io/docs/protoc-installation/#install-using-a-package-manager).
+
+### Install Intel Extension for Tensorflow
+
+#### Quantizing the model on Intel GPU(Mandatory to install ITEX)
+Intel Extension for Tensorflow is mandatory to be installed for quantizing the model on Intel GPUs.
+
+```shell
+pip install --upgrade intel-extension-for-tensorflow[xpu]
+```
+For any more details, please follow the procedure in [install-gpu-drivers](https://github.com/intel/intel-extension-for-tensorflow/blob/main/docs/install/install_for_xpu.md#install-gpu-drivers)
+
+#### Quantizing the model on Intel CPU(Optional to install ITEX)
+Intel Extension for Tensorflow for Intel CPUs is experimental currently. It's not mandatory for quantizing the model on Intel CPUs.
+
+```shell
+pip install --upgrade intel-extension-for-tensorflow[cpu]
+```
+
+> **Note**: 
+> The version compatibility of stock Tensorflow and ITEX can be checked [here](https://github.com/intel/intel-extension-for-tensorflow#compatibility-table). Please make sure you have installed compatible Tensorflow and ITEX.
+
+## 2. Prepare Model
+
+```shell
+wget http://download.tensorflow.org/models/object_detection/mask_rcnn_inception_v2_coco_2018_01_28.tar.gz
+tar -xvzf mask_rcnn_inception_v2_coco_2018_01_28.tar.gz
+```
+
+## 3. Prepare Dataset
+
+### Automatic dataset download
+
+> **_Note: `prepare_dataset.sh` script works with TF version 1.x._**
+
+Run the `prepare_dataset.sh` script located in `examples/3.x_api/tensorflow/object_detection`.
+
+Usage:
+```shell
+cd examples/3.x_api/tensorflow/object_detection/
+. prepare_dataset.sh
+cd mask_rcnn_inception_v2/quantization/ptq
+```
+
+This script will download the *train*, *validation* and *test* COCO datasets. Furthermore it will convert them to
+tensorflow records using the `https://github.com/tensorflow/models.git` dedicated script.
+
+### Manual dataset download
+Download CoCo Dataset from [Official Website](https://cocodataset.org/#download).
+
+
+# Run
+
+Now we support both pb and ckpt formats.
+
+## 1. Quantization
+### For PB format
+  
+  ```shell
+  # The cmd of running mask_rcnn_inception_v2
+  bash run_quant.sh --input_model=./mask_rcnn_inception_v2_coco_2018_01_28/frozen_inference_graph.pb --output_model=./tensorflow-mask_rcnn_inception_v2-tune.pb --dataset_location=/path/to/dataset/coco_val.record
+  ```
+
+### For ckpt format
+  
+  ```shell
+  # The cmd of running mask_rcnn_inception_v2
+  bash run_quant.sh --input_model=./mask_rcnn_inception_v2_coco_2018_01_28/ --output_model=./tensorflow-mask_rcnn_inception_v2-tune.pb --dataset_location=/path/to/dataset/coco_val.record
+  ```
+
+## 2. Benchmark
+  ```shell
+  bash run_benchmark.sh --input_model=./tensorflow-mask_rcnn_inception_v2-tune.pb  --dataset_location=/path/to/dataset/coco_val.record --mode=performance
+  ```
+
+Details of enabling Intel® Neural Compressor on mask_rcnn_inception_v2 for Tensorflow.
+=========================
+
+This is a tutorial of how to enable mask_rcnn_inception_v2 model with Intel® Neural Compressor.
+## User Code Analysis
+User specifies fp32 *model*, calibration dataset *q_dataloader* and a custom *eval_func* which encapsulates the evaluation dataset and metric by itself.
+
+For mask_rcnn_inception_v2, we applied the latter one because our philosophy is to enable the model with minimal changes. Hence we need to make two changes on the original code. The first one is to implement the q_dataloader and make necessary changes to *eval_func*.
+
+### Code update
+
+After prepare step is done, we just need update main.py like below.
+```python
+    if args.tune:
+        from neural_compressor.tensorflow import StaticQuantConfig, quantize_model, Model
+
+        quant_config = StaticQuantConfig()
+        model = Model(args.input_graph)
+        model.input_tensor_names = ['image_tensor']
+        model.output_tensor_names = ["num_detections", "detection_boxes", "detection_scores", "detection_classes"]
+        q_model = quantize_model(model, quant_config, calib_dataloader)
+        q_model.save(args.output_model)
+            
+    if args.benchmark:
+        if args.mode == 'performance':
+            evaluate(args.input_graph)
+        else:
+            accuracy = evaluate(args.input_graph)
+            print('Batch size = %d' % args.batch_size)
+            print("Accuracy: %.5f" % accuracy)
+```
+
+The quantization.fit() function will return a best quantized model during timeout constrain.
diff --git a/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/coco_tools.py b/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/coco_tools.py
new file mode 100644
index 00000000000..2f9369798df
--- /dev/null
+++ b/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/coco_tools.py
@@ -0,0 +1,694 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2021 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Wrappers for third party pycocotools to be used within object_detection.
+
+Note that nothing in this file is tensorflow related and thus cannot
+be called directly as a slim metric, for example.
+
+TODO(jonathanhuang): wrap as a slim metric in metrics.py
+
+
+Usage example: given a set of images with ids in the list image_ids
+and corresponding lists of numpy arrays encoding groundtruth (boxes and classes)
+and detections (boxes, scores and classes), where elements of each list
+correspond to detections/annotations of a single image,
+then evaluation (in multi-class mode) can be invoked as follows:
+
+  groundtruth_dict = coco_tools.ExportGroundtruthToCOCO(
+      image_ids, groundtruth_boxes_list, groundtruth_classes_list,
+      max_num_classes, output_path=None)
+  detections_list = coco_tools.ExportDetectionsToCOCO(
+      image_ids, detection_boxes_list, detection_scores_list,
+      detection_classes_list, output_path=None)
+  groundtruth = coco_tools.COCOWrapper(groundtruth_dict)
+  detections = groundtruth.LoadAnnotations(detections_list)
+  evaluator = coco_tools.COCOEvalWrapper(groundtruth, detections,
+                                         agnostic_mode=False)
+  metrics = evaluator.ComputeMetrics()
+"""
+
+import copy
+import time
+from collections import OrderedDict
+from typing import Any, Dict, List, Set, Union
+
+import numpy as np
+from pycocotools import coco, cocoeval, mask
+
+from neural_compressor.utils import logger
+
+
+class COCOWrapper(coco.COCO):
+    """Wrapper for the pycocotools COCO class.
+
+    Attributes:
+      dataset: a dictionary holding bounding box annotations in the COCO format.
+      detection_type: type of detections being wrapped. Can be one of ['bbox',
+        'segmentation']
+    """
+
+    def __init__(self, dataset: Dict[str, Any], detection_type: str = "bbox"):
+        """Construct a COCOWrapper.
+
+        See http://mscoco.org/dataset/#format for a description of the format.
+        By default, the coco.COCO class constructor reads from a JSON file.
+        This function duplicates the same behavior but loads from a dictionary,
+        allowing us to perform evaluation without writing to external storage.
+
+        Args:
+          dataset: a dictionary holding bounding box annotations in the COCO format.
+          detection_type: type of detections being wrapped. Can be one of ['bbox',
+            'segmentation']
+
+        Raises:
+          ValueError: if detection_type is unsupported.
+        """
+        supported_detection_types = ["bbox", "segmentation"]
+        if detection_type not in supported_detection_types:
+            raise ValueError(
+                "Unsupported detection type: {}. "
+                "Supported values are: {}".format(detection_type, supported_detection_types)
+            )
+        self._detection_type = detection_type
+        coco.COCO.__init__(self)
+        self.dataset = dataset
+        self.createIndex()
+
+    def LoadAnnotations(self, annotations: list) -> coco.COCO:
+        """Load annotations dictionary into COCO datastructure.
+
+        See http://mscoco.org/dataset/#format for a description of the annotations
+        format.  As above, this function replicates the default behavior of the API
+        but does not require writing to external storage.
+
+        Args:
+          annotations: python list holding object detection results where each
+            detection is encoded as a dict with required keys ['image_id',
+            'category_id', 'score'] and one of ['bbox', 'segmentation'] based on
+            `detection_type`.
+
+        Returns:
+          a coco.COCO datastructure holding object detection annotations results
+
+        Raises:
+          ValueError: if (1) annotations is not a list or annotations do not
+            correspond to the images contained in self.
+        """
+        results = coco.COCO()
+        results.dataset["images"] = [img for img in self.dataset["images"]]
+
+        logger.info("Load and prepare annotation results.")
+        tic = time.time()
+
+        if not isinstance(annotations, list):
+            raise ValueError("annotations is not a list of objects")
+        annotation_img_ids = [ann["image_id"] for ann in annotations]
+        if set(annotation_img_ids) != (set(annotation_img_ids) & set(self.getImgIds())):
+            raise ValueError("Results do not correspond to current coco set")
+        results.dataset["categories"] = copy.deepcopy(self.dataset["categories"])
+        if self._detection_type == "bbox":
+            for idx, ann in enumerate(annotations):
+                bb = ann["bbox"]
+                ann["area"] = bb[2] * bb[3]
+                ann["id"] = idx + 1
+                ann["iscrowd"] = 0
+        elif self._detection_type == "segmentation":
+            for idx, ann in enumerate(annotations):
+                ann["area"] = mask.area(ann["segmentation"])
+                ann["bbox"] = mask.toBbox(ann["segmentation"])
+                ann["id"] = idx + 1
+                ann["iscrowd"] = 0
+        logger.info("DONE (t=%0.2fs)", (time.time() - tic))
+
+        results.dataset["annotations"] = annotations
+        results.createIndex()
+        return results
+
+
+class COCOEvalWrapper(cocoeval.COCOeval):
+    """Wrapper for the pycocotools COCOeval class.
+
+    To evaluate, create two objects (groundtruth_dict and detections_list)
+    using the conventions listed at http://mscoco.org/dataset/#format.
+    Then call evaluation as follows:
+
+      groundtruth = coco_tools.COCOWrapper(groundtruth_dict)
+      detections = groundtruth.LoadAnnotations(detections_list)
+      evaluator = coco_tools.COCOEvalWrapper(groundtruth, detections,
+                                             agnostic_mode=False)
+      metrics = evaluator.ComputeMetrics()
+    """
+
+    def __init__(
+        self,
+        groundtruth: coco.COCO = None,
+        detections: coco.COCO = None,
+        agnostic_mode=False,
+        iou_type: str = "bbox",
+        iou_thrs: Union[str, float] = None,
+        map_points=None,
+    ):
+        """Construct a COCOEvalWrapper.
+
+        Note that for the area-based metrics to be meaningful, detection and
+        groundtruth boxes must be in image coordinates measured in pixels.
+
+        Args:
+          groundtruth: a coco.COCO (or coco_tools.COCOWrapper) object holding
+            groundtruth annotations
+          detections: a coco.COCO (or coco_tools.COCOWrapper) object holding
+            detections
+          agnostic_mode: boolean (default: False).  If True, evaluation ignores
+            class labels, treating all detections as proposals.
+          iou_thrs: Minimal value for intersection over union that allows to
+                    make decision that prediction bounding box is true positive.
+                    You can specify one float value between 0 to 1 or
+                    string "05:0.05:0.95" for standard COCO thresholds.
+          iou_type: IOU type to use for evaluation. Supports `bbox` or `segm`.
+          map_points: The way to calculate mAP. 101 for 101-point interpolated AP, 11 for
+                          11-point interpolated AP, 0 for area under PR curve.
+        """
+        cocoeval.COCOeval.__init__(self, groundtruth, detections, iouType=iou_type)
+        if agnostic_mode:
+            self.params.useCats = 0
+        if iou_thrs == "0.5:0.05:0.95":
+            self.params.iouThrs = np.linspace(0.5, 0.95, int(np.round((0.95 - 0.5) / 0.05)) + 1, endpoint=True)
+        elif isinstance(iou_thrs, float):
+            self.params.iouThrs = [iou_thrs]
+
+        if map_points == 101:
+            self.params.recThrs = np.linspace(0.0, 1.00, int(np.round((1.00 - 0.0) / 0.01)) + 1, endpoint=True)
+        if map_points == 11:
+            self.params.recThrs = np.linspace(0.0, 1.00, int(np.round((1.00 - 0.0) / 0.1)) + 1, endpoint=True)
+        if map_points == 0:
+            self.params.recThrs = [-1]
+
+    def GetCategory(self, category_id: int) -> dict:
+        """Fetch dictionary holding category information given category id.
+
+        Args:
+          category_id: integer id
+
+        Returns:
+          dictionary holding 'id', 'name'.
+        """
+        return self.cocoGt.cats[category_id]
+
+    def GetAgnosticMode(self) -> bool:
+        """Return whether COCO Eval is configured to evaluate in agnostic mode."""
+        return self.params.useCats == 0
+
+    def GetCategoryIdList(self) -> List[int]:
+        """Return the list of IDs of all valid categories."""
+        return self.params.catIds
+
+    def accumulate(self, p: cocoeval.Params = None):
+        """Accumulate evaluation results per image and store it to self.eval.
+
+        Args:
+          p: input params for evaluation
+        """
+        print("Accumulating evaluation results...")
+        tic = time.time()
+        if not self.evalImgs:
+            print("Please run evaluate() first")
+        # allows input customized parameters
+        if p is None:
+            p = self.params
+        p.catIds = p.catIds if p.useCats == 1 else [-1]
+        T = len(p.iouThrs)
+        R = len(p.recThrs)
+        K = len(p.catIds) if p.useCats else 1
+        A = len(p.areaRng)
+        M = len(p.maxDets)
+        precision = -np.ones((T, R, K, A, M))  # -1 for the precision of absent categories
+        recall = -np.ones((T, K, A, M))
+        scores = -np.ones((T, R, K, A, M))
+
+        # create dictionary for future indexing
+        _pe = self._paramsEval
+        print("-pe", _pe)
+        catIds = _pe.catIds if _pe.useCats else [-1]
+        setK = set(catIds)
+        setA = set(map(tuple, _pe.areaRng))
+        setM = set(_pe.maxDets)
+        setI = set(_pe.imgIds)
+        # get inds to evaluate
+        k_list = [n for n, k in enumerate(p.catIds) if k in setK]
+        m_list = [m for n, m in enumerate(p.maxDets) if m in setM]
+        a_list = [n for n, a in enumerate(map(lambda x: tuple(x), p.areaRng)) if a in setA]
+        i_list = [n for n, i in enumerate(p.imgIds) if i in setI]
+        I0 = len(_pe.imgIds)
+        A0 = len(_pe.areaRng)
+        # retrieve E at each category, area range, and max number of detections
+        for k, k0 in enumerate(k_list):
+            Nk = k0 * A0 * I0
+            for a, a0 in enumerate(a_list):
+                Na = a0 * I0
+                for m, maxDet in enumerate(m_list):
+                    E = [self.evalImgs[Nk + Na + i] for i in i_list]
+                    E = [e for e in E if e is not None]
+                    if len(E) == 0:
+                        continue
+                    dtScores = np.concatenate([e["dtScores"][0:maxDet] for e in E])
+
+                    # different sorting method generates slightly different results.
+                    # mergesort is used to be consistent as Matlab implementation.
+                    inds = np.argsort(-dtScores, kind="mergesort")
+                    dtScoresSorted = dtScores[inds]
+
+                    dtm = np.concatenate([e["dtMatches"][:, 0:maxDet] for e in E], axis=1)[:, inds]
+                    dtIg = np.concatenate([e["dtIgnore"][:, 0:maxDet] for e in E], axis=1)[:, inds]
+                    gtIg = np.concatenate([e["gtIgnore"] for e in E])
+                    npig = np.count_nonzero(gtIg == 0)
+                    if npig == 0:
+                        continue
+                    tps = np.logical_and(dtm, np.logical_not(dtIg))
+                    fps = np.logical_and(np.logical_not(dtm), np.logical_not(dtIg))
+
+                    tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float32)
+                    fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float32)
+                    for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)):
+                        tp = np.array(tp)
+                        fp = np.array(fp)
+                        nd = len(tp)
+                        rc = tp / npig
+                        pr = tp / (fp + tp + np.spacing(1))
+
+                        # calculate precision
+                        if R == 1:
+                            rc = np.concatenate(([0.0], rc, [1.0]))
+                            pr = np.concatenate(([0.0], pr, [0.0]))
+
+                            # compute the precision envelope
+                            for i in range(pr.size - 1, 0, -1):
+                                pr[i - 1] = np.maximum(pr[i - 1], pr[i])
+
+                            # to calculate area under PR curve, look for points
+                            # where X axis (recall) changes value
+                            change_point = np.where(rc[1:] != rc[:-1])[0]
+                            # and sum (\Delta recall) * recall
+                            res = np.sum((rc[change_point + 1] - rc[change_point]) * pr[change_point + 1])
+                            precision[t, :, k, a, m] = np.array([res])
+                        else:
+                            q = np.zeros((R,))
+
+                            # numpy is slow without cython optimization for accessing elements
+                            # use python array gets significant speed improvement
+                            pr = pr.tolist()
+                            q = q.tolist()
+
+                            for i in range(nd - 1, 0, -1):
+                                if pr[i] > pr[i - 1]:
+                                    pr[i - 1] = pr[i]
+
+                            inds = np.searchsorted(rc, p.recThrs, side="left")
+                            try:
+                                for ri, pi in enumerate(inds):
+                                    q[ri] = pr[pi]
+                            except:
+                                pass
+                            precision[t, :, k, a, m] = np.array(q)
+
+                        # calculate recall
+                        if nd:
+                            recall[t, k, a, m] = rc[-1]
+                        else:
+                            recall[t, k, a, m] = 0
+
+                        # calculate score
+                        ss = np.zeros((R,))
+                        inds = np.searchsorted(rc, p.recThrs, side="left")
+                        try:
+                            for ri, pi in enumerate(inds):
+                                ss[ri] = dtScoresSorted[pi]
+                        except:
+                            pass
+                        scores[t, :, k, a, m] = np.array(ss)
+        # exit(0)
+        self.eval = {
+            "params": p,
+            "counts": [T, R, K, A, M],
+            "precision": precision,
+            "recall": recall,
+            "scores": scores,
+        }
+        toc = time.time()
+        print("DONE (t={:0.2f}s).".format(toc - tic))
+
+    def ComputeMetrics(
+        self, include_metrics_per_category: bool = False, all_metrics_per_category: bool = False
+    ):  # pragma: no cover
+        """Compute detection metrics.
+
+        Args:
+          include_metrics_per_category: Whether include metrics per category.
+          all_metrics_per_category: Whether include all the summery metrics for
+            each category in per_category_ap. Be careful with setting it to true if
+            you have more than handful of categories, because it will pollute
+            your mldash.
+
+        Returns:
+          A tuple of (summary_metrics, per_category_ap), in which
+            (1) summary_metrics is a dictionary holding:
+              'Precision/mAP': mean average precision over classes averaged over IOU
+                thresholds ranging from .5 to .95 with .05 increments;
+              'Precision/mAP@.50IOU': mean average precision at 50% IOU;
+              'Precision/mAP@.75IOU': mean average precision at 75% IOU;
+              'Precision/mAP (small)': mean average precision for small objects
+                (area < 32^2 pixels);
+              'Precision/mAP (medium)': mean average precision for medium sized
+                objects (32^2 pixels < area < 96^2 pixels);
+              'Precision/mAP (large)': mean average precision for large objects
+                (96^2 pixels < area < 10000^2 pixels);
+              'Recall/AR@1': average recall with 1 detection;
+              'Recall/AR@10': average recall with 10 detections;
+              'Recall/AR@100': average recall with 100 detections;
+              'Recall/AR@100 (small)': average recall for small objects with 100
+                detections;
+              'Recall/AR@100 (medium)': average recall for medium objects with 100
+                detections;
+              'Recall/AR@100 (large)': average recall for large objects with 100
+                detections;
+            and (2) per_category_ap is a dictionary holding category specific results with
+              keys of the form: 'Precision mAP ByCategory/category'
+              (without the supercategory part if no supercategories exist).
+
+          For backward compatibility 'PerformanceByCategory' is included in the
+            output regardless of all_metrics_per_category. If evaluating class-agnostic
+            mode, per_category_ap is an empty dictionary.
+
+        Raises:
+          ValueError: If category_stats does not exist.
+        """
+        self.evaluate()
+        self.accumulate()
+        self.summarize()
+
+        summary_metrics = OrderedDict(
+            [
+                ("Precision/mAP", self.stats[0]),
+                ("Precision/mAP@.50IOU", self.stats[1]),
+                ("Precision/mAP@.75IOU", self.stats[2]),
+                ("Precision/mAP (small)", self.stats[3]),
+                ("Precision/mAP (medium)", self.stats[4]),
+                ("Precision/mAP (large)", self.stats[5]),
+                ("Recall/AR@1", self.stats[6]),
+                ("Recall/AR@10", self.stats[7]),
+                ("Recall/AR@100", self.stats[8]),
+                ("Recall/AR@100 (small)", self.stats[9]),
+                ("Recall/AR@100 (medium)", self.stats[10]),
+                ("Recall/AR@100 (large)", self.stats[11]),
+            ]
+        )
+        if not include_metrics_per_category:
+            return summary_metrics, {}
+        if not hasattr(self, "category_stats"):
+            raise ValueError("Category stats do not exist")
+        per_category_ap = OrderedDict([])
+        if self.GetAgnosticMode():
+            return summary_metrics, per_category_ap
+        for category_index, category_id in enumerate(self.GetCategoryIdList()):
+            category = self.GetCategory(category_id)["name"]
+            # Kept for backward compatilbility
+            # pylint: disable=no-member
+            per_category_ap["PerformanceByCategory/mAP/{}".format(category)] = self.category_stats[0][category_index]
+            if all_metrics_per_category:
+                per_category_ap["Precision mAP ByCategory/{}".format(category)] = self.category_stats[0][category_index]
+                per_category_ap["Precision mAP@.50IOU ByCategory/{}".format(category)] = self.category_stats[1][
+                    category_index
+                ]
+                per_category_ap["Precision mAP@.75IOU ByCategory/{}".format(category)] = self.category_stats[2][
+                    category_index
+                ]
+                per_category_ap["Precision mAP (small) ByCategory/{}".format(category)] = self.category_stats[3][
+                    category_index
+                ]
+                per_category_ap["Precision mAP (medium) ByCategory/{}".format(category)] = self.category_stats[4][
+                    category_index
+                ]
+                per_category_ap["Precision mAP (large) ByCategory/{}".format(category)] = self.category_stats[5][
+                    category_index
+                ]
+                per_category_ap["Recall AR@1 ByCategory/{}".format(category)] = self.category_stats[6][category_index]
+                per_category_ap["Recall AR@10 ByCategory/{}".format(category)] = self.category_stats[7][category_index]
+                per_category_ap["Recall AR@100 ByCategory/{}".format(category)] = self.category_stats[8][category_index]
+                per_category_ap["Recall AR@100 (small) ByCategory/{}".format(category)] = self.category_stats[9][
+                    category_index
+                ]
+                per_category_ap["Recall AR@100 (medium) ByCategory/{}".format(category)] = self.category_stats[10][
+                    category_index
+                ]
+                per_category_ap["Recall AR@100 (large) ByCategory/{}".format(category)] = self.category_stats[11][
+                    category_index
+                ]
+
+        return summary_metrics, per_category_ap
+
+
+def _ConvertBoxToCOCOFormat(box):
+    """Convert a box in [ymin, xmin, ymax, xmax] format to COCO format.
+
+    This is a utility function for converting from our internal
+    [ymin, xmin, ymax, xmax] convention to the convention used by the COCO API
+    i.e., [xmin, ymin, width, height].
+
+    Args:
+      box: a numpy array in format of [ymin, xmin, ymax, xmax]
+
+    Returns:
+      A list of floats, in COCO format, representing [xmin, ymin, width, height]
+    """
+    return [float(box[1]), float(box[0]), float(box[3] - box[1]), float(box[2] - box[0])]
+
+
+def _RleCompress(masks):
+    """Compresses mask using Run-length encoding provided by pycocotools.
+
+    Args:
+      masks: uint8 numpy array of shape [mask_height, mask_width] with values in
+        {0, 1}.
+
+    Returns:
+      A pycocotools Run-length encoding of the mask.
+    """
+    return mask.encode(np.asfortranarray(masks))
+
+
+def ExportSingleImageGroundtruthToCoco(
+    image_id: Union[int, str],
+    next_annotation_id: int,
+    category_id_set: Set[str],
+    groundtruth_boxes: np.array,
+    groundtruth_classes: np.array,
+    groundtruth_masks: Union[np.array, None] = None,
+    groundtruth_is_crowd: Union[np.array, None] = None,
+) -> list:
+    """Export groundtruth of a single image to COCO format.
+
+    This function converts groundtruth detection annotations represented as numpy
+    arrays to dictionaries that can be ingested by the COCO evaluation API. Note
+    that the image_ids provided here must match the ones given to
+    ExportSingleImageDetectionsToCoco. We assume that boxes and classes are in
+    correspondence - that is: groundtruth_boxes[i, :], and
+    groundtruth_classes[i] are associated with the same groundtruth annotation.
+
+    In the exported result, "area" fields are always set to the area of the
+    groundtruth bounding box.
+
+    Args:
+      image_id: a unique image identifier either of type integer or string.
+      next_annotation_id: integer specifying the first id to use for the
+        groundtruth annotations. All annotations are assigned a continuous integer
+        id starting from this value.
+      category_id_set: A set of valid class ids. Groundtruth with classes not in
+        category_id_set are dropped.
+      groundtruth_boxes: numpy array (float32) with shape [num_gt_boxes, 4]
+      groundtruth_classes: numpy array (int) with shape [num_gt_boxes]
+      groundtruth_masks: optional uint8 numpy array of shape [num_detections,
+        image_height, image_width] containing detection_masks.
+      groundtruth_is_crowd: optional numpy array (int) with shape [num_gt_boxes]
+        indicating whether groundtruth boxes are crowd.
+
+    Returns:
+      A list of groundtruth annotations for a single image in the COCO format.
+
+    Raises:
+      ValueError: if (1) groundtruth_boxes and groundtruth_classes do not have the
+        right lengths or (2) if each of the elements inside these lists do not
+        have the correct shapes or (3) if image_ids are not integers
+    """
+    if len(groundtruth_classes.shape) != 1:
+        raise ValueError("groundtruth_classes is " "expected to be of rank 1.")
+    if len(groundtruth_boxes.shape) != 2:
+        raise ValueError("groundtruth_boxes is expected to be of " "rank 2.")
+    if groundtruth_boxes.shape[1] != 4:
+        raise ValueError("groundtruth_boxes should have " "shape[1] == 4.")
+    num_boxes = groundtruth_classes.shape[0]
+    if num_boxes != groundtruth_boxes.shape[0]:
+        raise ValueError(
+            "Corresponding entries in groundtruth_classes, "
+            "and groundtruth_boxes should have "
+            "compatible shapes (i.e., agree on the 0th dimension)."
+            "Classes shape: %d. Boxes shape: %d. Image ID: %s"
+            % (groundtruth_classes.shape[0], groundtruth_boxes.shape[0], image_id)
+        )
+    has_is_crowd = groundtruth_is_crowd is not None
+    if has_is_crowd and len(groundtruth_is_crowd.shape) != 1:
+        raise ValueError("groundtruth_is_crowd is expected to be of rank 1.")
+    groundtruth_list = []
+    for i in range(num_boxes):
+        if groundtruth_classes[i] in category_id_set:
+            iscrowd = groundtruth_is_crowd[i] if has_is_crowd else 0
+            export_dict = {
+                "id": next_annotation_id + i,
+                "image_id": image_id,
+                "category_id": int(groundtruth_classes[i]),
+                "bbox": list(_ConvertBoxToCOCOFormat(groundtruth_boxes[i, :])),
+                "area": float(
+                    (groundtruth_boxes[i, 2] - groundtruth_boxes[i, 0])
+                    * (groundtruth_boxes[i, 3] - groundtruth_boxes[i, 1])
+                ),
+                "iscrowd": iscrowd,
+            }
+            if groundtruth_masks is not None:
+                export_dict["segmentation"] = _RleCompress(groundtruth_masks[i])
+            groundtruth_list.append(export_dict)
+    return groundtruth_list
+
+
+def ExportSingleImageDetectionBoxesToCoco(
+    image_id: Union[int, str],
+    category_id_set: Set[int],
+    detection_boxes: np.array,
+    detection_scores: np.array,
+    detection_classes: np.array,
+) -> list:
+    """Export detections of a single image to COCO format.
+
+    This function converts detections represented as numpy arrays to dictionaries
+    that can be ingested by the COCO evaluation API. Note that the image_ids
+    provided here must match the ones given to the
+    ExporSingleImageDetectionBoxesToCoco. We assume that boxes, and classes are in
+    correspondence - that is: boxes[i, :], and classes[i]
+    are associated with the same groundtruth annotation.
+
+    Args:
+        image_id: unique image identifier either of type integer or string.
+        category_id_set: A set of valid class ids. Detections with classes not in
+          category_id_set are dropped.
+        detection_boxes: float numpy array of shape [num_detections, 4] containing
+          detection boxes.
+        detection_scores: float numpy array of shape [num_detections] containing
+          scored for the detection boxes.
+        detection_classes: integer numpy array of shape [num_detections] containing
+          the classes for detection boxes.
+
+    Returns:
+        A list of detection annotations for a single image in the COCO format.
+
+    Raises:
+        ValueError: if (1) detection_boxes, detection_scores and detection_classes
+        do not have the right lengths or (2) if each of the elements inside these
+        lists do not have the correct shapes or (3) if image_ids are not integers.
+    """
+    if len(detection_classes.shape) != 1 or len(detection_scores.shape) != 1:
+        raise ValueError("All entries in detection_classes and detection_scores" "expected to be of rank 1.")
+    if len(detection_boxes.shape) != 2:
+        raise ValueError("All entries in detection_boxes expected to be of " "rank 2.")
+    if detection_boxes.shape[1] != 4:
+        raise ValueError("All entries in detection_boxes should have " "shape[1] == 4.")
+    num_boxes = detection_classes.shape[0]
+    if not num_boxes == detection_boxes.shape[0] == detection_scores.shape[0]:
+        raise ValueError(
+            "Corresponding entries in detection_classes, "
+            "detection_scores and detection_boxes should have "
+            "compatible shapes (i.e., agree on the 0th dimension). "
+            "Classes shape: %d. Boxes shape: %d. "
+            "Scores shape: %d" % (detection_classes.shape[0], detection_boxes.shape[0], detection_scores.shape[0])
+        )
+    detections_list = []
+    for i in range(num_boxes):
+        if detection_classes[i] in category_id_set:
+            detections_list.append(
+                {
+                    "image_id": image_id,
+                    "category_id": int(detection_classes[i]),
+                    "bbox": list(_ConvertBoxToCOCOFormat(detection_boxes[i, :])),
+                    "score": float(detection_scores[i]),
+                }
+            )
+    return detections_list
+
+
+def ExportSingleImageDetectionMasksToCoco(
+    image_id: Union[str, int],
+    category_id_set: Set[int],
+    detection_masks: np.array,
+    detection_scores: np.array,
+    detection_classes: np.array,
+) -> list:
+    """Export detection masks of a single image to COCO format.
+
+    This function converts detections represented as numpy arrays to dictionaries
+    that can be ingested by the COCO evaluation API. We assume that
+    detection_masks, detection_scores, and detection_classes are in correspondence
+    - that is: detection_masks[i, :], detection_classes[i] and detection_scores[i]
+        are associated with the same annotation.
+
+    Args:
+        image_id: unique image identifier either of type integer or string.
+        category_id_set: A set of valid class ids. Detections with classes not in
+        category_id_set are dropped.
+        detection_masks: uint8 numpy array of shape [num_detections, image_height,
+        image_width] containing detection_masks.
+        detection_scores: float numpy array of shape [num_detections] containing
+        scores for detection masks.
+        detection_classes: integer numpy array of shape [num_detections] containing
+        the classes for detection masks.
+
+    Returns:
+        A list of detection mask annotations for a single image in the COCO format.
+
+    Raises:
+        ValueError: if (1) detection_masks, detection_scores and detection_classes
+        do not have the right lengths or (2) if each of the elements inside these
+        lists do not have the correct shapes or (3) if image_ids are not integers.
+    """
+    if len(detection_classes.shape) != 1 or len(detection_scores.shape) != 1:
+        raise ValueError("All entries in detection_classes and detection_scores" "expected to be of rank 1.")
+    num_boxes = detection_classes.shape[0]
+    if not num_boxes == len(detection_masks) == detection_scores.shape[0]:
+        raise ValueError(
+            "Corresponding entries in detection_classes, "
+            "detection_scores and detection_masks should have "
+            "compatible lengths and shapes "
+            "Classes length: %d.  Masks length: %d. "
+            "Scores length: %d" % (detection_classes.shape[0], len(detection_masks), detection_scores.shape[0])
+        )
+    detections_list = []
+    for i in range(num_boxes):
+        if detection_classes[i] in category_id_set:
+            detections_list.append(
+                {
+                    "image_id": image_id,
+                    "category_id": int(detection_classes[i]),
+                    "segmentation": _RleCompress(detection_masks[i]),
+                    "score": float(detection_scores[i]),
+                }
+            )
+    return detections_list
diff --git a/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/data_process.py b/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/data_process.py
new file mode 100644
index 00000000000..32e55adb3fd
--- /dev/null
+++ b/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/data_process.py
@@ -0,0 +1,655 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2024 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import os
+import cv2
+import collections
+
+import numpy as np
+import tensorflow as tf
+
+from abc import abstractmethod
+from neural_compressor.common import logger
+from neural_compressor.tensorflow.utils.data import default_collate
+
+interpolation_map = {
+    "nearest": cv2.INTER_NEAREST,
+    "bilinear": cv2.INTER_LINEAR,
+    "bicubic": cv2.INTER_CUBIC,
+}
+
+category_map = {
+    1: "person",
+    2: "bicycle",
+    3: "car",
+    4: "motorcycle",
+    5: "airplane",
+    6: "bus",
+    7: "train",
+    8: "truck",
+    9: "boat",
+    10: "traffic light",
+    11: "fire hydrant",
+    13: "stop sign",
+    14: "parking meter",
+    15: "bench",
+    16: "bird",
+    17: "cat",
+    18: "dog",
+    19: "horse",
+    20: "sheep",
+    21: "cow",
+    22: "elephant",
+    23: "bear",
+    24: "zebra",
+    25: "giraffe",
+    27: "backpack",
+    28: "umbrella",
+    31: "handbag",
+    32: "tie",
+    33: "suitcase",
+    34: "frisbee",
+    35: "skis",
+    36: "snowboard",
+    37: "sports ball",
+    38: "kite",
+    39: "baseball bat",
+    40: "baseball glove",
+    41: "skateboard",
+    42: "surfboard",
+    43: "tennis racket",
+    44: "bottle",
+    46: "wine glass",
+    47: "cup",
+    48: "fork",
+    49: "knife",
+    50: "spoon",
+    51: "bowl",
+    52: "banana",
+    53: "apple",
+    54: "sandwich",
+    55: "orange",
+    56: "broccoli",
+    57: "carrot",
+    58: "hot dog",
+    59: "pizza",
+    60: "donut",
+    61: "cake",
+    62: "chair",
+    63: "couch",
+    64: "potted plant",
+    65: "bed",
+    67: "dining table",
+    70: "toilet",
+    72: "tv",
+    73: "laptop",
+    74: "mouse",
+    75: "remote",
+    76: "keyboard",
+    77: "cell phone",
+    78: "microwave",
+    79: "oven",
+    80: "toaster",
+    81: "sink",
+    82: "refrigerator",
+    84: "book",
+    85: "clock",
+    86: "vase",
+    87: "scissors",
+    88: "teddy bear",
+    89: "hair drier",
+    90: "toothbrush",
+}
+
+class ComposeTransform(object):
+    """Composes several transforms together.
+
+    Args:
+        transform_list (list of Transform objects):  list of transforms to compose
+
+    Returns:
+        sample (tuple): tuple of processed image and label
+    """
+
+    def __init__(self, transform_list):
+        """Initialize `ComposeTransform` class."""
+        self.transform_list = transform_list
+
+    def __call__(self, sample):
+        """Call transforms in transform_list."""
+        for transform in self.transform_list:
+            sample = transform(sample)
+        return sample
+    
+
+class ResizeTFTransform(object):
+    """Resize the input image to the given size.
+
+    Args:
+        size (list or int): Size of the result
+        interpolation (str, default='bilinear'):Desired interpolation type,
+                                                support 'bilinear', 'nearest', 'bicubic'
+
+    Returns:
+        tuple of processed image and label
+    """
+
+    def __init__(self, size, interpolation="bilinear"):
+        """Initialize `ResizeTFTransform` class."""
+        if isinstance(size, int):
+            self.size = size, size
+        elif isinstance(size, list):
+            if len(size) == 1:
+                self.size = size[0], size[0]
+            elif len(size) == 2:
+                self.size = size[0], size[1]
+        self.interpolation = interpolation
+
+        if self.interpolation not in ["bilinear", "nearest", "bicubic"]:
+            raise ValueError("Unsupported interpolation type!")
+
+    def __call__(self, sample):
+        """Resize the input image in sample to the given size."""
+        image, label = sample
+        if isinstance(image, tf.Tensor):
+            image = tf.image.resize(image, self.size, method=self.interpolation)
+        else:
+            image = cv2.resize(image, self.size, interpolation=interpolation_map[self.interpolation])
+        return (image, label)
+
+
+class BaseMetric(object):
+    """The base class of Metric."""
+
+    def __init__(self, metric, single_output=False, hvd=None):
+        """Initialize the basic metric.
+
+        Args:
+            metric: The metric class.
+            single_output: Whether the output is single or not, defaults to False.
+            hvd: The Horovod class for distributed training, defaults to None.
+        """
+        self._metric_cls = metric
+        self._single_output = single_output
+        self._hvd = hvd
+
+    def __call__(self, *args, **kwargs):
+        """Evaluate the model predictions, and the reference.
+
+        Returns:
+            The class itself.
+        """
+        self._metric = self._metric_cls(*args, **kwargs)
+        return self
+
+    @abstractmethod
+    def update(self, preds, labels=None, sample_weight=None):
+        """Update the state that need to be evaluated.
+
+        Args:
+            preds: The prediction result.
+            labels: The reference. Defaults to None.
+            sample_weight: The sampling weight. Defaults to None.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def reset(self):
+        """Clear the predictions and labels.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def result(self):
+        """Evaluate the difference between predictions and labels.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @property
+    def metric(self):
+        """Return its metric class.
+
+        Returns:
+            The metric class.
+        """
+        return self._metric
+
+    @property
+    def hvd(self):
+        """Return its hvd class.
+
+        Returns:
+            The hvd class.
+        """
+        return self._hvd
+
+    @hvd.setter
+    def hvd(self, hvd):
+        """Set its hvd.
+
+        Args:
+            hvd: The Horovod class for distributed training.
+        """
+        self._hvd = hvd
+
+
+class COCOmAPv2(BaseMetric):
+    """Compute mean average precision of the detection task."""
+
+    def __init__(
+        self,
+        anno_path=None,
+        iou_thrs="0.5:0.05:0.95",
+        map_points=101,
+        map_key="DetectionBoxes_Precision/mAP",
+        output_index_mapping={"num_detections": -1, "boxes": 0, "scores": 1, "classes": 2},
+    ):
+        """Initialize the metric.
+
+        Args:
+            anno_path: The path of annotation file.
+            iou_thrs: Minimal value for intersection over union that allows to make decision
+              that prediction bounding box is true positive. You can specify one float value
+              between 0 to 1 or string "05:0.05:0.95" for standard COCO thresholds.
+            map_points: The way to calculate mAP. 101 for 101-point interpolated AP, 11 for
+              11-point interpolated AP, 0 for area under PR curve.
+            map_key: The key that mapping to pycocotools COCOeval.
+              Defaults to 'DetectionBoxes_Precision/mAP'.
+            output_index_mapping: The output index mapping.
+              Defaults to {'num_detections':-1, 'boxes':0, 'scores':1, 'classes':2}.
+        """
+        self.output_index_mapping = output_index_mapping
+
+        if anno_path:
+            import os
+            import yaml
+
+            assert os.path.exists(anno_path), "Annotation path does not exists!"
+            with open(anno_path, "r") as f:
+                label_map = yaml.safe_load(f.read())
+            self.category_map_reverse = {k: v for k, v in label_map.items()}
+        else:
+            # label: index
+            self.category_map_reverse = {v: k for k, v in category_map.items()}
+        self.image_ids = []
+        self.ground_truth_list = []
+        self.detection_list = []
+        self.annotation_id = 1
+        self.category_map = category_map
+        self.category_id_set = set([cat for cat in self.category_map])  # index
+        self.iou_thrs = iou_thrs
+        self.map_points = map_points
+        self.map_key = map_key
+
+    def update(self, predicts, labels, sample_weight=None):
+        """Add the predictions and labels.
+
+        Args:
+            predicts: The predictions.
+            labels: The labels corresponding to the predictions.
+            sample_weight: The sample weight. Defaults to None.
+        """
+        from coco_tools import ExportSingleImageDetectionBoxesToCoco, ExportSingleImageGroundtruthToCoco
+
+        detections = []
+        if "num_detections" in self.output_index_mapping and self.output_index_mapping["num_detections"] > -1:
+            for item in zip(*predicts):
+                detection = {}
+                num = int(item[self.output_index_mapping["num_detections"]])
+                detection["boxes"] = np.asarray(item[self.output_index_mapping["boxes"]])[0:num]
+                detection["scores"] = np.asarray(item[self.output_index_mapping["scores"]])[0:num]
+                detection["classes"] = np.asarray(item[self.output_index_mapping["classes"]])[0:num]
+                detections.append(detection)
+        else:
+            for item in zip(*predicts):
+                detection = {}
+                detection["boxes"] = np.asarray(item[self.output_index_mapping["boxes"]])
+                detection["scores"] = np.asarray(item[self.output_index_mapping["scores"]])
+                detection["classes"] = np.asarray(item[self.output_index_mapping["classes"]])
+                detections.append(detection)
+
+        bboxes, str_labels, int_labels, image_ids = labels
+        labels = []
+        if len(int_labels[0]) == 0:
+            for str_label in str_labels:
+                str_label = [x if type(x) == "str" else x.decode("utf-8") for x in str_label]
+                labels.append([self.category_map_reverse[x] for x in str_label])
+        elif len(str_labels[0]) == 0:
+            for int_label in int_labels:
+                labels.append([x for x in int_label])
+
+        for idx, image_id in enumerate(image_ids):
+            image_id = image_id if type(image_id) == "str" else image_id.decode("utf-8")
+            if image_id in self.image_ids:
+                continue
+            self.image_ids.append(image_id)
+
+            ground_truth = {}
+            ground_truth["boxes"] = np.asarray(bboxes[idx])
+            ground_truth["classes"] = np.asarray(labels[idx])
+
+            self.ground_truth_list.extend(
+                ExportSingleImageGroundtruthToCoco(
+                    image_id=image_id,
+                    next_annotation_id=self.annotation_id,
+                    category_id_set=self.category_id_set,
+                    groundtruth_boxes=ground_truth["boxes"],
+                    groundtruth_classes=ground_truth["classes"],
+                )
+            )
+            self.annotation_id += ground_truth["boxes"].shape[0]
+
+            self.detection_list.extend(
+                ExportSingleImageDetectionBoxesToCoco(
+                    image_id=image_id,
+                    category_id_set=self.category_id_set,
+                    detection_boxes=detections[idx]["boxes"],
+                    detection_scores=detections[idx]["scores"],
+                    detection_classes=detections[idx]["classes"],
+                )
+            )
+
+    def reset(self):
+        """Reset the prediction and labels."""
+        self.image_ids = []
+        self.ground_truth_list = []
+        self.detection_list = []
+        self.annotation_id = 1
+
+    def result(self):
+        """Compute mean average precision.
+
+        Returns:
+            The mean average precision score.
+        """
+        from coco_tools import COCOEvalWrapper, COCOWrapper
+
+        if len(self.ground_truth_list) == 0:
+            logger.warning("Sample num during evaluation is 0.")
+            return 0
+        else:
+            groundtruth_dict = {
+                "annotations": self.ground_truth_list,
+                "images": [{"id": image_id} for image_id in self.image_ids],
+                "categories": [{"id": k, "name": v} for k, v in self.category_map.items()],
+            }
+            coco_wrapped_groundtruth = COCOWrapper(groundtruth_dict)
+            coco_wrapped_detections = coco_wrapped_groundtruth.LoadAnnotations(self.detection_list)
+            box_evaluator = COCOEvalWrapper(
+                coco_wrapped_groundtruth,
+                coco_wrapped_detections,
+                agnostic_mode=False,
+                iou_thrs=self.iou_thrs,
+                map_points=self.map_points,
+            )
+            box_metrics, box_per_category_ap = box_evaluator.ComputeMetrics(
+                include_metrics_per_category=False, all_metrics_per_category=False
+            )
+            box_metrics.update(box_per_category_ap)
+            box_metrics = {"DetectionBoxes_" + key: value for key, value in iter(box_metrics.items())}
+
+            return box_metrics[self.map_key]
+
+
+class ParseDecodeCoco:  # pragma: no cover
+    """Helper function for TensorflowModelZooBertDataset.
+
+    Parse the features from sample.
+    """
+
+    def __call__(self, sample):
+        """Parse the sample data.
+
+        Args:
+            sample: Data to be parsed.
+        """
+        # Dense features in Example proto.
+        feature_map = {
+            "image/encoded": tf.compat.v1.FixedLenFeature([], dtype=tf.string, default_value=""),
+            "image/object/class/text": tf.compat.v1.VarLenFeature(dtype=tf.string),
+            "image/object/class/label": tf.compat.v1.VarLenFeature(dtype=tf.int64),
+            "image/source_id": tf.compat.v1.FixedLenFeature([], dtype=tf.string, default_value=""),
+        }
+        sparse_float32 = tf.compat.v1.VarLenFeature(dtype=tf.float32)
+        # Sparse features in Example proto.
+        feature_map.update(
+            {
+                k: sparse_float32
+                for k in [
+                    "image/object/bbox/xmin",
+                    "image/object/bbox/ymin",
+                    "image/object/bbox/xmax",
+                    "image/object/bbox/ymax",
+                ]
+            }
+        )
+
+        features = tf.io.parse_single_example(sample, feature_map)
+
+        xmin = tf.expand_dims(features["image/object/bbox/xmin"].values, 0)
+        ymin = tf.expand_dims(features["image/object/bbox/ymin"].values, 0)
+        xmax = tf.expand_dims(features["image/object/bbox/xmax"].values, 0)
+        ymax = tf.expand_dims(features["image/object/bbox/ymax"].values, 0)
+
+        bbox = tf.concat([ymin, xmin, ymax, xmax], 0)
+        # Force the variable number of bounding boxes into the shape
+        # [1, num_boxes, coords].
+        bbox = tf.expand_dims(bbox, 0)
+        bbox = tf.transpose(bbox, [0, 2, 1])
+
+        encoded_image = features["image/encoded"]
+        image_tensor = tf.image.decode_image(encoded_image, channels=3)
+        image_tensor.set_shape([None, None, 3])
+
+        str_label = features["image/object/class/text"].values
+        int_label = features["image/object/class/label"].values
+        image_id = features["image/source_id"]
+
+        return image_tensor, (bbox[0], str_label, int_label, image_id)
+
+
+class COCORecordDataset(object):
+    """Tensorflow COCO dataset in tf record format.
+
+    Root is a full path to tfrecord file, which contains the file name.
+    Please use Resize transform when batch_size > 1
+
+    Args: root (str): Root directory of dataset.
+          num_cores (int, default=28):The number of input Datasets to interleave from in parallel.
+          transform (transform object, default=None):  transform to process input data.
+          filter (Filter objects, default=None): filter out examples according
+                                                 to specific conditions.
+    """
+
+    def __new__(cls, root, num_cores=28, transform=None, filter=filter):
+        """Build a new object."""
+        record_iterator = tf.compat.v1.python_io.tf_record_iterator(root)
+        example = tf.train.SequenceExample()
+        for element in record_iterator:
+            example.ParseFromString(element)
+            break
+        feature = example.context.feature
+        if (
+            len(feature["image/object/class/text"].bytes_list.value) == 0
+            and len(feature["image/object/class/label"].int64_list.value) == 0
+        ):
+            raise ValueError(
+                "Tfrecord format is incorrect, please refer\
+                'https://github.com/tensorflow/models/blob/master/research/\
+                object_detection/dataset_tools/create_coco_tf_record.py' to\
+                create correct tfrecord"
+            )
+        # pylint: disable=no-name-in-module
+        from tensorflow.python.data.experimental import parallel_interleave
+
+        tfrecord_paths = [root]
+        ds = tf.data.TFRecordDataset.list_files(tfrecord_paths)
+        ds = ds.apply(
+            parallel_interleave(
+                tf.data.TFRecordDataset,
+                cycle_length=num_cores,
+                block_length=5,
+                sloppy=True,
+                buffer_output_elements=10000,
+                prefetch_input_elements=10000,
+            )
+        )
+        if transform is not None:
+            transform.transform_list.insert(0, ParseDecodeCoco())
+        else:
+            transform = ParseDecodeCoco()
+        ds = ds.map(transform, num_parallel_calls=None)
+        if filter is not None:
+            ds = ds.filter(filter)
+        ds = ds.prefetch(buffer_size=1000)
+        return ds
+
+
+class TFDataLoader(object):
+    """Tensorflow dataloader class.
+
+    In tensorflow1.x dataloader is coupled with the graph, but it also support feed_dict
+    method to do session run, this dataloader is designed to satisfy the usage of feed dict
+    in tf1.x. Although it's a general dataloader and can be used in MXNet and PyTorch.
+
+    Args:
+        dataset: obj. wrapper of needed data.
+        batch_size: int. batch size
+    """
+
+    def __init__(self, dataset, batch_size=1, last_batch="rollover"):
+        """Initialize `TFDataDataLoader` class."""
+        self.dataset = dataset
+        self.last_batch = last_batch
+        self.batch_size = batch_size
+        dataset = dataset.batch(batch_size)
+
+    def batch(self, batch_size, last_batch="rollover"):
+        """Dataset return data per batch."""
+        drop_last = False if last_batch == "rollover" else True
+        self.batch_size = batch_size
+        self.dataset = self.dataset.batch(batch_size, drop_last)
+
+    def __iter__(self):
+        """Iterate dataloader."""
+        return self._generate_dataloader(
+            self.dataset,
+            batch_size=self.batch_size,
+            last_batch=self.last_batch,
+        )
+
+    def _generate_dataloader(
+        self,
+        dataset,
+        batch_size=1,
+        last_batch="rollover",
+        collate_fn=None,
+        sampler=None,
+        batch_sampler=None,
+        num_workers=None,
+        pin_memory=None,
+        distributed=False,
+    ):
+        """Yield data."""
+        drop_last = False if last_batch == "rollover" else True
+
+        def check_dynamic_shape(element_spec):
+            if isinstance(element_spec, collections.abc.Sequence):
+                return any([check_dynamic_shape(ele) for ele in element_spec])
+            elif isinstance(element_spec, tf.TensorSpec):
+                return True if element_spec.shape.num_elements() is None else False
+            else:
+                raise ValueError("unrecognized element spec...")
+
+        def squeeze_output(output):
+            if isinstance(output, collections.abc.Sequence):
+                return [squeeze_output(ele) for ele in output]
+            elif isinstance(output, np.ndarray):
+                return np.squeeze(output, axis=0)
+            else:
+                raise ValueError("not supported output format....")
+
+        if tf.executing_eagerly():
+            index = 0
+            outputs = []
+            for iter_tensors in dataset:
+                samples = []
+                iter_inputs, iter_labels = iter_tensors[0], iter_tensors[1]
+                if isinstance(iter_inputs, tf.Tensor):
+                    samples.append(iter_inputs.numpy())
+                else:
+                    samples.append(tuple(iter_input.numpy() for iter_input in iter_inputs))
+                if isinstance(iter_labels, tf.Tensor):
+                    samples.append(iter_labels.numpy())
+                else:
+                    samples.append([np.array(l) for l in iter_labels])
+                index += 1
+                outputs.append(samples)
+                if index == batch_size:
+                    outputs = default_collate(outputs)
+                    yield outputs
+                    outputs = []
+                    index = 0
+            if len(outputs) > 0:
+                outputs = default_collate(outputs)
+                yield outputs
+        else:
+            try_single_batch = check_dynamic_shape(dataset.element_spec)
+            dataset = dataset.batch(1 if try_single_batch else batch_size, drop_last)
+            ds_iterator = tf.compat.v1.data.make_one_shot_iterator(dataset)
+            iter_tensors = ds_iterator.get_next()
+            data_config = tf.compat.v1.ConfigProto()
+            data_config.use_per_session_threads = 1
+            data_config.intra_op_parallelism_threads = 1
+            data_config.inter_op_parallelism_threads = 16
+            data_sess = tf.compat.v1.Session(config=data_config)
+            # pylint: disable=no-name-in-module
+            from tensorflow.python.framework.errors_impl import OutOfRangeError
+
+            while True:
+                if not try_single_batch:
+                    try:
+                        outputs = data_sess.run(iter_tensors)
+                        yield outputs
+                    except OutOfRangeError:
+                        data_sess.close()
+                        return
+                else:
+                    try:
+                        outputs = []
+                        for i in range(0, batch_size):
+                            outputs.append(squeeze_output(data_sess.run(iter_tensors)))
+                        outputs = default_collate(outputs)
+                        yield outputs
+                    except OutOfRangeError:
+                        if len(outputs) == 0:
+                            data_sess.close()
+                            return
+                        else:
+                            outputs = default_collate(outputs)
+                            yield outputs
+                            data_sess.close()
+                            return
diff --git a/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/main.py b/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/main.py
new file mode 100644
index 00000000000..7751c5dadc6
--- /dev/null
+++ b/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/main.py
@@ -0,0 +1,131 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2023 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+#
+
+import time
+
+import numpy as np
+import tensorflow as tf
+
+from __future__ import division
+from argparse import ArgumentParser
+from data_process import(
+    COCOmAPv2,
+    COCORecordDataset,
+    ComposeTransform,
+    ResizeTFTransform,
+    TFDataLoader,
+)
+
+arg_parser = ArgumentParser(description='Parse args')
+
+arg_parser.add_argument('-g',
+                    "--input-graph",
+                    help='Specify the input graph.',
+                    dest='input_graph')
+arg_parser.add_argument('--config', type=str, default='')
+arg_parser.add_argument('--dataset_location', type=str, default='')
+arg_parser.add_argument('--output_model', type=str, default='')
+arg_parser.add_argument('--mode', type=str, default='accuracy')
+arg_parser.add_argument('--batch_size', type=int, default=10)
+arg_parser.add_argument('--iters', type=int, default=100, dest='iters', help='iterations')
+arg_parser.add_argument('--tune', action='store_true', default=False)
+arg_parser.add_argument('--benchmark', dest='benchmark',
+                    action='store_true', help='run benchmark')
+args = arg_parser.parse_args()
+
+def evaluate(model):
+    """Custom evaluate function to estimate the accuracy of the model.
+
+    Args:
+        model (tf.Graph or string or INC.model.TensorflowCheckpointModel): The input model.
+        
+    Returns:
+        accuracy (float): evaluation result, the larger is better.
+    """
+    from neural_compressor.tensorflow import Model
+    if isinstance(model, str) or isinstance(model, tf.compat.v1.Graph):
+        model = Model(model)
+        model.input_tensor_names = ["image_tensor:0"]
+        model.output_tensor_names = ["num_detections:0", "detection_boxes:0", \
+                                        "detection_scores:0", "detection_classes:0"]
+    input_tensor = model.input_tensor
+    output_tensor = model.output_tensor if len(model.output_tensor)>1 else \
+                        model.output_tensor[0]
+    warmup = 5
+    iteration = -1
+    if args.benchmark and args.mode == 'performance':
+        iteration = args.iters
+    metric = COCOmAPv2(output_index_mapping={'num_detections':0, 'boxes':1, 'scores':2, 'classes':3})
+
+    def eval_func(dataloader):
+        latency_list = []
+        for idx, (inputs, labels) in enumerate(dataloader):
+            # dataloader should keep the order and len of inputs same with input_tensor
+            inputs = np.array([inputs])
+            feed_dict = dict(zip(input_tensor, inputs))
+
+            start = time.time()
+            predictions = model.sess.run(output_tensor, feed_dict)
+            end = time.time()
+
+            metric.update(predictions, labels)
+            latency_list.append(end-start)
+            if idx + 1 == iteration:
+                break
+        latency = np.array(latency_list[warmup:]).mean() / args.batch_size
+        return latency
+
+    eval_dataset = COCORecordDataset(root=args.dataset_location, filter=None, \
+            transform=ComposeTransform(transform_list=[TensorflowResizeWithRatio(
+                                        min_dim=800, max_dim=1356, padding=False)]))
+    batch_size = 1 if args.mode == 'accuracy' else args.batch_size
+    eval_dataloader=TFDataLoader(dataset=eval_dataset, batch_size=args.batch_size)
+
+    latency = eval_func(eval_dataloader)
+    if args.benchmark and args.mode == 'performance':
+        print("Batch size = {}".format(args.batch_size))
+        print("Latency: {:.3f} ms".format(latency * 1000))
+        print("Throughput: {:.3f} images/sec".format(1. / latency))
+    acc = metric.result()
+    return acc 
+
+def main(_):
+    calib_dataset = COCORecordDataset(root=args.dataset_location, filter=LabelBalanceCOCORecordFilter(size=1))
+    calib_dataloader = TFDataLoader(dataset=calib_dataset, batch_size=1)
+
+    if args.tune:
+        from neural_compressor.tensorflow import StaticQuantConfig, quantize_model, Model
+
+        quant_config = StaticQuantConfig()
+        model = Model(args.input_graph)
+        model.input_tensor_names = ['image_tensor']
+        model.output_tensor_names = ["num_detections", "detection_boxes", "detection_scores", "detection_classes"]
+        q_model = quantize_model(model, quant_config, calib_dataloader)
+        q_model.save(args.output_model)
+            
+    if args.benchmark:
+        if args.mode == 'performance':
+            evaluate(args.input_graph)
+        else:
+            accuracy = evaluate(args.input_graph)
+            print('Batch size = %d' % args.batch_size)
+            print("Accuracy: %.5f" % accuracy)
+
+if __name__ == "__main__":
+	tf.compat.v1.app.run()
diff --git a/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/run_benchmark.sh b/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/run_benchmark.sh
new file mode 100644
index 00000000000..6c2115f58ff
--- /dev/null
+++ b/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/run_benchmark.sh
@@ -0,0 +1,51 @@
+#!/bin/bash
+set -x
+
+function main {
+
+  init_params "$@"
+  run_benchmark
+
+}
+
+# init params
+function init_params {
+  batch_size=32
+  iters=100
+  for var in "$@"
+  do
+    case $var in
+      --input_model=*)
+          input_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --mode=*)
+          mode=$(echo $var |cut -f2 -d=)
+      ;;
+      --dataset_location=*)
+          dataset_location=$(echo "$var" |cut -f2 -d=)
+      ;;
+      --batch_size=*)
+          batch_size=$(echo $var |cut -f2 -d=)
+      ;;
+      --iters=*)
+          iters=$(echo $var |cut -f2 -d=)
+      ;;
+    esac
+  done
+
+}
+
+
+# run_tuning
+function run_benchmark {
+
+    python  main.py \
+            --input-graph ${input_model} \
+            --mode ${mode} \
+            --dataset_location "${dataset_location}" \
+            --batch_size ${batch_size} \
+            --iters ${iters} \
+            --benchmark
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/run_quant.sh b/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/run_quant.sh
new file mode 100644
index 00000000000..559d695f768
--- /dev/null
+++ b/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/run_quant.sh
@@ -0,0 +1,41 @@
+#!/bin/bash
+set -x
+
+function main {
+
+  init_params "$@"
+
+  run_tuning
+
+}
+
+# init params
+function init_params {
+
+  for var in "$@"
+  do
+    case $var in
+      --input_model=*)
+          input_model=$(echo "$var" |cut -f2 -d=)
+      ;;
+      --output_model=*)
+          output_model=$(echo "$var" |cut -f2 -d=)
+      ;;
+      --dataset_location=*)
+          dataset_location=$(echo "$var" |cut -f2 -d=)
+      ;;
+    esac
+  done
+
+}
+
+# run_tuning
+function run_tuning {
+  python  main.py \
+          --input-graph "${input_model}" \
+          --output_model "${output_model}" \
+          --dataset_location "${dataset_location}" \
+          --tune
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/object_detection/prepare_dataset.sh b/examples/3.x_api/tensorflow/object_detection/prepare_dataset.sh
new file mode 100644
index 00000000000..fea0ff1c373
--- /dev/null
+++ b/examples/3.x_api/tensorflow/object_detection/prepare_dataset.sh
@@ -0,0 +1,136 @@
+!/bin/bash
+# set -x
+
+DATA_DIR="${PWD}/data"
+DATA_NAME="val2017"
+DATA_URL_LIST='http://images.cocodataset.org/zips/val2017.zip http://images.cocodataset.org/annotations/annotations_trainval2017.zip'
+PACKAGES_LIST='val2017.zip annotations_trainval2017.zip'
+VAL_IMAGE_DIR=$DATA_DIR/val2017
+TRAIN_ANNOTATIONS_FILE=$DATA_DIR/annotations/empty.json
+VAL_ANNOTATIONS_FILE=$DATA_DIR/annotations/instances_val2017.json
+TESTDEV_ANNOTATIONS_FILE=$DATA_DIR/annotations/empty.json
+OUTPUT_DIR=$DATA_DIR
+
+help()
+{
+   cat <<- EOF
+
+   Desc: Prepare dataset for Tensorflow COCO object detection.
+
+   -h --help              help info
+
+   --dataset_location     set dataset location, default is ./data
+
+EOF
+   exit 0
+}
+
+function main {
+  init_params "$@"
+  download_dataset
+  convert_to_tf_record
+}
+
+# init params
+function init_params {
+
+  for var in "$@"
+  do
+    case $var in
+      --dataset_location=*)
+          DATA_DIR=$(echo "$var" |cut -f2 -d=)
+      ;;
+      -h|--help) help
+      ;;
+      *)
+      echo "Error: No such parameter: ${var}"
+      exit 1
+      ;;
+    esac
+  done
+
+}
+
+# removes files that will not be used anymore
+function remove_zipped_packages {
+  for package in $PACKAGES_LIST; do
+    rm "$package"
+  done
+}
+
+function download_tf_models_repo {
+  if [ ! -d models ]; then
+    git clone https://github.com/tensorflow/models.git
+  fi
+  cd models || exit
+  git checkout 7a9934df2afdf95be9405b4e9f1f2480d748dc40
+  cd ..
+}
+
+function divide_tf_records_by_dataset {
+  if [ ! -d "${DATA_DIR}/tf_test2017" ]; then
+    mkdir "${DATA_DIR}/tf_test2017"
+  fi
+  if [ ! -d "${DATA_DIR}/tf_train2017" ]; then
+    mkdir "${DATA_DIR}/tf_train2017"
+  fi
+  if [ ! -d "${DATA_DIR}/tf_val2017" ]; then
+    mkdir "${DATA_DIR}/tf_val2017"
+  fi
+  mv ${DATA_DIR}/coco_testdev.record* ${DATA_DIR}/tf_test2017
+  mv ${DATA_DIR}/coco_train.record* ${DATA_DIR}/tf_train2017
+  mv ${DATA_DIR}/coco_val.record* ${DATA_DIR}/tf_val2017
+}
+
+function convert {
+  cd models/research
+  protoc object_detection/protos/*.proto --python_out=.
+  export PYTHONPATH=$PYTHONPATH:$(pwd)
+  export PYTHONPATH=$PYTHONPATH:$(pwd)/slim
+  python ./object_detection/dataset_tools/create_coco_tf_record.py --logtostderr \
+    --train_image_dir=empty_dir \
+    --val_image_dir="${VAL_IMAGE_DIR}" \
+    --test_image_dir=empty_dir \
+    --train_annotations_file="${TRAIN_ANNOTATIONS_FILE}" \
+    --val_annotations_file="${VAL_ANNOTATIONS_FILE}" \
+    --testdev_annotations_file="${TESTDEV_ANNOTATIONS_FILE}" \
+    --output_dir="${OUTPUT_DIR}"
+}
+
+function convert_to_tf_record {
+  download_tf_models_repo
+  convert
+  divide_tf_records_by_dataset
+}
+
+# download_dataset
+function download_dataset {
+  if [ ! -d "${DATA_DIR}" ]; then
+    mkdir "${DATA_DIR}"
+  fi
+
+  cd "${DATA_DIR}" || exit
+  if [ ! -f "${VAL_IMAGE_DIR}" ]; then
+
+    for dataset_dowload_link in $DATA_URL_LIST; do
+      wget "$dataset_dowload_link"
+    done
+    for package in $PACKAGES_LIST; do
+      unzip -o "$package"
+    done
+    remove_zipped_packages
+    if [ ! -d empty_dir ]; then
+      mkdir empty_dir
+    fi
+
+    cd annotations || exit
+    echo "{ \"images\": {}, \"categories\": {}}" > empty.json
+    cd ..
+  else
+    echo "Dataset ${DATA_NAME} is exist!"
+  fi
+
+  cd ../
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/object_detection/requirements.txt b/examples/3.x_api/tensorflow/object_detection/requirements.txt
new file mode 100644
index 00000000000..865df0f3a6b
--- /dev/null
+++ b/examples/3.x_api/tensorflow/object_detection/requirements.txt
@@ -0,0 +1,8 @@
+Cython
+contextlib2
+pillow>=8.2.0
+lxml>=4.6.2
+matplotlib
+numpy>=1.17.4
+pycocotools
+protobuf
diff --git a/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/README.md b/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/README.md
new file mode 100644
index 00000000000..43026b9002d
--- /dev/null
+++ b/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/README.md
@@ -0,0 +1,158 @@
+Step-by-Step
+============
+
+This document is used to list steps of reproducing TensorFlow Object Detection models tuning results. This example can run on Intel CPUs and GPUs.
+
+# Prerequisite
+
+
+## 1. Environment
+Recommend python 3.6 or higher version.
+
+### Install Intel® Neural Compressor
+```shell
+pip install neural-compressor
+```
+
+### Install Intel Tensorflow
+```shell
+pip install intel-tensorflow
+```
+> Note: Validated TensorFlow [Version](/docs/source/installation_guide.md#validated-software-environment).
+
+### Installation Dependency packages
+```shell
+cd examples/3.x_api//tensorflow/object_detection
+pip install -r requirements.txt
+cd ssd_mobilenet_v1/quantization/ptq
+```
+
+### Install Protocol Buffer Compiler
+
+`Protocol Buffer Compiler` in version higher than 3.0.0 is necessary ingredient for automatic COCO dataset preparation. To install please follow
+[Protobuf installation instructions](https://grpc.io/docs/protoc-installation/#install-using-a-package-manager).
+
+### Install Intel Extension for Tensorflow
+
+#### Quantizing the model on Intel GPU(Mandatory to install ITEX)
+Intel Extension for Tensorflow is mandatory to be installed for quantizing the model on Intel GPUs.
+
+```shell
+pip install --upgrade intel-extension-for-tensorflow[xpu]
+```
+For any more details, please follow the procedure in [install-gpu-drivers](https://github.com/intel/intel-extension-for-tensorflow/blob/main/docs/install/install_for_xpu.md#install-gpu-drivers)
+
+#### Quantizing the model on Intel CPU(Optional to install ITEX)
+Intel Extension for Tensorflow for Intel CPUs is experimental currently. It's not mandatory for quantizing the model on Intel CPUs.
+
+```shell
+pip install --upgrade intel-extension-for-tensorflow[cpu]
+```
+
+> **Note**: 
+> The version compatibility of stock Tensorflow and ITEX can be checked [here](https://github.com/intel/intel-extension-for-tensorflow#compatibility-table). Please make sure you have installed compatible Tensorflow and ITEX.
+
+## 2. Prepare Model
+
+### Automated approach
+Run the `prepare_model.py` script located in `examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq`.
+
+```
+python prepare_model.py --model_name=ssd_mobilenet_v1 --model_path=./
+
+Prepare pre-trained model for COCO object detection
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --model_name {ssd_resnet50_v1,ssd_mobilenet_v1}
+                        model to download, default is ssd_resnet50_v1
+  --model_path MODEL_PATH
+                        directory to put models, default is ./model
+```
+
+### Manual approach
+
+```shell
+wget http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2018_01_28.tar.gz
+tar -xvzf ssd_mobilenet_v1_coco_2018_01_28.tar.gz
+```
+
+## 3. Prepare Dataset
+
+### Automatic dataset download
+
+> **_Note: `prepare_dataset.sh` script works with TF version 1.x._**
+
+Run the `prepare_dataset.sh` script located in `examples/3.x_api/tensorflow/object_detection`.
+
+Usage:
+```shell
+cd examples/3.x_api/tensorflow/object_detection
+. prepare_dataset.sh
+cd ssd_mobilenet_v1/quantization/ptq
+```
+
+This script will download the *train*, *validation* and *test* COCO datasets. Furthermore it will convert them to
+tensorflow records using the `https://github.com/tensorflow/models.git` dedicated script.
+
+### Manual dataset download
+Download CoCo Dataset from [Official Website](https://cocodataset.org/#download).
+
+
+# Run Command
+
+Now we support both pb and ckpt formats.
+
+## 1. Quantization
+### For PB format
+  
+  ```shell
+  # The cmd of running ssd_mobilenet_v1
+  bash run_quant.sh --input_model=./ssd_mobilenet_v1_coco_2018_01_28/frozen_inference_graph.pb --output_model=./tensorflow-ssd_mobilenet_v1-tune.pb --dataset_location=/path/to/dataset/coco_val.record
+  ```
+
+### For ckpt format
+  
+  ```shell
+  # The cmd of running ssd_mobilenet_v1
+  bash run_quant.sh --input_model=./ssd_mobilenet_v1_coco_2018_01_28/ --output_model=./tensorflow-ssd_mobilenet_v1-tune.pb --dataset_location=/path/to/dataset/coco_val.record
+  ```
+
+## 2. Benchmark
+  ```shell
+  bash run_benchmark.sh --input_model=./tensorflow-ssd_mobilenet_v1-tune.pb  --dataset_location=/path/to/dataset/coco_val.record --mode=performance
+  ```
+
+Details of enabling Intel® Neural Compressor on ssd_mobilenet_v1 for Tensorflow.
+=========================
+
+This is a tutorial of how to enable ssd_mobilenet_v1 model with Intel® Neural Compressor.
+## User Code Analysis
+User specifies fp32 *model*, calibration dataset *q_dataloader* and a custom *eval_func* which encapsulates the evaluation dataset and metric by itself.
+
+For ssd_mobilenet_v1, we applied the latter one because our philosophy is to enable the model with minimal changes. Hence we need to make two changes on the original code. The first one is to implement the q_dataloader and make necessary changes to *eval_func*.
+
+### Code update
+
+After prepare step is done, we just need update main.py like below.
+```python
+    if args.tune:
+        from neural_compressor.tensorflow import StaticQuantConfig, quantize_model, Model
+
+        quant_config = StaticQuantConfig()
+        model = Model(args.input_graph)
+        model.input_tensor_names = ['image_tensor']
+        model.output_tensor_names = ["num_detections", "detection_boxes", "detection_scores", "detection_classes"]
+        q_model = quantize_model(model, quant_config, calib_dataloader)
+        q_model.save(args.output_model)
+            
+    if args.benchmark:
+        if args.mode == 'performance':
+            evaluate(args.input_graph)
+        else:
+            accuracy = evaluate(args.input_graph)
+            print('Batch size = %d' % args.batch_size)
+            print("Accuracy: %.5f" % accuracy)
+```
+
+The quantization.fit() function will return a best quantized model during timeout constrain.
diff --git a/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/coco_tools.py b/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/coco_tools.py
new file mode 100644
index 00000000000..2f9369798df
--- /dev/null
+++ b/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/coco_tools.py
@@ -0,0 +1,694 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2021 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Wrappers for third party pycocotools to be used within object_detection.
+
+Note that nothing in this file is tensorflow related and thus cannot
+be called directly as a slim metric, for example.
+
+TODO(jonathanhuang): wrap as a slim metric in metrics.py
+
+
+Usage example: given a set of images with ids in the list image_ids
+and corresponding lists of numpy arrays encoding groundtruth (boxes and classes)
+and detections (boxes, scores and classes), where elements of each list
+correspond to detections/annotations of a single image,
+then evaluation (in multi-class mode) can be invoked as follows:
+
+  groundtruth_dict = coco_tools.ExportGroundtruthToCOCO(
+      image_ids, groundtruth_boxes_list, groundtruth_classes_list,
+      max_num_classes, output_path=None)
+  detections_list = coco_tools.ExportDetectionsToCOCO(
+      image_ids, detection_boxes_list, detection_scores_list,
+      detection_classes_list, output_path=None)
+  groundtruth = coco_tools.COCOWrapper(groundtruth_dict)
+  detections = groundtruth.LoadAnnotations(detections_list)
+  evaluator = coco_tools.COCOEvalWrapper(groundtruth, detections,
+                                         agnostic_mode=False)
+  metrics = evaluator.ComputeMetrics()
+"""
+
+import copy
+import time
+from collections import OrderedDict
+from typing import Any, Dict, List, Set, Union
+
+import numpy as np
+from pycocotools import coco, cocoeval, mask
+
+from neural_compressor.utils import logger
+
+
+class COCOWrapper(coco.COCO):
+    """Wrapper for the pycocotools COCO class.
+
+    Attributes:
+      dataset: a dictionary holding bounding box annotations in the COCO format.
+      detection_type: type of detections being wrapped. Can be one of ['bbox',
+        'segmentation']
+    """
+
+    def __init__(self, dataset: Dict[str, Any], detection_type: str = "bbox"):
+        """Construct a COCOWrapper.
+
+        See http://mscoco.org/dataset/#format for a description of the format.
+        By default, the coco.COCO class constructor reads from a JSON file.
+        This function duplicates the same behavior but loads from a dictionary,
+        allowing us to perform evaluation without writing to external storage.
+
+        Args:
+          dataset: a dictionary holding bounding box annotations in the COCO format.
+          detection_type: type of detections being wrapped. Can be one of ['bbox',
+            'segmentation']
+
+        Raises:
+          ValueError: if detection_type is unsupported.
+        """
+        supported_detection_types = ["bbox", "segmentation"]
+        if detection_type not in supported_detection_types:
+            raise ValueError(
+                "Unsupported detection type: {}. "
+                "Supported values are: {}".format(detection_type, supported_detection_types)
+            )
+        self._detection_type = detection_type
+        coco.COCO.__init__(self)
+        self.dataset = dataset
+        self.createIndex()
+
+    def LoadAnnotations(self, annotations: list) -> coco.COCO:
+        """Load annotations dictionary into COCO datastructure.
+
+        See http://mscoco.org/dataset/#format for a description of the annotations
+        format.  As above, this function replicates the default behavior of the API
+        but does not require writing to external storage.
+
+        Args:
+          annotations: python list holding object detection results where each
+            detection is encoded as a dict with required keys ['image_id',
+            'category_id', 'score'] and one of ['bbox', 'segmentation'] based on
+            `detection_type`.
+
+        Returns:
+          a coco.COCO datastructure holding object detection annotations results
+
+        Raises:
+          ValueError: if (1) annotations is not a list or annotations do not
+            correspond to the images contained in self.
+        """
+        results = coco.COCO()
+        results.dataset["images"] = [img for img in self.dataset["images"]]
+
+        logger.info("Load and prepare annotation results.")
+        tic = time.time()
+
+        if not isinstance(annotations, list):
+            raise ValueError("annotations is not a list of objects")
+        annotation_img_ids = [ann["image_id"] for ann in annotations]
+        if set(annotation_img_ids) != (set(annotation_img_ids) & set(self.getImgIds())):
+            raise ValueError("Results do not correspond to current coco set")
+        results.dataset["categories"] = copy.deepcopy(self.dataset["categories"])
+        if self._detection_type == "bbox":
+            for idx, ann in enumerate(annotations):
+                bb = ann["bbox"]
+                ann["area"] = bb[2] * bb[3]
+                ann["id"] = idx + 1
+                ann["iscrowd"] = 0
+        elif self._detection_type == "segmentation":
+            for idx, ann in enumerate(annotations):
+                ann["area"] = mask.area(ann["segmentation"])
+                ann["bbox"] = mask.toBbox(ann["segmentation"])
+                ann["id"] = idx + 1
+                ann["iscrowd"] = 0
+        logger.info("DONE (t=%0.2fs)", (time.time() - tic))
+
+        results.dataset["annotations"] = annotations
+        results.createIndex()
+        return results
+
+
+class COCOEvalWrapper(cocoeval.COCOeval):
+    """Wrapper for the pycocotools COCOeval class.
+
+    To evaluate, create two objects (groundtruth_dict and detections_list)
+    using the conventions listed at http://mscoco.org/dataset/#format.
+    Then call evaluation as follows:
+
+      groundtruth = coco_tools.COCOWrapper(groundtruth_dict)
+      detections = groundtruth.LoadAnnotations(detections_list)
+      evaluator = coco_tools.COCOEvalWrapper(groundtruth, detections,
+                                             agnostic_mode=False)
+      metrics = evaluator.ComputeMetrics()
+    """
+
+    def __init__(
+        self,
+        groundtruth: coco.COCO = None,
+        detections: coco.COCO = None,
+        agnostic_mode=False,
+        iou_type: str = "bbox",
+        iou_thrs: Union[str, float] = None,
+        map_points=None,
+    ):
+        """Construct a COCOEvalWrapper.
+
+        Note that for the area-based metrics to be meaningful, detection and
+        groundtruth boxes must be in image coordinates measured in pixels.
+
+        Args:
+          groundtruth: a coco.COCO (or coco_tools.COCOWrapper) object holding
+            groundtruth annotations
+          detections: a coco.COCO (or coco_tools.COCOWrapper) object holding
+            detections
+          agnostic_mode: boolean (default: False).  If True, evaluation ignores
+            class labels, treating all detections as proposals.
+          iou_thrs: Minimal value for intersection over union that allows to
+                    make decision that prediction bounding box is true positive.
+                    You can specify one float value between 0 to 1 or
+                    string "05:0.05:0.95" for standard COCO thresholds.
+          iou_type: IOU type to use for evaluation. Supports `bbox` or `segm`.
+          map_points: The way to calculate mAP. 101 for 101-point interpolated AP, 11 for
+                          11-point interpolated AP, 0 for area under PR curve.
+        """
+        cocoeval.COCOeval.__init__(self, groundtruth, detections, iouType=iou_type)
+        if agnostic_mode:
+            self.params.useCats = 0
+        if iou_thrs == "0.5:0.05:0.95":
+            self.params.iouThrs = np.linspace(0.5, 0.95, int(np.round((0.95 - 0.5) / 0.05)) + 1, endpoint=True)
+        elif isinstance(iou_thrs, float):
+            self.params.iouThrs = [iou_thrs]
+
+        if map_points == 101:
+            self.params.recThrs = np.linspace(0.0, 1.00, int(np.round((1.00 - 0.0) / 0.01)) + 1, endpoint=True)
+        if map_points == 11:
+            self.params.recThrs = np.linspace(0.0, 1.00, int(np.round((1.00 - 0.0) / 0.1)) + 1, endpoint=True)
+        if map_points == 0:
+            self.params.recThrs = [-1]
+
+    def GetCategory(self, category_id: int) -> dict:
+        """Fetch dictionary holding category information given category id.
+
+        Args:
+          category_id: integer id
+
+        Returns:
+          dictionary holding 'id', 'name'.
+        """
+        return self.cocoGt.cats[category_id]
+
+    def GetAgnosticMode(self) -> bool:
+        """Return whether COCO Eval is configured to evaluate in agnostic mode."""
+        return self.params.useCats == 0
+
+    def GetCategoryIdList(self) -> List[int]:
+        """Return the list of IDs of all valid categories."""
+        return self.params.catIds
+
+    def accumulate(self, p: cocoeval.Params = None):
+        """Accumulate evaluation results per image and store it to self.eval.
+
+        Args:
+          p: input params for evaluation
+        """
+        print("Accumulating evaluation results...")
+        tic = time.time()
+        if not self.evalImgs:
+            print("Please run evaluate() first")
+        # allows input customized parameters
+        if p is None:
+            p = self.params
+        p.catIds = p.catIds if p.useCats == 1 else [-1]
+        T = len(p.iouThrs)
+        R = len(p.recThrs)
+        K = len(p.catIds) if p.useCats else 1
+        A = len(p.areaRng)
+        M = len(p.maxDets)
+        precision = -np.ones((T, R, K, A, M))  # -1 for the precision of absent categories
+        recall = -np.ones((T, K, A, M))
+        scores = -np.ones((T, R, K, A, M))
+
+        # create dictionary for future indexing
+        _pe = self._paramsEval
+        print("-pe", _pe)
+        catIds = _pe.catIds if _pe.useCats else [-1]
+        setK = set(catIds)
+        setA = set(map(tuple, _pe.areaRng))
+        setM = set(_pe.maxDets)
+        setI = set(_pe.imgIds)
+        # get inds to evaluate
+        k_list = [n for n, k in enumerate(p.catIds) if k in setK]
+        m_list = [m for n, m in enumerate(p.maxDets) if m in setM]
+        a_list = [n for n, a in enumerate(map(lambda x: tuple(x), p.areaRng)) if a in setA]
+        i_list = [n for n, i in enumerate(p.imgIds) if i in setI]
+        I0 = len(_pe.imgIds)
+        A0 = len(_pe.areaRng)
+        # retrieve E at each category, area range, and max number of detections
+        for k, k0 in enumerate(k_list):
+            Nk = k0 * A0 * I0
+            for a, a0 in enumerate(a_list):
+                Na = a0 * I0
+                for m, maxDet in enumerate(m_list):
+                    E = [self.evalImgs[Nk + Na + i] for i in i_list]
+                    E = [e for e in E if e is not None]
+                    if len(E) == 0:
+                        continue
+                    dtScores = np.concatenate([e["dtScores"][0:maxDet] for e in E])
+
+                    # different sorting method generates slightly different results.
+                    # mergesort is used to be consistent as Matlab implementation.
+                    inds = np.argsort(-dtScores, kind="mergesort")
+                    dtScoresSorted = dtScores[inds]
+
+                    dtm = np.concatenate([e["dtMatches"][:, 0:maxDet] for e in E], axis=1)[:, inds]
+                    dtIg = np.concatenate([e["dtIgnore"][:, 0:maxDet] for e in E], axis=1)[:, inds]
+                    gtIg = np.concatenate([e["gtIgnore"] for e in E])
+                    npig = np.count_nonzero(gtIg == 0)
+                    if npig == 0:
+                        continue
+                    tps = np.logical_and(dtm, np.logical_not(dtIg))
+                    fps = np.logical_and(np.logical_not(dtm), np.logical_not(dtIg))
+
+                    tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float32)
+                    fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float32)
+                    for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)):
+                        tp = np.array(tp)
+                        fp = np.array(fp)
+                        nd = len(tp)
+                        rc = tp / npig
+                        pr = tp / (fp + tp + np.spacing(1))
+
+                        # calculate precision
+                        if R == 1:
+                            rc = np.concatenate(([0.0], rc, [1.0]))
+                            pr = np.concatenate(([0.0], pr, [0.0]))
+
+                            # compute the precision envelope
+                            for i in range(pr.size - 1, 0, -1):
+                                pr[i - 1] = np.maximum(pr[i - 1], pr[i])
+
+                            # to calculate area under PR curve, look for points
+                            # where X axis (recall) changes value
+                            change_point = np.where(rc[1:] != rc[:-1])[0]
+                            # and sum (\Delta recall) * recall
+                            res = np.sum((rc[change_point + 1] - rc[change_point]) * pr[change_point + 1])
+                            precision[t, :, k, a, m] = np.array([res])
+                        else:
+                            q = np.zeros((R,))
+
+                            # numpy is slow without cython optimization for accessing elements
+                            # use python array gets significant speed improvement
+                            pr = pr.tolist()
+                            q = q.tolist()
+
+                            for i in range(nd - 1, 0, -1):
+                                if pr[i] > pr[i - 1]:
+                                    pr[i - 1] = pr[i]
+
+                            inds = np.searchsorted(rc, p.recThrs, side="left")
+                            try:
+                                for ri, pi in enumerate(inds):
+                                    q[ri] = pr[pi]
+                            except:
+                                pass
+                            precision[t, :, k, a, m] = np.array(q)
+
+                        # calculate recall
+                        if nd:
+                            recall[t, k, a, m] = rc[-1]
+                        else:
+                            recall[t, k, a, m] = 0
+
+                        # calculate score
+                        ss = np.zeros((R,))
+                        inds = np.searchsorted(rc, p.recThrs, side="left")
+                        try:
+                            for ri, pi in enumerate(inds):
+                                ss[ri] = dtScoresSorted[pi]
+                        except:
+                            pass
+                        scores[t, :, k, a, m] = np.array(ss)
+        # exit(0)
+        self.eval = {
+            "params": p,
+            "counts": [T, R, K, A, M],
+            "precision": precision,
+            "recall": recall,
+            "scores": scores,
+        }
+        toc = time.time()
+        print("DONE (t={:0.2f}s).".format(toc - tic))
+
+    def ComputeMetrics(
+        self, include_metrics_per_category: bool = False, all_metrics_per_category: bool = False
+    ):  # pragma: no cover
+        """Compute detection metrics.
+
+        Args:
+          include_metrics_per_category: Whether include metrics per category.
+          all_metrics_per_category: Whether include all the summery metrics for
+            each category in per_category_ap. Be careful with setting it to true if
+            you have more than handful of categories, because it will pollute
+            your mldash.
+
+        Returns:
+          A tuple of (summary_metrics, per_category_ap), in which
+            (1) summary_metrics is a dictionary holding:
+              'Precision/mAP': mean average precision over classes averaged over IOU
+                thresholds ranging from .5 to .95 with .05 increments;
+              'Precision/mAP@.50IOU': mean average precision at 50% IOU;
+              'Precision/mAP@.75IOU': mean average precision at 75% IOU;
+              'Precision/mAP (small)': mean average precision for small objects
+                (area < 32^2 pixels);
+              'Precision/mAP (medium)': mean average precision for medium sized
+                objects (32^2 pixels < area < 96^2 pixels);
+              'Precision/mAP (large)': mean average precision for large objects
+                (96^2 pixels < area < 10000^2 pixels);
+              'Recall/AR@1': average recall with 1 detection;
+              'Recall/AR@10': average recall with 10 detections;
+              'Recall/AR@100': average recall with 100 detections;
+              'Recall/AR@100 (small)': average recall for small objects with 100
+                detections;
+              'Recall/AR@100 (medium)': average recall for medium objects with 100
+                detections;
+              'Recall/AR@100 (large)': average recall for large objects with 100
+                detections;
+            and (2) per_category_ap is a dictionary holding category specific results with
+              keys of the form: 'Precision mAP ByCategory/category'
+              (without the supercategory part if no supercategories exist).
+
+          For backward compatibility 'PerformanceByCategory' is included in the
+            output regardless of all_metrics_per_category. If evaluating class-agnostic
+            mode, per_category_ap is an empty dictionary.
+
+        Raises:
+          ValueError: If category_stats does not exist.
+        """
+        self.evaluate()
+        self.accumulate()
+        self.summarize()
+
+        summary_metrics = OrderedDict(
+            [
+                ("Precision/mAP", self.stats[0]),
+                ("Precision/mAP@.50IOU", self.stats[1]),
+                ("Precision/mAP@.75IOU", self.stats[2]),
+                ("Precision/mAP (small)", self.stats[3]),
+                ("Precision/mAP (medium)", self.stats[4]),
+                ("Precision/mAP (large)", self.stats[5]),
+                ("Recall/AR@1", self.stats[6]),
+                ("Recall/AR@10", self.stats[7]),
+                ("Recall/AR@100", self.stats[8]),
+                ("Recall/AR@100 (small)", self.stats[9]),
+                ("Recall/AR@100 (medium)", self.stats[10]),
+                ("Recall/AR@100 (large)", self.stats[11]),
+            ]
+        )
+        if not include_metrics_per_category:
+            return summary_metrics, {}
+        if not hasattr(self, "category_stats"):
+            raise ValueError("Category stats do not exist")
+        per_category_ap = OrderedDict([])
+        if self.GetAgnosticMode():
+            return summary_metrics, per_category_ap
+        for category_index, category_id in enumerate(self.GetCategoryIdList()):
+            category = self.GetCategory(category_id)["name"]
+            # Kept for backward compatilbility
+            # pylint: disable=no-member
+            per_category_ap["PerformanceByCategory/mAP/{}".format(category)] = self.category_stats[0][category_index]
+            if all_metrics_per_category:
+                per_category_ap["Precision mAP ByCategory/{}".format(category)] = self.category_stats[0][category_index]
+                per_category_ap["Precision mAP@.50IOU ByCategory/{}".format(category)] = self.category_stats[1][
+                    category_index
+                ]
+                per_category_ap["Precision mAP@.75IOU ByCategory/{}".format(category)] = self.category_stats[2][
+                    category_index
+                ]
+                per_category_ap["Precision mAP (small) ByCategory/{}".format(category)] = self.category_stats[3][
+                    category_index
+                ]
+                per_category_ap["Precision mAP (medium) ByCategory/{}".format(category)] = self.category_stats[4][
+                    category_index
+                ]
+                per_category_ap["Precision mAP (large) ByCategory/{}".format(category)] = self.category_stats[5][
+                    category_index
+                ]
+                per_category_ap["Recall AR@1 ByCategory/{}".format(category)] = self.category_stats[6][category_index]
+                per_category_ap["Recall AR@10 ByCategory/{}".format(category)] = self.category_stats[7][category_index]
+                per_category_ap["Recall AR@100 ByCategory/{}".format(category)] = self.category_stats[8][category_index]
+                per_category_ap["Recall AR@100 (small) ByCategory/{}".format(category)] = self.category_stats[9][
+                    category_index
+                ]
+                per_category_ap["Recall AR@100 (medium) ByCategory/{}".format(category)] = self.category_stats[10][
+                    category_index
+                ]
+                per_category_ap["Recall AR@100 (large) ByCategory/{}".format(category)] = self.category_stats[11][
+                    category_index
+                ]
+
+        return summary_metrics, per_category_ap
+
+
+def _ConvertBoxToCOCOFormat(box):
+    """Convert a box in [ymin, xmin, ymax, xmax] format to COCO format.
+
+    This is a utility function for converting from our internal
+    [ymin, xmin, ymax, xmax] convention to the convention used by the COCO API
+    i.e., [xmin, ymin, width, height].
+
+    Args:
+      box: a numpy array in format of [ymin, xmin, ymax, xmax]
+
+    Returns:
+      A list of floats, in COCO format, representing [xmin, ymin, width, height]
+    """
+    return [float(box[1]), float(box[0]), float(box[3] - box[1]), float(box[2] - box[0])]
+
+
+def _RleCompress(masks):
+    """Compresses mask using Run-length encoding provided by pycocotools.
+
+    Args:
+      masks: uint8 numpy array of shape [mask_height, mask_width] with values in
+        {0, 1}.
+
+    Returns:
+      A pycocotools Run-length encoding of the mask.
+    """
+    return mask.encode(np.asfortranarray(masks))
+
+
+def ExportSingleImageGroundtruthToCoco(
+    image_id: Union[int, str],
+    next_annotation_id: int,
+    category_id_set: Set[str],
+    groundtruth_boxes: np.array,
+    groundtruth_classes: np.array,
+    groundtruth_masks: Union[np.array, None] = None,
+    groundtruth_is_crowd: Union[np.array, None] = None,
+) -> list:
+    """Export groundtruth of a single image to COCO format.
+
+    This function converts groundtruth detection annotations represented as numpy
+    arrays to dictionaries that can be ingested by the COCO evaluation API. Note
+    that the image_ids provided here must match the ones given to
+    ExportSingleImageDetectionsToCoco. We assume that boxes and classes are in
+    correspondence - that is: groundtruth_boxes[i, :], and
+    groundtruth_classes[i] are associated with the same groundtruth annotation.
+
+    In the exported result, "area" fields are always set to the area of the
+    groundtruth bounding box.
+
+    Args:
+      image_id: a unique image identifier either of type integer or string.
+      next_annotation_id: integer specifying the first id to use for the
+        groundtruth annotations. All annotations are assigned a continuous integer
+        id starting from this value.
+      category_id_set: A set of valid class ids. Groundtruth with classes not in
+        category_id_set are dropped.
+      groundtruth_boxes: numpy array (float32) with shape [num_gt_boxes, 4]
+      groundtruth_classes: numpy array (int) with shape [num_gt_boxes]
+      groundtruth_masks: optional uint8 numpy array of shape [num_detections,
+        image_height, image_width] containing detection_masks.
+      groundtruth_is_crowd: optional numpy array (int) with shape [num_gt_boxes]
+        indicating whether groundtruth boxes are crowd.
+
+    Returns:
+      A list of groundtruth annotations for a single image in the COCO format.
+
+    Raises:
+      ValueError: if (1) groundtruth_boxes and groundtruth_classes do not have the
+        right lengths or (2) if each of the elements inside these lists do not
+        have the correct shapes or (3) if image_ids are not integers
+    """
+    if len(groundtruth_classes.shape) != 1:
+        raise ValueError("groundtruth_classes is " "expected to be of rank 1.")
+    if len(groundtruth_boxes.shape) != 2:
+        raise ValueError("groundtruth_boxes is expected to be of " "rank 2.")
+    if groundtruth_boxes.shape[1] != 4:
+        raise ValueError("groundtruth_boxes should have " "shape[1] == 4.")
+    num_boxes = groundtruth_classes.shape[0]
+    if num_boxes != groundtruth_boxes.shape[0]:
+        raise ValueError(
+            "Corresponding entries in groundtruth_classes, "
+            "and groundtruth_boxes should have "
+            "compatible shapes (i.e., agree on the 0th dimension)."
+            "Classes shape: %d. Boxes shape: %d. Image ID: %s"
+            % (groundtruth_classes.shape[0], groundtruth_boxes.shape[0], image_id)
+        )
+    has_is_crowd = groundtruth_is_crowd is not None
+    if has_is_crowd and len(groundtruth_is_crowd.shape) != 1:
+        raise ValueError("groundtruth_is_crowd is expected to be of rank 1.")
+    groundtruth_list = []
+    for i in range(num_boxes):
+        if groundtruth_classes[i] in category_id_set:
+            iscrowd = groundtruth_is_crowd[i] if has_is_crowd else 0
+            export_dict = {
+                "id": next_annotation_id + i,
+                "image_id": image_id,
+                "category_id": int(groundtruth_classes[i]),
+                "bbox": list(_ConvertBoxToCOCOFormat(groundtruth_boxes[i, :])),
+                "area": float(
+                    (groundtruth_boxes[i, 2] - groundtruth_boxes[i, 0])
+                    * (groundtruth_boxes[i, 3] - groundtruth_boxes[i, 1])
+                ),
+                "iscrowd": iscrowd,
+            }
+            if groundtruth_masks is not None:
+                export_dict["segmentation"] = _RleCompress(groundtruth_masks[i])
+            groundtruth_list.append(export_dict)
+    return groundtruth_list
+
+
+def ExportSingleImageDetectionBoxesToCoco(
+    image_id: Union[int, str],
+    category_id_set: Set[int],
+    detection_boxes: np.array,
+    detection_scores: np.array,
+    detection_classes: np.array,
+) -> list:
+    """Export detections of a single image to COCO format.
+
+    This function converts detections represented as numpy arrays to dictionaries
+    that can be ingested by the COCO evaluation API. Note that the image_ids
+    provided here must match the ones given to the
+    ExporSingleImageDetectionBoxesToCoco. We assume that boxes, and classes are in
+    correspondence - that is: boxes[i, :], and classes[i]
+    are associated with the same groundtruth annotation.
+
+    Args:
+        image_id: unique image identifier either of type integer or string.
+        category_id_set: A set of valid class ids. Detections with classes not in
+          category_id_set are dropped.
+        detection_boxes: float numpy array of shape [num_detections, 4] containing
+          detection boxes.
+        detection_scores: float numpy array of shape [num_detections] containing
+          scored for the detection boxes.
+        detection_classes: integer numpy array of shape [num_detections] containing
+          the classes for detection boxes.
+
+    Returns:
+        A list of detection annotations for a single image in the COCO format.
+
+    Raises:
+        ValueError: if (1) detection_boxes, detection_scores and detection_classes
+        do not have the right lengths or (2) if each of the elements inside these
+        lists do not have the correct shapes or (3) if image_ids are not integers.
+    """
+    if len(detection_classes.shape) != 1 or len(detection_scores.shape) != 1:
+        raise ValueError("All entries in detection_classes and detection_scores" "expected to be of rank 1.")
+    if len(detection_boxes.shape) != 2:
+        raise ValueError("All entries in detection_boxes expected to be of " "rank 2.")
+    if detection_boxes.shape[1] != 4:
+        raise ValueError("All entries in detection_boxes should have " "shape[1] == 4.")
+    num_boxes = detection_classes.shape[0]
+    if not num_boxes == detection_boxes.shape[0] == detection_scores.shape[0]:
+        raise ValueError(
+            "Corresponding entries in detection_classes, "
+            "detection_scores and detection_boxes should have "
+            "compatible shapes (i.e., agree on the 0th dimension). "
+            "Classes shape: %d. Boxes shape: %d. "
+            "Scores shape: %d" % (detection_classes.shape[0], detection_boxes.shape[0], detection_scores.shape[0])
+        )
+    detections_list = []
+    for i in range(num_boxes):
+        if detection_classes[i] in category_id_set:
+            detections_list.append(
+                {
+                    "image_id": image_id,
+                    "category_id": int(detection_classes[i]),
+                    "bbox": list(_ConvertBoxToCOCOFormat(detection_boxes[i, :])),
+                    "score": float(detection_scores[i]),
+                }
+            )
+    return detections_list
+
+
+def ExportSingleImageDetectionMasksToCoco(
+    image_id: Union[str, int],
+    category_id_set: Set[int],
+    detection_masks: np.array,
+    detection_scores: np.array,
+    detection_classes: np.array,
+) -> list:
+    """Export detection masks of a single image to COCO format.
+
+    This function converts detections represented as numpy arrays to dictionaries
+    that can be ingested by the COCO evaluation API. We assume that
+    detection_masks, detection_scores, and detection_classes are in correspondence
+    - that is: detection_masks[i, :], detection_classes[i] and detection_scores[i]
+        are associated with the same annotation.
+
+    Args:
+        image_id: unique image identifier either of type integer or string.
+        category_id_set: A set of valid class ids. Detections with classes not in
+        category_id_set are dropped.
+        detection_masks: uint8 numpy array of shape [num_detections, image_height,
+        image_width] containing detection_masks.
+        detection_scores: float numpy array of shape [num_detections] containing
+        scores for detection masks.
+        detection_classes: integer numpy array of shape [num_detections] containing
+        the classes for detection masks.
+
+    Returns:
+        A list of detection mask annotations for a single image in the COCO format.
+
+    Raises:
+        ValueError: if (1) detection_masks, detection_scores and detection_classes
+        do not have the right lengths or (2) if each of the elements inside these
+        lists do not have the correct shapes or (3) if image_ids are not integers.
+    """
+    if len(detection_classes.shape) != 1 or len(detection_scores.shape) != 1:
+        raise ValueError("All entries in detection_classes and detection_scores" "expected to be of rank 1.")
+    num_boxes = detection_classes.shape[0]
+    if not num_boxes == len(detection_masks) == detection_scores.shape[0]:
+        raise ValueError(
+            "Corresponding entries in detection_classes, "
+            "detection_scores and detection_masks should have "
+            "compatible lengths and shapes "
+            "Classes length: %d.  Masks length: %d. "
+            "Scores length: %d" % (detection_classes.shape[0], len(detection_masks), detection_scores.shape[0])
+        )
+    detections_list = []
+    for i in range(num_boxes):
+        if detection_classes[i] in category_id_set:
+            detections_list.append(
+                {
+                    "image_id": image_id,
+                    "category_id": int(detection_classes[i]),
+                    "segmentation": _RleCompress(detection_masks[i]),
+                    "score": float(detection_scores[i]),
+                }
+            )
+    return detections_list
diff --git a/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/data_process.py b/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/data_process.py
new file mode 100644
index 00000000000..32e55adb3fd
--- /dev/null
+++ b/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/data_process.py
@@ -0,0 +1,655 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2024 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import os
+import cv2
+import collections
+
+import numpy as np
+import tensorflow as tf
+
+from abc import abstractmethod
+from neural_compressor.common import logger
+from neural_compressor.tensorflow.utils.data import default_collate
+
+interpolation_map = {
+    "nearest": cv2.INTER_NEAREST,
+    "bilinear": cv2.INTER_LINEAR,
+    "bicubic": cv2.INTER_CUBIC,
+}
+
+category_map = {
+    1: "person",
+    2: "bicycle",
+    3: "car",
+    4: "motorcycle",
+    5: "airplane",
+    6: "bus",
+    7: "train",
+    8: "truck",
+    9: "boat",
+    10: "traffic light",
+    11: "fire hydrant",
+    13: "stop sign",
+    14: "parking meter",
+    15: "bench",
+    16: "bird",
+    17: "cat",
+    18: "dog",
+    19: "horse",
+    20: "sheep",
+    21: "cow",
+    22: "elephant",
+    23: "bear",
+    24: "zebra",
+    25: "giraffe",
+    27: "backpack",
+    28: "umbrella",
+    31: "handbag",
+    32: "tie",
+    33: "suitcase",
+    34: "frisbee",
+    35: "skis",
+    36: "snowboard",
+    37: "sports ball",
+    38: "kite",
+    39: "baseball bat",
+    40: "baseball glove",
+    41: "skateboard",
+    42: "surfboard",
+    43: "tennis racket",
+    44: "bottle",
+    46: "wine glass",
+    47: "cup",
+    48: "fork",
+    49: "knife",
+    50: "spoon",
+    51: "bowl",
+    52: "banana",
+    53: "apple",
+    54: "sandwich",
+    55: "orange",
+    56: "broccoli",
+    57: "carrot",
+    58: "hot dog",
+    59: "pizza",
+    60: "donut",
+    61: "cake",
+    62: "chair",
+    63: "couch",
+    64: "potted plant",
+    65: "bed",
+    67: "dining table",
+    70: "toilet",
+    72: "tv",
+    73: "laptop",
+    74: "mouse",
+    75: "remote",
+    76: "keyboard",
+    77: "cell phone",
+    78: "microwave",
+    79: "oven",
+    80: "toaster",
+    81: "sink",
+    82: "refrigerator",
+    84: "book",
+    85: "clock",
+    86: "vase",
+    87: "scissors",
+    88: "teddy bear",
+    89: "hair drier",
+    90: "toothbrush",
+}
+
+class ComposeTransform(object):
+    """Composes several transforms together.
+
+    Args:
+        transform_list (list of Transform objects):  list of transforms to compose
+
+    Returns:
+        sample (tuple): tuple of processed image and label
+    """
+
+    def __init__(self, transform_list):
+        """Initialize `ComposeTransform` class."""
+        self.transform_list = transform_list
+
+    def __call__(self, sample):
+        """Call transforms in transform_list."""
+        for transform in self.transform_list:
+            sample = transform(sample)
+        return sample
+    
+
+class ResizeTFTransform(object):
+    """Resize the input image to the given size.
+
+    Args:
+        size (list or int): Size of the result
+        interpolation (str, default='bilinear'):Desired interpolation type,
+                                                support 'bilinear', 'nearest', 'bicubic'
+
+    Returns:
+        tuple of processed image and label
+    """
+
+    def __init__(self, size, interpolation="bilinear"):
+        """Initialize `ResizeTFTransform` class."""
+        if isinstance(size, int):
+            self.size = size, size
+        elif isinstance(size, list):
+            if len(size) == 1:
+                self.size = size[0], size[0]
+            elif len(size) == 2:
+                self.size = size[0], size[1]
+        self.interpolation = interpolation
+
+        if self.interpolation not in ["bilinear", "nearest", "bicubic"]:
+            raise ValueError("Unsupported interpolation type!")
+
+    def __call__(self, sample):
+        """Resize the input image in sample to the given size."""
+        image, label = sample
+        if isinstance(image, tf.Tensor):
+            image = tf.image.resize(image, self.size, method=self.interpolation)
+        else:
+            image = cv2.resize(image, self.size, interpolation=interpolation_map[self.interpolation])
+        return (image, label)
+
+
+class BaseMetric(object):
+    """The base class of Metric."""
+
+    def __init__(self, metric, single_output=False, hvd=None):
+        """Initialize the basic metric.
+
+        Args:
+            metric: The metric class.
+            single_output: Whether the output is single or not, defaults to False.
+            hvd: The Horovod class for distributed training, defaults to None.
+        """
+        self._metric_cls = metric
+        self._single_output = single_output
+        self._hvd = hvd
+
+    def __call__(self, *args, **kwargs):
+        """Evaluate the model predictions, and the reference.
+
+        Returns:
+            The class itself.
+        """
+        self._metric = self._metric_cls(*args, **kwargs)
+        return self
+
+    @abstractmethod
+    def update(self, preds, labels=None, sample_weight=None):
+        """Update the state that need to be evaluated.
+
+        Args:
+            preds: The prediction result.
+            labels: The reference. Defaults to None.
+            sample_weight: The sampling weight. Defaults to None.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def reset(self):
+        """Clear the predictions and labels.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def result(self):
+        """Evaluate the difference between predictions and labels.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @property
+    def metric(self):
+        """Return its metric class.
+
+        Returns:
+            The metric class.
+        """
+        return self._metric
+
+    @property
+    def hvd(self):
+        """Return its hvd class.
+
+        Returns:
+            The hvd class.
+        """
+        return self._hvd
+
+    @hvd.setter
+    def hvd(self, hvd):
+        """Set its hvd.
+
+        Args:
+            hvd: The Horovod class for distributed training.
+        """
+        self._hvd = hvd
+
+
+class COCOmAPv2(BaseMetric):
+    """Compute mean average precision of the detection task."""
+
+    def __init__(
+        self,
+        anno_path=None,
+        iou_thrs="0.5:0.05:0.95",
+        map_points=101,
+        map_key="DetectionBoxes_Precision/mAP",
+        output_index_mapping={"num_detections": -1, "boxes": 0, "scores": 1, "classes": 2},
+    ):
+        """Initialize the metric.
+
+        Args:
+            anno_path: The path of annotation file.
+            iou_thrs: Minimal value for intersection over union that allows to make decision
+              that prediction bounding box is true positive. You can specify one float value
+              between 0 to 1 or string "05:0.05:0.95" for standard COCO thresholds.
+            map_points: The way to calculate mAP. 101 for 101-point interpolated AP, 11 for
+              11-point interpolated AP, 0 for area under PR curve.
+            map_key: The key that mapping to pycocotools COCOeval.
+              Defaults to 'DetectionBoxes_Precision/mAP'.
+            output_index_mapping: The output index mapping.
+              Defaults to {'num_detections':-1, 'boxes':0, 'scores':1, 'classes':2}.
+        """
+        self.output_index_mapping = output_index_mapping
+
+        if anno_path:
+            import os
+            import yaml
+
+            assert os.path.exists(anno_path), "Annotation path does not exists!"
+            with open(anno_path, "r") as f:
+                label_map = yaml.safe_load(f.read())
+            self.category_map_reverse = {k: v for k, v in label_map.items()}
+        else:
+            # label: index
+            self.category_map_reverse = {v: k for k, v in category_map.items()}
+        self.image_ids = []
+        self.ground_truth_list = []
+        self.detection_list = []
+        self.annotation_id = 1
+        self.category_map = category_map
+        self.category_id_set = set([cat for cat in self.category_map])  # index
+        self.iou_thrs = iou_thrs
+        self.map_points = map_points
+        self.map_key = map_key
+
+    def update(self, predicts, labels, sample_weight=None):
+        """Add the predictions and labels.
+
+        Args:
+            predicts: The predictions.
+            labels: The labels corresponding to the predictions.
+            sample_weight: The sample weight. Defaults to None.
+        """
+        from coco_tools import ExportSingleImageDetectionBoxesToCoco, ExportSingleImageGroundtruthToCoco
+
+        detections = []
+        if "num_detections" in self.output_index_mapping and self.output_index_mapping["num_detections"] > -1:
+            for item in zip(*predicts):
+                detection = {}
+                num = int(item[self.output_index_mapping["num_detections"]])
+                detection["boxes"] = np.asarray(item[self.output_index_mapping["boxes"]])[0:num]
+                detection["scores"] = np.asarray(item[self.output_index_mapping["scores"]])[0:num]
+                detection["classes"] = np.asarray(item[self.output_index_mapping["classes"]])[0:num]
+                detections.append(detection)
+        else:
+            for item in zip(*predicts):
+                detection = {}
+                detection["boxes"] = np.asarray(item[self.output_index_mapping["boxes"]])
+                detection["scores"] = np.asarray(item[self.output_index_mapping["scores"]])
+                detection["classes"] = np.asarray(item[self.output_index_mapping["classes"]])
+                detections.append(detection)
+
+        bboxes, str_labels, int_labels, image_ids = labels
+        labels = []
+        if len(int_labels[0]) == 0:
+            for str_label in str_labels:
+                str_label = [x if type(x) == "str" else x.decode("utf-8") for x in str_label]
+                labels.append([self.category_map_reverse[x] for x in str_label])
+        elif len(str_labels[0]) == 0:
+            for int_label in int_labels:
+                labels.append([x for x in int_label])
+
+        for idx, image_id in enumerate(image_ids):
+            image_id = image_id if type(image_id) == "str" else image_id.decode("utf-8")
+            if image_id in self.image_ids:
+                continue
+            self.image_ids.append(image_id)
+
+            ground_truth = {}
+            ground_truth["boxes"] = np.asarray(bboxes[idx])
+            ground_truth["classes"] = np.asarray(labels[idx])
+
+            self.ground_truth_list.extend(
+                ExportSingleImageGroundtruthToCoco(
+                    image_id=image_id,
+                    next_annotation_id=self.annotation_id,
+                    category_id_set=self.category_id_set,
+                    groundtruth_boxes=ground_truth["boxes"],
+                    groundtruth_classes=ground_truth["classes"],
+                )
+            )
+            self.annotation_id += ground_truth["boxes"].shape[0]
+
+            self.detection_list.extend(
+                ExportSingleImageDetectionBoxesToCoco(
+                    image_id=image_id,
+                    category_id_set=self.category_id_set,
+                    detection_boxes=detections[idx]["boxes"],
+                    detection_scores=detections[idx]["scores"],
+                    detection_classes=detections[idx]["classes"],
+                )
+            )
+
+    def reset(self):
+        """Reset the prediction and labels."""
+        self.image_ids = []
+        self.ground_truth_list = []
+        self.detection_list = []
+        self.annotation_id = 1
+
+    def result(self):
+        """Compute mean average precision.
+
+        Returns:
+            The mean average precision score.
+        """
+        from coco_tools import COCOEvalWrapper, COCOWrapper
+
+        if len(self.ground_truth_list) == 0:
+            logger.warning("Sample num during evaluation is 0.")
+            return 0
+        else:
+            groundtruth_dict = {
+                "annotations": self.ground_truth_list,
+                "images": [{"id": image_id} for image_id in self.image_ids],
+                "categories": [{"id": k, "name": v} for k, v in self.category_map.items()],
+            }
+            coco_wrapped_groundtruth = COCOWrapper(groundtruth_dict)
+            coco_wrapped_detections = coco_wrapped_groundtruth.LoadAnnotations(self.detection_list)
+            box_evaluator = COCOEvalWrapper(
+                coco_wrapped_groundtruth,
+                coco_wrapped_detections,
+                agnostic_mode=False,
+                iou_thrs=self.iou_thrs,
+                map_points=self.map_points,
+            )
+            box_metrics, box_per_category_ap = box_evaluator.ComputeMetrics(
+                include_metrics_per_category=False, all_metrics_per_category=False
+            )
+            box_metrics.update(box_per_category_ap)
+            box_metrics = {"DetectionBoxes_" + key: value for key, value in iter(box_metrics.items())}
+
+            return box_metrics[self.map_key]
+
+
+class ParseDecodeCoco:  # pragma: no cover
+    """Helper function for TensorflowModelZooBertDataset.
+
+    Parse the features from sample.
+    """
+
+    def __call__(self, sample):
+        """Parse the sample data.
+
+        Args:
+            sample: Data to be parsed.
+        """
+        # Dense features in Example proto.
+        feature_map = {
+            "image/encoded": tf.compat.v1.FixedLenFeature([], dtype=tf.string, default_value=""),
+            "image/object/class/text": tf.compat.v1.VarLenFeature(dtype=tf.string),
+            "image/object/class/label": tf.compat.v1.VarLenFeature(dtype=tf.int64),
+            "image/source_id": tf.compat.v1.FixedLenFeature([], dtype=tf.string, default_value=""),
+        }
+        sparse_float32 = tf.compat.v1.VarLenFeature(dtype=tf.float32)
+        # Sparse features in Example proto.
+        feature_map.update(
+            {
+                k: sparse_float32
+                for k in [
+                    "image/object/bbox/xmin",
+                    "image/object/bbox/ymin",
+                    "image/object/bbox/xmax",
+                    "image/object/bbox/ymax",
+                ]
+            }
+        )
+
+        features = tf.io.parse_single_example(sample, feature_map)
+
+        xmin = tf.expand_dims(features["image/object/bbox/xmin"].values, 0)
+        ymin = tf.expand_dims(features["image/object/bbox/ymin"].values, 0)
+        xmax = tf.expand_dims(features["image/object/bbox/xmax"].values, 0)
+        ymax = tf.expand_dims(features["image/object/bbox/ymax"].values, 0)
+
+        bbox = tf.concat([ymin, xmin, ymax, xmax], 0)
+        # Force the variable number of bounding boxes into the shape
+        # [1, num_boxes, coords].
+        bbox = tf.expand_dims(bbox, 0)
+        bbox = tf.transpose(bbox, [0, 2, 1])
+
+        encoded_image = features["image/encoded"]
+        image_tensor = tf.image.decode_image(encoded_image, channels=3)
+        image_tensor.set_shape([None, None, 3])
+
+        str_label = features["image/object/class/text"].values
+        int_label = features["image/object/class/label"].values
+        image_id = features["image/source_id"]
+
+        return image_tensor, (bbox[0], str_label, int_label, image_id)
+
+
+class COCORecordDataset(object):
+    """Tensorflow COCO dataset in tf record format.
+
+    Root is a full path to tfrecord file, which contains the file name.
+    Please use Resize transform when batch_size > 1
+
+    Args: root (str): Root directory of dataset.
+          num_cores (int, default=28):The number of input Datasets to interleave from in parallel.
+          transform (transform object, default=None):  transform to process input data.
+          filter (Filter objects, default=None): filter out examples according
+                                                 to specific conditions.
+    """
+
+    def __new__(cls, root, num_cores=28, transform=None, filter=filter):
+        """Build a new object."""
+        record_iterator = tf.compat.v1.python_io.tf_record_iterator(root)
+        example = tf.train.SequenceExample()
+        for element in record_iterator:
+            example.ParseFromString(element)
+            break
+        feature = example.context.feature
+        if (
+            len(feature["image/object/class/text"].bytes_list.value) == 0
+            and len(feature["image/object/class/label"].int64_list.value) == 0
+        ):
+            raise ValueError(
+                "Tfrecord format is incorrect, please refer\
+                'https://github.com/tensorflow/models/blob/master/research/\
+                object_detection/dataset_tools/create_coco_tf_record.py' to\
+                create correct tfrecord"
+            )
+        # pylint: disable=no-name-in-module
+        from tensorflow.python.data.experimental import parallel_interleave
+
+        tfrecord_paths = [root]
+        ds = tf.data.TFRecordDataset.list_files(tfrecord_paths)
+        ds = ds.apply(
+            parallel_interleave(
+                tf.data.TFRecordDataset,
+                cycle_length=num_cores,
+                block_length=5,
+                sloppy=True,
+                buffer_output_elements=10000,
+                prefetch_input_elements=10000,
+            )
+        )
+        if transform is not None:
+            transform.transform_list.insert(0, ParseDecodeCoco())
+        else:
+            transform = ParseDecodeCoco()
+        ds = ds.map(transform, num_parallel_calls=None)
+        if filter is not None:
+            ds = ds.filter(filter)
+        ds = ds.prefetch(buffer_size=1000)
+        return ds
+
+
+class TFDataLoader(object):
+    """Tensorflow dataloader class.
+
+    In tensorflow1.x dataloader is coupled with the graph, but it also support feed_dict
+    method to do session run, this dataloader is designed to satisfy the usage of feed dict
+    in tf1.x. Although it's a general dataloader and can be used in MXNet and PyTorch.
+
+    Args:
+        dataset: obj. wrapper of needed data.
+        batch_size: int. batch size
+    """
+
+    def __init__(self, dataset, batch_size=1, last_batch="rollover"):
+        """Initialize `TFDataDataLoader` class."""
+        self.dataset = dataset
+        self.last_batch = last_batch
+        self.batch_size = batch_size
+        dataset = dataset.batch(batch_size)
+
+    def batch(self, batch_size, last_batch="rollover"):
+        """Dataset return data per batch."""
+        drop_last = False if last_batch == "rollover" else True
+        self.batch_size = batch_size
+        self.dataset = self.dataset.batch(batch_size, drop_last)
+
+    def __iter__(self):
+        """Iterate dataloader."""
+        return self._generate_dataloader(
+            self.dataset,
+            batch_size=self.batch_size,
+            last_batch=self.last_batch,
+        )
+
+    def _generate_dataloader(
+        self,
+        dataset,
+        batch_size=1,
+        last_batch="rollover",
+        collate_fn=None,
+        sampler=None,
+        batch_sampler=None,
+        num_workers=None,
+        pin_memory=None,
+        distributed=False,
+    ):
+        """Yield data."""
+        drop_last = False if last_batch == "rollover" else True
+
+        def check_dynamic_shape(element_spec):
+            if isinstance(element_spec, collections.abc.Sequence):
+                return any([check_dynamic_shape(ele) for ele in element_spec])
+            elif isinstance(element_spec, tf.TensorSpec):
+                return True if element_spec.shape.num_elements() is None else False
+            else:
+                raise ValueError("unrecognized element spec...")
+
+        def squeeze_output(output):
+            if isinstance(output, collections.abc.Sequence):
+                return [squeeze_output(ele) for ele in output]
+            elif isinstance(output, np.ndarray):
+                return np.squeeze(output, axis=0)
+            else:
+                raise ValueError("not supported output format....")
+
+        if tf.executing_eagerly():
+            index = 0
+            outputs = []
+            for iter_tensors in dataset:
+                samples = []
+                iter_inputs, iter_labels = iter_tensors[0], iter_tensors[1]
+                if isinstance(iter_inputs, tf.Tensor):
+                    samples.append(iter_inputs.numpy())
+                else:
+                    samples.append(tuple(iter_input.numpy() for iter_input in iter_inputs))
+                if isinstance(iter_labels, tf.Tensor):
+                    samples.append(iter_labels.numpy())
+                else:
+                    samples.append([np.array(l) for l in iter_labels])
+                index += 1
+                outputs.append(samples)
+                if index == batch_size:
+                    outputs = default_collate(outputs)
+                    yield outputs
+                    outputs = []
+                    index = 0
+            if len(outputs) > 0:
+                outputs = default_collate(outputs)
+                yield outputs
+        else:
+            try_single_batch = check_dynamic_shape(dataset.element_spec)
+            dataset = dataset.batch(1 if try_single_batch else batch_size, drop_last)
+            ds_iterator = tf.compat.v1.data.make_one_shot_iterator(dataset)
+            iter_tensors = ds_iterator.get_next()
+            data_config = tf.compat.v1.ConfigProto()
+            data_config.use_per_session_threads = 1
+            data_config.intra_op_parallelism_threads = 1
+            data_config.inter_op_parallelism_threads = 16
+            data_sess = tf.compat.v1.Session(config=data_config)
+            # pylint: disable=no-name-in-module
+            from tensorflow.python.framework.errors_impl import OutOfRangeError
+
+            while True:
+                if not try_single_batch:
+                    try:
+                        outputs = data_sess.run(iter_tensors)
+                        yield outputs
+                    except OutOfRangeError:
+                        data_sess.close()
+                        return
+                else:
+                    try:
+                        outputs = []
+                        for i in range(0, batch_size):
+                            outputs.append(squeeze_output(data_sess.run(iter_tensors)))
+                        outputs = default_collate(outputs)
+                        yield outputs
+                    except OutOfRangeError:
+                        if len(outputs) == 0:
+                            data_sess.close()
+                            return
+                        else:
+                            outputs = default_collate(outputs)
+                            yield outputs
+                            data_sess.close()
+                            return
diff --git a/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/main.py b/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/main.py
new file mode 100644
index 00000000000..b217f63a3ec
--- /dev/null
+++ b/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/main.py
@@ -0,0 +1,129 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2023 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+#
+
+import time
+
+import numpy as np
+import tensorflow as tf
+
+from __future__ import division
+from argparse import ArgumentParser
+from data_process import(
+    COCOmAPv2,
+    COCORecordDataset,
+    ComposeTransform,
+    ResizeTFTransform,
+    TFDataLoader,
+)
+
+arg_parser = ArgumentParser(description='Parse args')
+
+arg_parser.add_argument('-g',
+                    "--input-graph",
+                    help='Specify the input graph.',
+                    dest='input_graph')
+arg_parser.add_argument('--config', type=str, default='')
+arg_parser.add_argument('--dataset_location', type=str, default='')
+arg_parser.add_argument('--output_model', type=str, default='')
+arg_parser.add_argument('--mode', type=str, default='performance')
+arg_parser.add_argument('--batch_size', type=int, default=10)
+arg_parser.add_argument('--iters', type=int, default=100, dest='iters', help='iterations')
+arg_parser.add_argument('--tune', action='store_true', default=False)
+arg_parser.add_argument('--benchmark', dest='benchmark',
+                    action='store_true', help='run benchmark')
+args = arg_parser.parse_args()
+
+def evaluate(model):
+    """Custom evaluate function to estimate the accuracy of the model.
+
+    Args:
+        model (tf.Graph or string or INC.model.TensorflowCheckpointModel): The input model.
+        
+    Returns:
+        accuracy (float): evaluation result, the larger is better.
+    """
+    from neural_compressor.tensorflow import Model
+    if isinstance(model, str) or isinstance(model, tf.compat.v1.Graph):
+        model = Model(model)
+        model.input_tensor_names = ["image_tensor:0"]
+        model.output_tensor_names = ["num_detections:0", "detection_boxes:0", \
+                                        "detection_scores:0", "detection_classes:0"]
+    input_tensor = model.input_tensor
+    output_tensor = model.output_tensor if len(model.output_tensor)>1 else \
+                        model.output_tensor[0]
+    warmup = 5
+    iteration = -1
+    if args.benchmark and args.mode == 'performance':
+        iteration = args.iters
+    metric = COCOmAPv2(output_index_mapping={'num_detections':0, 'boxes':1, 'scores':2, 'classes':3})
+
+    def eval_func(dataloader):
+        latency_list = []
+        for idx, (inputs, labels) in enumerate(dataloader):
+            # dataloader should keep the order and len of inputs same with input_tensor
+            inputs = np.array([inputs])
+            feed_dict = dict(zip(input_tensor, inputs))
+
+            start = time.time()
+            predictions = model.sess.run(output_tensor, feed_dict)
+            end = time.time()
+
+            metric.update(predictions, labels)
+            latency_list.append(end-start)
+            if idx + 1 == iteration:
+                break
+        latency = np.array(latency_list[warmup:]).mean() / args.batch_size
+        return latency
+
+    eval_dataset = COCORecordDataset(root=args.dataset_location, filter=None, \
+            transform=ComposeTransform(transform_list=[ResizeTFTransform(size=300)]))
+    eval_dataloader=TFDataLoader(framework='tensorflow', dataset=eval_dataset, batch_size=args.batch_size)
+    latency = eval_func(eval_dataloader)
+    if args.benchmark and args.mode == 'performance':
+        print("Batch size = {}".format(args.batch_size))
+        print("Latency: {:.3f} ms".format(latency * 1000))
+        print("Throughput: {:.3f} images/sec".format(1. / latency))
+    acc = metric.result()
+    return acc 
+
+def main(_):
+    calib_dataset = COCORecordDataset(root=args.dataset_location, filter=None, \
+            transform=ComposeTransform(transform_list=[ResizeTFTransform(size=300)]))
+    calib_dataloader = TFDataLoader(framework='tensorflow', dataset=calib_dataset, batch_size=args.batch_size)
+
+    if args.tune:
+        from neural_compressor.tensorflow import StaticQuantConfig, quantize_model, Model
+
+        quant_config = StaticQuantConfig()
+        model = Model(args.input_graph)
+        model.input_tensor_names = ['image_tensor']
+        model.output_tensor_names = ["num_detections", "detection_boxes", "detection_scores", "detection_classes"]
+        q_model = quantize_model(model, quant_config, calib_dataloader)
+        q_model.save(args.output_model)
+            
+    if args.benchmark:
+        if args.mode == 'performance':
+            evaluate(args.input_graph)
+        else:
+            accuracy = evaluate(args.input_graph)
+            print('Batch size = %d' % args.batch_size)
+            print("Accuracy: %.5f" % accuracy)
+
+if __name__ == "__main__":
+	tf.compat.v1.app.run()
diff --git a/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/prepare_model.py b/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/prepare_model.py
new file mode 100644
index 00000000000..51882cf0bfe
--- /dev/null
+++ b/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/prepare_model.py
@@ -0,0 +1,99 @@
+import os
+import argparse
+import enum
+import tarfile
+import abc
+
+
+class SupportedModels(enum.Enum):
+    """
+    Enumeration containing supported models
+    """
+    ssd_resnet50_v1 = 'ssd_resnet50_v1'
+    ssd_mobilnet_v1 = 'ssd_mobilenet_v1'
+
+
+class Model(abc.ABC):
+    """
+    Base model class used to obtain the model (and perform any necessary operations to make it usable)
+    """
+
+    @abc.abstractmethod
+    def get_pretrained_model(self, destination):
+        """
+        Base method for obtaining a ready to use model
+        Args:
+            destination: path to where the file should be stored
+        """
+        pass
+
+
+class SsdMobilenetV1(Model):
+    """ Concrete implementation of the Model base class for ssd_mobilenet_v1"""
+
+    def get_pretrained_model(self, destination):
+        """
+        Obtains a ready to use ssd_mobilenet_v1 model file.
+        Args:
+            destination: path to where the file should be stored
+        """
+        url = 'http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2018_01_28.tar.gz'
+        os.system("curl -o ssd_mobilenet_v1_coco_2018_01_28.tar.gz {0}".format(url))
+        with tarfile.open("ssd_mobilenet_v1_coco_2018_01_28.tar.gz") as tar:
+            if not os.path.exists(destination):
+                os.makedirs(destination)
+            tar.extractall(destination)
+
+
+class SsdResnet50(Model):
+    """ Concrete implementation of the Model base class for ssd_resnet_50"""
+
+    def get_pretrained_model(self, destination):
+        """
+        Obtains a ready to use ssd_resnet_50 model file.
+        Args:
+            destination: path to where the file should be stored
+        """
+        url = "http://download.tensorflow.org/models/object_detection/" \
+              "ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03.tar.gz"
+        os.system("curl -o ssd_resnet50_v1.tar.gz {0}".format(url))
+        with tarfile.open("ssd_resnet50_v1.tar.gz") as tar:
+            if not os.path.exists(destination):
+                os.makedirs(destination)
+            tar.extractall(destination)
+
+
+def get_model(model: SupportedModels) -> Model:
+    """
+    Factory method that returns the requested model object
+    Args:
+        model: model from SupportedModels enumeration
+
+    Returns: Concrete object inheriting the Model base class
+
+    """
+    if model == SupportedModels.ssd_resnet50_v1:
+        return SsdResnet50()
+    if model == SupportedModels.ssd_mobilnet_v1:
+        return SsdMobilenetV1()
+    else:
+        raise AttributeError("The model {0} is not supported. Supported models: {1}"
+                             .format(model_name, SupportedModels.__members__.keys()))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Prepare pre-trained model for COCO object detection')
+    parser.add_argument('--model_name', type=str, default='ssd_resnet50_v1',
+                        help='model to download, default is ssd_resnet50_v1',
+                        choices=["ssd_resnet50_v1", "ssd_mobilenet_v1"])
+    parser.add_argument('--model_path', type=str, default='./model', help='directory to put models, default is ./model')
+
+    args = parser.parse_args()
+    model_name = args.model_name
+    model_path = args.model_path
+    try:
+        model = get_model(SupportedModels(model_name))
+        model.get_pretrained_model(model_path)
+    except AttributeError:
+        print("The model {0} is not supported. Supported models: {1}"
+              .format(model_name, SupportedModels.__members__.keys()))
diff --git a/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/run_benchmark.sh b/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/run_benchmark.sh
new file mode 100644
index 00000000000..8ee728de373
--- /dev/null
+++ b/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/run_benchmark.sh
@@ -0,0 +1,52 @@
+#!/bin/bash
+set -x
+
+function main {
+
+  init_params "$@"
+  run_benchmark
+
+}
+
+# init params
+function init_params {
+  batch_size=32
+  iters=100
+
+  for var in "$@"
+  do
+    case $var in
+      --input_model=*)
+          input_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --mode=*)
+          mode=$(echo $var |cut -f2 -d=)
+      ;;
+      --dataset_location=*)
+          dataset_location=$(echo $var |cut -f2 -d=)
+      ;;
+      --batch_size=*)
+          batch_size=$(echo $var |cut -f2 -d=)
+      ;;
+      --iters=*)
+          iters=$(echo $var |cut -f2 -d=)
+      ;;
+    esac
+  done
+
+}
+
+
+# run_tuning
+function run_benchmark {
+
+    python  main.py \
+            --input-graph ${input_model} \
+            --mode ${mode} \
+            --dataset_location "${dataset_location}" \
+            --batch_size ${batch_size} \
+            --benchmark \
+            --iters ${iters}
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/run_quant.sh b/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/run_quant.sh
new file mode 100644
index 00000000000..559d695f768
--- /dev/null
+++ b/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/run_quant.sh
@@ -0,0 +1,41 @@
+#!/bin/bash
+set -x
+
+function main {
+
+  init_params "$@"
+
+  run_tuning
+
+}
+
+# init params
+function init_params {
+
+  for var in "$@"
+  do
+    case $var in
+      --input_model=*)
+          input_model=$(echo "$var" |cut -f2 -d=)
+      ;;
+      --output_model=*)
+          output_model=$(echo "$var" |cut -f2 -d=)
+      ;;
+      --dataset_location=*)
+          dataset_location=$(echo "$var" |cut -f2 -d=)
+      ;;
+    esac
+  done
+
+}
+
+# run_tuning
+function run_tuning {
+  python  main.py \
+          --input-graph "${input_model}" \
+          --output_model "${output_model}" \
+          --dataset_location "${dataset_location}" \
+          --tune
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/README.md b/examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/README.md
new file mode 100644
index 00000000000..7bff08a2f84
--- /dev/null
+++ b/examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/README.md
@@ -0,0 +1,98 @@
+Step-by-Step
+============
+
+This document is used to list steps of reproducing TensorFlow Wide & Deep tuning zoo result.
+This example can run on Intel CPUs and GPUs.
+
+# Prerequisite
+
+## 1. Environment
+
+### Installation
+```shell
+# Install Intel® Neural Compressor
+pip install neural-compressor
+```
+### Install Intel Tensorflow
+```shell
+pip install intel-tensorflow
+```
+> Note: Validated TensorFlow [Version](/docs/source/installation_guide.md#validated-software-environment).
+
+### Install Intel Extension for Tensorflow
+#### Quantizing the model on Intel GPU(Mandatory to install ITEX)
+Intel Extension for Tensorflow is mandatory to be installed for quantizing the model on Intel GPUs.
+
+```shell
+pip install --upgrade intel-extension-for-tensorflow[xpu]
+```
+Please refer to the [Installation Guides](https://dgpu-docs.intel.com/installation-guides/ubuntu/ubuntu-focal-dc.html) for latest Intel GPU driver installation.
+For any more details, please follow the procedure in [install-gpu-drivers](https://github.com/intel/intel-extension-for-tensorflow/blob/main/docs/install/install_for_xpu.md#install-gpu-drivers).
+
+#### Quantizing the model on Intel CPU(Optional to install ITEX)
+Intel Extension for Tensorflow for Intel CPUs is experimental currently. It's not mandatory for quantizing the model on Intel CPUs.
+
+```shell
+pip install --upgrade intel-extension-for-tensorflow[cpu]
+```
+
+> **Note**: 
+> The version compatibility of stock Tensorflow and ITEX can be checked [here](https://github.com/intel/intel-extension-for-tensorflow#compatibility-table). Please make sure you have installed compatible Tensorflow and ITEX.
+
+### Install Additional Dependency packages
+```shell
+cd examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq
+pip install -r requirements.txt
+```
+
+### 2. Download Frozen PB
+```shell
+wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_6/wide_deep_fp32_pretrained_model.pb
+```
+
+### 3. Prepare Dataset
+Download training dataset: (8 million samples)
+```bash
+$ wget https://storage.googleapis.com/dataset-uploader/criteo-kaggle/large_version/train.csv
+```
+Download evaluation dataset (2 million samples)
+```bash
+$ wget https://storage.googleapis.com/dataset-uploader/criteo-kaggle/large_version/eval.csv
+```
+
+### 4. Process Dataset
+Process calib dataset
+```bash
+python preprocess_csv_tfrecords.py \
+        --inputcsv-datafile train.csv \
+        --calibrationcsv-datafile eval.csv \
+        --outputfile-name processed_data
+```
+Process eval dataset
+```bash
+python preprocess_csv_tfrecords.py \
+        --inputcsv-datafile eval.csv \
+        --calibrationcsv-datafile train.csv \
+        --outputfile-name processed_data
+```
+Two .tfrecords files are generated and will be used later on:
+1) train_processed_data.tfrecords
+2) eval_processed_data.tfrecords
+
+
+# Run Command
+
+## Quantization
+  ```shell
+  bash run_quant.sh --dataset_location=/path/to/datasets --input_model=/path/to/wide_deep_fp32_pretrained_model.pb --output_model=./wnd_int8_opt.pb
+  ```
+
+## Benchmark
+  ```
+  bash run_benchmark.sh --dataset_location=/path/to/datasets --input_model=./wnd_int8_opt.pb --mode=accuracy --batch_size=500
+  bash run_benchmark.sh --dataset_location=/path/to/datasets --input_model=./wnd_int8_opt.pb --mode=performance --batch_size=500
+  ```
+
+# Other
+This example takes the reference from https://github.com/IntelAI/models/tree/master/benchmarks/recommendation/tensorflow/wide_deep_large_ds.
+The pretrained model was trained with preprocessed data from dataset Criteo.
diff --git a/examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/main.py b/examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/main.py
new file mode 100644
index 00000000000..19ee3647f7e
--- /dev/null
+++ b/examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/main.py
@@ -0,0 +1,348 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2023 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+#
+
+import sys
+import os
+import numpy as np
+import argparse
+import collections
+import time
+import math
+import json
+import datetime
+
+import tensorflow as tf
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.framework import ops
+from tensorflow.core.framework import graph_pb2
+from google.protobuf import text_format
+from argparse import ArgumentParser
+from tensorflow.python.tools.optimize_for_inference_lib import optimize_for_inference
+from tensorflow.compat.v1 import graph_util
+
+
+def load_graph(model_file):
+    """This is a function to load TF graph from pb file
+
+    Args:
+        model_file (string): TF pb file local path
+
+    Returns:
+        graph: TF graph object
+  """
+    graph = tf.Graph()
+    #graph_def = tf.compat.v1.GraphDef()
+    graph_def = graph_pb2.GraphDef()
+
+    file_ext = os.path.splitext(model_file)[1]
+
+    with open(model_file, "rb") as f:
+        if file_ext == '.pbtxt':
+            text_format.Merge(f.read(), graph_def)
+        else:
+            graph_def.ParseFromString(f.read())
+
+    with graph.as_default():
+        tf.import_graph_def(graph_def, name='')
+
+    return graph
+
+
+numeric_feature_names = ["numeric_1"]
+string_feature_names = ["string_1"]
+
+def get_feature_name(compute_accuracy):
+
+    if compute_accuracy:
+        full_features_names = numeric_feature_names + string_feature_names + ["label"]
+        feature_datatypes = [tf.io.FixedLenSequenceFeature([], tf.float32, default_value=0.0, allow_missing=True)]+[tf.io.FixedLenSequenceFeature(
+                    [], tf.int64, default_value=0, allow_missing=True)]+[tf.io.FixedLenSequenceFeature([], tf.int64, default_value=0, allow_missing=True)]
+    else:
+        full_features_names = numeric_feature_names + string_feature_names
+        feature_datatypes = [tf.io.FixedLenSequenceFeature([], tf.float32, default_value=0.0, allow_missing=True)]+[tf.io.FixedLenSequenceFeature(
+                    [], tf.int64, default_value=0, allow_missing=True)]
+    return full_features_names, feature_datatypes
+
+def input_fn(data_file, num_epochs, shuffle, batch_size, compute_accuracy=True):
+    """Generate an input function for the Estimator."""
+    full_features_names, feature_datatypes = get_feature_name(compute_accuracy)
+    def _parse_function(proto):
+        f = collections.OrderedDict(
+                zip(full_features_names, feature_datatypes))
+        parsed_features = tf.io.parse_example(proto, f)
+        parsed_feature_vals_num = [tf.reshape(
+                                    parsed_features["numeric_1"], shape=[-1, 13])]
+        parsed_feature_vals_str = [tf.reshape(
+                                    parsed_features["string_1"], shape=[-1, 2]) for i in string_feature_names]
+        parsed_feature_vals = parsed_feature_vals_num + parsed_feature_vals_str
+        if compute_accuracy:
+            parsed_feature_vals_label = [tf.reshape(parsed_features[i], shape=[-1]) for i in ["label"]]
+            parsed_feature_vals = parsed_feature_vals + parsed_feature_vals_label
+        return parsed_feature_vals
+
+    # Extract lines from input files using the Dataset API.
+    dataset = tf.data.TFRecordDataset([data_file])
+    if shuffle:
+        dataset = dataset.shuffle(buffer_size=20000)
+    dataset = dataset.batch(batch_size)
+    dataset = dataset.map(_parse_function, num_parallel_calls=28)
+    dataset = dataset.prefetch(batch_size*10)
+    return dataset
+
+def evaluation_func(model, measurer=None):
+    evaluate_opt_graph.eval_inference(model)
+
+class eval_classifier_optimized_graph:
+    """Evaluate image classifier with optimized TensorFlow graph"""
+
+    def __init__(self):
+        arg_parser = ArgumentParser(description='Parse args')
+        arg_parser.add_argument('-i', '--input_graph', type=str,
+                                help='Specify the input of the model',
+                                dest='input_graph',
+                                required=True)
+        arg_parser.add_argument('-o', '--output_graph', type=str,
+                                help='Specify the output of the model',
+                                dest='output_graph')
+        arg_parser.add_argument('--calibration_data_location', type=str,
+                                help='full path of calibration data file',
+                                dest='calib_data')
+        arg_parser.add_argument('--evaluation_data_location', type=str,
+                                help='full path of validation data file',
+                                dest='eval_data',
+                                required=True)
+        arg_parser.add_argument('--batch_size', type=int,
+                                help='batch size for inference.Default is 512',
+                                default=512,
+                                dest='batch_size')
+        arg_parser.add_argument('--num_intra_threads', type=int,
+                                help='number of threads for an operator',
+                                required=False,
+                                default=0,
+                                dest='num_intra_threads')
+        arg_parser.add_argument('--num_inter_threads', type=int,
+                                help='number of threads across operators',
+                                required=False,
+                                default=0,
+                                dest='num_inter_threads')
+        arg_parser.add_argument('--kmp_blocktime', type=str,
+                                help='KMP_BLOCKTIME value',
+                                required=False,
+                                default=None,
+                                dest='kmp_blocktime')
+        arg_parser.add_argument('-r', "--accuracy",
+                                help='For accuracy measurement only.',
+                                dest='accuracy', action='store_true')
+        arg_parser.add_argument("--config", default=None,
+                                help="tuning config")
+        arg_parser.add_argument('--performance',
+                                dest='performance',
+                                action='store_true',
+                                help='run performance')
+        arg_parser.add_argument('--tune',
+                                dest='tune',
+                                action='store_true',
+                                help='use neural_compressor to tune.')
+        arg_parser.add_argument("--warmup-steps",
+                                type=int, default=50,
+                                help="number of warmup steps")
+        arg_parser.add_argument("--steps",
+                                type=int, default=2000,
+                                help="number of iterations")
+
+        arg_parser.add_argument('--env',
+                                dest='env',
+                                help='specific Tensorflow env',
+                                default='mkl')
+
+
+        self.args = arg_parser.parse_args()
+
+    def auto_tune(self):
+        """This is neural_compressor tuning part to generate a quantized pb
+        Returns:
+            graph: it will return a quantized pb
+        """
+        from neural_compressor.common import set_random_seed
+        from neural_compressor.tensorflow import StaticQuantConfig, quantize_model, Model
+
+        set_random_seed(9527)
+        infer_graph = load_graph(self.args.input_graph)
+        model = Model(infer_graph)
+        model.input_tensor_names = ["new_numeric_placeholder", "new_categorical_placeholder"]
+        model.output_tensor_names = ["import/head/predictions/probabilities"]
+
+        if self.args.calib_data:
+            quant_config = StaticQuantConfig()
+            calib_dataloader=Dataloader(self.args.calib_data, self.args.batch_size)
+            q_model = quantize_model(model, quant_config, calib_dataloader)
+            return q_model
+        print("Please provide calibration dataset!")
+
+    def eval_inference(self, infer_graph):
+        print("Run inference")
+        if isinstance(infer_graph, tf.compat.v1.GraphDef):
+            graph = tf.Graph()
+            with graph.as_default():
+                tf.import_graph_def(infer_graph, name='')
+            infer_graph = graph
+
+        data_config = tf.compat.v1.ConfigProto()
+        data_config.intra_op_parallelism_threads = self.args.num_intra_threads
+        data_config.inter_op_parallelism_threads = self.args.num_inter_threads
+        data_config.use_per_session_threads = 1
+
+        infer_config = tf.compat.v1.ConfigProto()
+        if self.args.env == 'mkl':
+            print("Set inter and intra for mkl: ")
+            print("intra_op_parallelism_threads = ", self.args.num_intra_threads)
+            print("inter_op_parallelism_threads = ", self.args.num_inter_threads)
+            infer_config.intra_op_parallelism_threads = self.args.num_intra_threads
+            infer_config.inter_op_parallelism_threads = self.args.num_inter_threads
+        infer_config.use_per_session_threads = 1
+
+        total_test_samples = sum(1 for _ in tf.compat.v1.python_io.tf_record_iterator(self.args.eval_data))
+        total_batches = math.ceil(float(total_test_samples)/self.args.batch_size)
+        placeholder_list = ['new_numeric_placeholder','new_categorical_placeholder']
+        input_tensor = [infer_graph.get_tensor_by_name(name + ":0") for name in placeholder_list]
+        output_name = "import/head/predictions/probabilities"
+        output_tensor = infer_graph.get_tensor_by_name(output_name + ":0" )
+        correctly_predicted = 0
+        evaluate_duration = 0.0
+
+        features_list = []
+        data_graph = tf.Graph()
+        with data_graph.as_default():
+            res_dataset = input_fn(self.args.eval_data, 1, False, self.args.batch_size)
+            iterator = tf.compat.v1.data.make_one_shot_iterator(res_dataset)
+            next_element = iterator.get_next()
+            with tf.compat.v1.Session(config=data_config, graph=data_graph) as data_sess:
+                for i in range(int(total_batches)):
+                    batch = data_sess.run(next_element)
+                    features=batch[0:3]
+                    features_list.append(features)
+
+        if self.args.performance:
+            iteration = 0
+            warm_up_iteration = self.args.warmup_steps
+            total_run = self.args.steps
+
+            if total_run > total_batches:
+                total_run = total_batches
+
+            with tf.compat.v1.Session(config=infer_config, graph=infer_graph) as infer_sess:
+                i = 0
+                for i in range(int(total_run)):
+                    start_time = time.time()
+                    logistic = infer_sess.run(output_tensor, dict(zip(input_tensor, features_list[iteration][0:2])))
+                    time_consume = time.time() - start_time
+
+                    if iteration > warm_up_iteration:
+                        evaluate_duration += time_consume
+
+                    iteration += 1
+                    if iteration > total_batches:
+                        iteration = 0
+                test_batches = total_run - warm_up_iteration
+        else:
+            with tf.compat.v1.Session(config=infer_config, graph=infer_graph) as infer_sess:
+                i = 0
+                for i in range(int(total_batches)):
+                    start_time = time.time()
+                    logistic = infer_sess.run(output_tensor, dict(zip(input_tensor, features_list[i][0:2])))
+                    time_consume = time.time() - start_time
+                    evaluate_duration += time_consume
+
+                    predicted_labels = np.argmax(logistic,1)
+                    correctly_predicted=correctly_predicted+np.sum(features_list[i][2] == predicted_labels)
+
+                    i=i+1
+
+                accuracy = float(correctly_predicted) / float(total_test_samples)
+                test_batches = total_batches
+
+        no_of_test_samples = test_batches * self.args.batch_size
+        latency = 1000 * float(evaluate_duration) / float(test_batches)
+        throughput = no_of_test_samples / evaluate_duration
+
+        print('--------------------------------------------------')
+        print('Total test records: %d' % no_of_test_samples)
+        print('Number of batches: %d' % test_batches)
+        print('Batch size = %d' % self.args.batch_size)
+        print('Latency: %.3f ms' % latency)
+        print('Throughput: %.3f records/sec' % throughput)
+        print('--------------------------------------------------')
+
+        if self.args.accuracy:
+            return accuracy
+
+    def run(self):
+        """ This is neural_compressor function include tuning and benchmark option """
+
+        if self.args.tune:
+            q_model = evaluate_opt_graph.auto_tune()
+            q_model.save(self.args.output_graph)
+        else:
+            if self.args.accuracy:
+                infer_graph = load_graph(self.args.input_graph)
+                acc = evaluation_func(infer_graph)
+                print("Accuracy: %.5f" % acc)
+            if self.args.performance:
+                infer_graph = load_graph(self.args.input_graph)
+                evaluation_func(infer_graph)
+
+
+class Dataloader(object):
+    def __init__(self, data_location, batch_size):
+        """dataloader generator
+
+        Args:
+            data_location (str): tf recorder local path
+            batch_size (int): dataloader batch size
+        """
+        self.batch_size = batch_size
+        self.data_file = data_location
+        self.total_samples = sum(1 for _ in tf.compat.v1.python_io.tf_record_iterator(data_location))
+        self.n = math.ceil(float(self.total_samples) / batch_size)
+        print("batch size is " + str(self.batch_size) + "," + str(self.n) + " iteration")
+
+    def __iter__(self):
+        data_graph = tf.Graph()
+        with data_graph.as_default():
+            self.dataset = input_fn(self.data_file, 1, False, self.batch_size)
+            self.dataset_iterator = tf.compat.v1.data.make_one_shot_iterator(self.dataset)
+            next_element = self.dataset_iterator.get_next()
+
+        with tf.compat.v1.Session(graph=data_graph) as sess:
+            for i in range(self.n):
+                batch = sess.run(next_element)
+                yield (batch[0:2], batch[2])
+
+    def __len__(self):
+        return self.n
+
+
+if __name__ == "__main__":
+    evaluate_opt_graph = eval_classifier_optimized_graph()
+    evaluate_opt_graph.run()
diff --git a/examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/preprocess_csv_tfrecords.py b/examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/preprocess_csv_tfrecords.py
new file mode 100644
index 00000000000..e1a82cd674c
--- /dev/null
+++ b/examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/preprocess_csv_tfrecords.py
@@ -0,0 +1,155 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2018 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+#
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import os
+import sys
+import pandas
+import argparse
+import numpy as np
+import tensorflow as tf
+if tf.version.VERSION < '2.0':
+    tf.enable_eager_execution()
+parser = argparse.ArgumentParser()
+parser.add_argument('--inputcsv-datafile', type=str,
+                    help='full path of data file e.g. eval.csv',
+                    dest='evaldatafile_path',
+                    required=True)
+parser.add_argument('--calibrationcsv-datafile', type=str,
+                    help='full path of data file of calibration/train dataset to get normalization ranges',
+                    dest='traindatafile_path',
+                    default='NULL',
+                    required=False)
+
+parser.add_argument('--outputfile-name', type=str,
+                    help='output tfrecord file name e.g. processed_eval.[tfrecords]',
+                    dest='outputfile_path',
+                    default="processed_data.tfrecords",
+                    required=False)
+
+args = parser.parse_args()
+
+eval_csv_file = args.evaldatafile_path
+train_csv_file = args.traindatafile_path
+output_file = args.outputfile_path
+
+if not os.path.isfile(eval_csv_file):
+    print("Please input a valid csv file")
+    sys.exit(1)
+
+filename, file_ext = os.path.splitext(output_file)
+in_filename, _ = os.path.splitext(os.path.basename(eval_csv_file))
+
+if file_ext != ".tfrecords":
+    output_file = output_file + ".tfrecords"
+
+output_file = "{}_{}".format(in_filename,output_file)
+csv = pandas.read_csv(eval_csv_file, header=None)
+if len(csv.columns)==39:
+    dataset_type = 'test'
+else:
+    dataset_type = 'eval'
+fill_na_dict  = {}
+if dataset_type=='test':
+    for i in range(0,13):
+        fill_na_dict[i]=0.0
+    for i in range(13,39):
+        fill_na_dict[i]=""
+else:
+    for i in range(1,14):
+        fill_na_dict[i]=0.0
+    for i in range(14,40):
+        fill_na_dict[i]=""
+csv=csv.fillna(value=fill_na_dict).values
+numeric_feature_names = ["numeric_1"]
+string_feature_names = ["string_1"]
+LABEL_COLUMN =["clicked"]
+CATEGORICAL_COLUMNS1 = ["C"+str(i)+"_embedding" for i in range(1, 27)]
+NUMERIC_COLUMNS1 = ["I"+str(i) for i in range(1, 14)]
+if dataset_type=='eval':
+    DATA_COLUMNS = LABEL_COLUMN + NUMERIC_COLUMNS1 + CATEGORICAL_COLUMNS1
+else:
+    DATA_COLUMNS = NUMERIC_COLUMNS1 + CATEGORICAL_COLUMNS1
+CATEGORICAL_COLUMNS2 = ["C"+str(i)+"_embedding" for i in range(1, 27)]
+NUMERIC_COLUMNS2 = ["I"+str(i) for i in range(1, 14)]
+
+CATEGORICAL_COLUMNS1.sort()
+NUMERIC_COLUMNS1.sort()
+no_of_rows = 0
+with open(eval_csv_file, 'r') as f:
+    if not os.path.isfile(train_csv_file):
+        nums=[line.strip('\n\r').split(',') for line in f.readlines()]
+    else: 
+        f1 = open(train_csv_file, 'r')
+        nums=[line.strip('\n\r').split(',') for line in f.readlines(
+        )]+[line.strip('\n\t').split(',') for line in f1.readlines()]
+    numpy_arr = np.array(nums)
+    numpy_arr[numpy_arr=='']='0'
+    min_list,max_list,range_list = [],[],[]
+    for i in range(len(DATA_COLUMNS)):
+        if DATA_COLUMNS[i] in NUMERIC_COLUMNS1:
+            col_min = numpy_arr[:,i].astype(np.float32).min()
+            col_max = numpy_arr[:,i].astype(np.float32).max()
+            min_list.append(col_min)
+            max_list.append(col_max)
+            range_list.append(col_max-col_min)
+    if os.path.isfile(train_csv_file):
+        f1.close()
+    print('min list',min_list)
+    print('max list',max_list)
+    print('range list',range_list)
+
+
+with tf.compat.v1.python_io.TFRecordWriter(output_file) as writer:
+    print('*****Processing data******')
+    for row in csv:
+        no_of_rows = no_of_rows+1
+        if dataset_type == 'eval':
+            unnormalized_vals = np.array(row[1:14])
+        else:
+            unnormalized_vals = np.array(row[0:13])
+        normalized_vals = (unnormalized_vals-min_list)/range_list
+        if dataset_type == 'eval':
+            new_categorical_dict = dict(zip(CATEGORICAL_COLUMNS2, row[14:40]))
+        else:
+            new_categorical_dict = dict(zip(CATEGORICAL_COLUMNS2, row[13:39]))
+        new_categorical_list = []
+        for i in CATEGORICAL_COLUMNS1:
+            if pandas.isnull(new_categorical_dict[i]):
+                new_categorical_list.append("")
+            else:
+                new_categorical_list.append(new_categorical_dict[i])
+        hash_values =  tf.compat.v1.string_to_hash_bucket_fast(
+            new_categorical_list, 1000).numpy()
+        new_numerical_dict = dict(zip(NUMERIC_COLUMNS2, normalized_vals))
+        example = tf.train.Example()
+        for i in NUMERIC_COLUMNS1:
+            example.features.feature[numeric_feature_names[0]].float_list.value.extend([new_numerical_dict[i]])
+        for i in range(0, 26):
+            example.features.feature[string_feature_names[0]].int64_list.value.extend([i])
+            example.features.feature[string_feature_names[0]].int64_list.value.extend([hash_values[i]])
+        if dataset_type == 'eval':
+            example.features.feature["label"].int64_list.value.append(row[0])
+        writer.write(example.SerializeToString())
+
+print('Total number of rows ', no_of_rows)
+print('Generated output file name :'+output_file)
diff --git a/examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/requirements.txt b/examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/requirements.txt
new file mode 100644
index 00000000000..e2f0ef81736
--- /dev/null
+++ b/examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/requirements.txt
@@ -0,0 +1,9 @@
+intel-tensorflow>=2.12.0
+Cython
+contextlib2
+pillow>=8.2.0
+lxml>=4.6.2
+matplotlib
+numpy>=1.17.4
+pycocotools
+protobuf
diff --git a/examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/run_benchmark.sh b/examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/run_benchmark.sh
new file mode 100644
index 00000000000..72ab01f2a19
--- /dev/null
+++ b/examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/run_benchmark.sh
@@ -0,0 +1,57 @@
+#!/bin/bash
+set -x
+
+function main {
+
+  init_params "$@"
+  define_mode
+  run_benchmark
+
+}
+
+# init params
+function init_params {
+  iters=1000
+  for var in "$@"
+  do
+    case $var in
+      --dataset_location=*)
+          dataset_location=$(echo $var |cut -f2 -d=)
+      ;;
+      --input_model=*)
+          input_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --mode=*)
+          mode=$(echo $var |cut -f2 -d=)
+      ;;
+      --batch_size=*)
+          batch_size=$(echo $var |cut -f2 -d=)
+      ;;
+    esac
+  done
+
+}
+
+function define_mode {
+    if [[ ${mode} == "accuracy" ]]; then
+      mode_cmd=" --accuracy"
+    elif [[ ${mode} == "performance" ]]; then
+      mode_cmd=" --performance"
+    else
+      echo "Error: No such mode: ${mode}"
+      exit 1
+    fi
+}
+
+# run_tuning
+function run_benchmark {
+    #numactl -N 0 -m 0 \
+    python main.py \
+            --input_graph ${input_model} \
+            --evaluation_data_location ${dataset_location}/eval_processed_data.tfrecords \
+            --batch_size ${batch_size} \
+            --num_inter_threads 4 \
+            ${mode_cmd}
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/run_quant.sh b/examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/run_quant.sh
new file mode 100644
index 00000000000..a8068917a27
--- /dev/null
+++ b/examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/run_quant.sh
@@ -0,0 +1,48 @@
+#!/bin/bash
+set -x
+
+function main {
+
+  init_params "$@"
+  run_tuning
+
+}
+
+# init params
+function init_params {
+
+  for var in "$@"
+  do
+    case $var in
+      --dataset_location=*)
+          dataset_location=$(echo $var |cut -f2 -d=)
+      ;;
+      --input_model=*)
+          input_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --output_model=*)
+          output_model=$(echo $var |cut -f2 -d=)
+      ;;
+      *)
+          echo "Error: No such parameter: ${var}"
+          exit 1
+      ;;
+    esac
+  done
+
+}
+
+
+# run_tuning
+function run_tuning {
+    python main.py \
+            --input_graph ${input_model} \
+            --evaluation_data_location ${dataset_location}/eval_processed_data.tfrecords \
+            --calibration_data_location ${dataset_location}/train_processed_data.tfrecords \
+            --accuracy \
+            --batch_size 1000 \
+            --output_graph ${output_model} \
+            --tune
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/README.md b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/README.md
new file mode 100644
index 00000000000..5747ba5b4ac
--- /dev/null
+++ b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/README.md
@@ -0,0 +1,73 @@
+Step-by-Step
+============
+
+This document is used to list steps of reproducing TensorFlow Intel® Neural Compressor tuning zoo result of 3dunet-mlperf.
+This example can run on Intel CPUs and GPUs.
+
+# Prerequisite
+
+## 1. Environment
+
+### Installation
+```shell
+# Install Intel® Neural Compressor
+pip install neural-compressor
+```
+
+### Install Intel Tensorflow
+```shell
+pip install intel-tensorflow
+```
+> Note: Validated TensorFlow [Version](/docs/source/installation_guide.md#validated-software-environment).
+
+### Install Intel Extension for Tensorflow
+#### Quantizing the model on Intel GPU(Mandatory to install ITEX)
+Intel Extension for Tensorflow is mandatory to be installed for quantizing the model on Intel GPUs.
+
+```shell
+pip install --upgrade intel-extension-for-tensorflow[xpu]
+```
+Please refer to the [Installation Guides](https://dgpu-docs.intel.com/installation-guides/ubuntu/ubuntu-focal-dc.html) for latest Intel GPU driver installation.
+For any more details, please follow the procedure in [install-gpu-drivers](https://github.com/intel/intel-extension-for-tensorflow/blob/main/docs/install/install_for_xpu.md#install-gpu-drivers).
+
+#### Quantizing the model on Intel CPU(Optional to install ITEX)
+Intel Extension for Tensorflow for Intel CPUs is experimental currently. It's not mandatory for quantizing the model on Intel CPUs.
+
+```shell
+pip install --upgrade intel-extension-for-tensorflow[cpu]
+```
+
+> **Note**: 
+> The version compatibility of stock Tensorflow and ITEX can be checked [here](https://github.com/intel/intel-extension-for-tensorflow#compatibility-table). Please make sure you have installed compatible Tensorflow and ITEX.
+
+## 2. Prepare Pre-trained model
+   Download the pre-trained model from the
+   [3DUnetCNN](https://storage.googleapis.com/intel-optimized-tensorflow/models/v2_7_0/3dunet_dynamic_ndhwc.pb).
+   In this example, we are using the model,
+   trained using the fold 1 BRATS 2019 data.
+   The validation files have been copied from [here](https://github.com/mlcommons/inference/tree/r0.7/vision/medical_imaging/3d-unet/folds)
+
+## 3. Prepare dataset
+
+### Download BraTS 2019 dataset
+   Please download [Brats 2019](https://www.med.upenn.edu/cbica/brats2019/data.html)
+   separately and unzip the dataset. The directory that contains the dataset files will be
+   passed to the launch script when running the benchmarking script.
+
+### Prepare Calibration set
+   The calibration set is the forty images listed in brats_cal_images_list.txt. They are randomly selected from Fold 0, Fold 2, Fold 3, and Fold 4 of BraTS 2019 Training Dataset.
+
+
+# Run command
+
+## Quantization
+
+
+## Benchmark
+
+* `export nnUNet_preprocessed=<path/to/build>/build/preprocessed_data`
+* `export nnUNet_raw_data_base=<path/to/build>/build/raw_data`
+* `export RESULTS_FOLDER=<path/to/build>/build/result`
+* `pip install -r requirements.txt`
+* `python run_accuracy.py --input-model=<path/to/model_file> --data-location=<path/to/dataset> --calib-preprocess=<path/to/calibrationset> --iters=100 --batch-size=1 --mode=benchmark --bfloat16 0`
+
diff --git a/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/__init__.py b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/__init__.py
new file mode 100644
index 00000000000..0a18c579d8b
--- /dev/null
+++ b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/__init__.py
@@ -0,0 +1,19 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2018 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+#
\ No newline at end of file
diff --git a/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/brats_cal_images_list.txt b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/brats_cal_images_list.txt
new file mode 100644
index 00000000000..69276e67b6a
--- /dev/null
+++ b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/brats_cal_images_list.txt
@@ -0,0 +1,40 @@
+HGG__BraTS19_2013_18_1
+HGG__BraTS19_2013_20_1
+HGG__BraTS19_CBICA_AAP_1
+HGG__BraTS19_CBICA_ABN_1
+HGG__BraTS19_CBICA_ABO_1
+HGG__BraTS19_CBICA_ALU_1
+HGG__BraTS19_CBICA_ANZ_1
+HGG__BraTS19_CBICA_APY_1
+HGG__BraTS19_CBICA_AQJ_1
+HGG__BraTS19_CBICA_AQZ_1
+HGG__BraTS19_CBICA_ASN_1
+HGG__BraTS19_CBICA_ASY_1
+HGG__BraTS19_CBICA_AUW_1
+HGG__BraTS19_CBICA_AXJ_1
+HGG__BraTS19_CBICA_AXM_1
+HGG__BraTS19_CBICA_AYG_1
+HGG__BraTS19_CBICA_AYU_1
+HGG__BraTS19_CBICA_AZD_1
+HGG__BraTS19_CBICA_BAX_1
+HGG__BraTS19_CBICA_BGR_1
+HGG__BraTS19_CBICA_BHV_1
+HGG__BraTS19_TCIA01_235_1
+HGG__BraTS19_TCIA02_394_1
+HGG__BraTS19_TCIA02_473_1
+HGG__BraTS19_TCIA02_606_1
+HGG__BraTS19_TCIA03_419_1
+HGG__BraTS19_TCIA04_192_1
+HGG__BraTS19_TCIA04_479_1
+HGG__BraTS19_TCIA06_372_1
+HGG__BraTS19_TCIA08_278_1
+LGG__BraTS19_2013_28_1
+LGG__BraTS19_TCIA09_462_1
+LGG__BraTS19_TCIA10_130_1
+LGG__BraTS19_TCIA10_202_1
+LGG__BraTS19_TCIA10_346_1
+LGG__BraTS19_TCIA10_387_1
+LGG__BraTS19_TCIA10_628_1
+LGG__BraTS19_TCIA12_470_1
+LGG__BraTS19_TCIA13_621_1
+LGG__BraTS19_TCIA13_653_1
diff --git a/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/main.py b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/main.py
new file mode 100644
index 00000000000..5ba82875420
--- /dev/null
+++ b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/main.py
@@ -0,0 +1,219 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2021 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+#
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import time
+from argparse import ArgumentParser
+import os
+import pickle
+import sys
+import math
+import array
+
+import numpy as np
+import tensorflow as tf
+from tensorflow.python.framework import dtypes
+from tensorflow.core.protobuf import rewriter_config_pb2
+from tensorflow.python.tools.optimize_for_inference_lib import optimize_for_inference
+from nnunet.evaluation.region_based_evaluation import evaluate_regions, get_brats_regions
+
+from nnUNet.setup import setup
+from nnUNet.postprocess import postprocess_output
+
+INPUTS = 'input'
+OUTPUTS = 'Identity'
+
+if __name__ == "__main__":
+    """Evaluate 3d_unet with optimized TensorFlow graph"""
+    def get_args():
+        arg_parser = ArgumentParser(description='Parse args')
+
+        arg_parser.add_argument('-m', "--mode",
+                                help="One of three options: 'benchmark'/'accuracy'/'tune'.")
+        arg_parser.add_argument('-n', "--iters",
+                                help='The number of iteration. shall > warmup num(10)',
+                                type=int, default=20)
+        arg_parser.add_argument('-e', "--num-inter-threads",
+                                help='The number of inter-thread.',
+                                dest='num_inter_threads', type=int, default=0)
+        arg_parser.add_argument('-a', "--num-intra-threads",
+                                help='The number of intra-thread.',
+                                dest='num_intra_threads', type=int, default=0)
+        arg_parser.add_argument('-i', "--input-model",
+                                help='Specify the input graph.',
+                                dest='input_model')
+        arg_parser.add_argument('-o', "--output-model",
+                                help='Specify the output graph.',
+                                dest='output_model')
+        arg_parser.add_argument('-c', "--calib-preprocess",
+                                help='Specify calibration preprocess dir.',
+                                dest='calib_preprocess')
+        arg_parser.add_argument('-d', "--data-location",
+                                help='Specify the location of the data.',
+                                dest="data_location", default=None)
+        arg_parser.add_argument("--batch-size", dest="batch_size", type=int, default=1)
+        arg_parser.add_argument("--bfloat16", type=int, default=0)
+
+        args = arg_parser.parse_args()
+        print(args)
+        return args
+
+    def eval_func(graph):
+        print("Run inference for accuracy")
+        args = get_args()
+        #setup(args.data_location, args.input_model)
+
+        output_graph = optimize_for_inference(graph.as_graph_def(), [INPUTS], [OUTPUTS],
+                            dtypes.float32.as_datatype_enum, False)
+        tf.import_graph_def(output_graph, name="")
+
+        input_tensor = graph.get_tensor_by_name('input:0')
+        output_tensor = graph.get_tensor_by_name('Identity:0')
+
+        config = tf.compat.v1.ConfigProto()
+        config.intra_op_parallelism_threads=args.num_intra_threads
+        config.inter_op_parallelism_threads=args.num_inter_threads
+        if args.bfloat16:
+            config.graph_options.rewrite_options.auto_mixed_precision_mkl = rewriter_config_pb2.RewriterConfig.ON
+
+        sess = tf.compat.v1.Session(graph=graph, config=config)
+        if args.mode:
+            print("Inference with real data")
+            preprocessed_data_dir = os.path.join(args.data_location, "preprocessed_data")
+            with open(os.path.join(preprocessed_data_dir, "preprocessed_files.pkl"), "rb") as f:
+                preprocessed_files = pickle.load(f)
+
+            dictionaries = []
+            for preprocessed_file in preprocessed_files:
+                with open(os.path.join(preprocessed_data_dir, preprocessed_file + ".pkl"), "rb") as f:
+                    dct = pickle.load(f)[1]
+                    dictionaries.append(dct)
+
+            count = len(preprocessed_files)
+            predictions = [None] * count
+            validation_indices = list(range(0,count))
+            print("Found {:d} preprocessed files".format(count))
+            loaded_files = {}
+            batch_size = args.batch_size
+  
+            # Get the number of steps based on batch size
+            steps = count#math.ceil(count/batch_size)
+            warmup = 10
+            assert args.iters >= warmup, 'iteration must be larger than warmup'
+            time_list=[]
+            for i in range(steps):
+                print("Iteration {} ...".format(i))
+                test_data_index = validation_indices[i]#validation_indices[i * batch_size:(i + 1) * batch_size]
+                file_name = preprocessed_files[test_data_index]
+                with open(os.path.join(preprocessed_data_dir, "{:}.pkl".format(file_name)), "rb") as f:
+                    data = pickle.load(f)[0]
+                if args.mode == 'performance' and i < args.iters:
+                    time_start = time.time()
+                    predictions[i] = sess.run(output_tensor, feed_dict={input_tensor: data[np.newaxis, ...]})[0].astype(np.float32)
+                    duration = time.time() - time_start
+                    time_list.append(duration)
+                else:
+                    predictions[i] = sess.run(output_tensor, feed_dict={input_tensor: data[np.newaxis, ...]})[0].astype(np.float32)
+            if args.mode == 'performance':
+                latency = np.array(time_list[warmup: ]).mean() / args.batch_size
+                print('Batch size = {}'.format(args.batch_size))
+                print('Latency: {:.3f} ms'.format(latency * 1000))
+                print('Throughput: {:.3f} items/sec'.format(1./ latency))
+            else:
+                output_folder = os.path.join(args.data_location, "postprocessed_data")
+                output_files = preprocessed_files
+                # Post Process
+                postprocess_output(predictions, dictionaries, validation_indices, output_folder, output_files)
+
+                ground_truths = os.path.join(args.data_location, \
+                     "raw_data/nnUNet_raw_data/Task043_BraTS2019/labelsTr")
+                # Run evaluation
+                print("Running evaluation...")
+                evaluate_regions(output_folder, ground_truths, get_brats_regions())
+                # Load evaluation summary
+                print("Loading evaluation summary...")
+                accuracy=0.0
+                with open(os.path.join(output_folder, "summary.csv")) as f:
+                    for line in f:
+                        words = line.split(",")
+                        if words[0] == "mean":
+                            whole = float(words[1])
+                            core = float(words[2])
+                            enhancing = float(words[3])
+                            mean = (whole + core + enhancing) / 3
+                            accuracy=mean
+                            print("Batch size =", args.batch_size)
+                            print("Accuracy is {:.5f}".format(mean))
+                            break
+                print("Done!")
+                return accuracy
+
+    def load_graph(file_name):
+        tf.compat.v1.logging.info('Loading graph from: ' + file_name)
+        with tf.io.gfile.GFile(file_name, "rb") as f:
+            graph_def = tf.compat.v1.GraphDef()
+            graph_def.ParseFromString(f.read())
+        with tf.Graph().as_default() as graph:
+            tf.import_graph_def(graph_def, name='')
+        return graph
+
+    class CalibrationDL():
+        def __init__(self):
+            path = os.path.abspath(os.path.expanduser(
+                './brats_cal_images_list.txt'))
+            with open(path, 'r') as f:
+                self.preprocess_files = [line.rstrip() for line in f]
+
+            self.loaded_files = {}
+            self.batch_size = 1
+
+        def __getitem__(self, sample_id):
+            file_name = self.preprocess_files[sample_id]
+            print("Loading file {:}".format(file_name))
+            with open(os.path.join(args.calib_preprocess, "{:}.pkl".format(file_name)), "rb") as f:
+                self.loaded_files[sample_id] = pickle.load(f)[0]
+            # note that calibration phase does not care label, here we return 0 for label free case.
+            return self.loaded_files[sample_id], 0
+
+        def __len__(self):
+            self.count = len(self.preprocess_files)
+            return self.count
+
+
+    args = get_args()
+    print(args)
+    graph = load_graph(args.input_model)
+    if args.mode == 'tune':
+        from neural_compressor.common import set_random_seed
+        from neural_compressor.tensorflow.utils import BaseDataLoader
+        from neural_compressor.tensorflow import StaticQuantConfig, quantize_model
+
+        set_random_seed(9527)
+        quant_config = StaticQuantConfig()
+        calib_dataloader=BaseDataloader(dataset=CalibrationDL())
+        q_model = quantize_model(graph, quant_config, calib_dataloader)
+        try:
+            q_model.save(args.output_model)
+        except Exception as e:
+            print("Failed to save model due to {}".format(str(e)))
+    else:
+        eval_func(graph)
diff --git a/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/Task043_BraTS_2019.py b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/Task043_BraTS_2019.py
new file mode 100644
index 00000000000..d26521276d6
--- /dev/null
+++ b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/Task043_BraTS_2019.py
@@ -0,0 +1,125 @@
+#    Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+#    Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
+#
+#    Licensed under the Apache License, Version 2.0 (the "License");
+#    you may not use this file except in compliance with the License.
+#    You may obtain a copy of the License at
+#
+#        http://www.apache.org/licenses/LICENSE-2.0
+#
+#    Unless required by applicable law or agreed to in writing, software
+#    distributed under the License is distributed on an "AS IS" BASIS,
+#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#    See the License for the specific language governing permissions and
+#    limitations under the License.
+
+# This file is copied from nnUnet/nnunet/dataset_conversion/Task043_BraTS_2019.py, except that
+# the validation/test set part is removed and downloaded_data_dir is now configurable.
+
+import argparse
+import numpy as np
+from collections import OrderedDict
+import os
+import sys
+
+from batchgenerators.utilities.file_and_folder_operations import *
+from nnunet.paths import nnUNet_raw_data
+import SimpleITK as sitk
+import shutil
+
+def copy_BraTS_segmentation_and_convert_labels(in_file, out_file):
+    # use this for segmentation only!!!
+    # nnUNet wants the labels to be continuous. BraTS is 0, 1, 2, 4 -> we make that into 0, 1, 2, 3
+    img = sitk.ReadImage(in_file)
+    img_npy = sitk.GetArrayFromImage(img)
+
+    uniques = np.unique(img_npy)
+    for u in uniques:
+        if u not in [0, 1, 2, 4]:
+            raise RuntimeError('unexpected label')
+
+    seg_new = np.zeros_like(img_npy)
+    seg_new[img_npy == 4] = 3
+    seg_new[img_npy == 2] = 1
+    seg_new[img_npy == 1] = 2
+    img_corr = sitk.GetImageFromArray(seg_new)
+    img_corr.CopyInformation(img)
+    sitk.WriteImage(img_corr, out_file)
+
+def task_setup(downloaded_data_dir):
+    """
+    REMEMBER TO CONVERT LABELS BACK TO BRATS CONVENTION AFTER PREDICTION!
+    """
+
+    task_name = "Task043_BraTS2019"
+    print(task_name)
+    print(downloaded_data_dir)
+    print(nnUNet_raw_data)
+
+    target_base = join(nnUNet_raw_data, task_name)
+    if not os.path.isdir(target_base):
+        target_imagesTr = join(target_base, "imagesTr")
+        target_imagesVal = join(target_base, "imagesVal")
+        target_imagesTs = join(target_base, "imagesTs")
+        target_labelsTr = join(target_base, "labelsTr")
+
+        maybe_mkdir_p(target_imagesTr)
+        maybe_mkdir_p(target_imagesVal)
+        maybe_mkdir_p(target_imagesTs)
+        maybe_mkdir_p(target_labelsTr)
+
+        patient_names = []
+        for tpe in ["HGG", "LGG"]:
+            cur = join(downloaded_data_dir, tpe)
+            for p in subdirs(cur, join=False):
+                patdir = join(cur, p)
+                patient_name = tpe + "__" + p
+                patient_names.append(patient_name)
+                t1 = join(patdir, p + "_t1.nii.gz")
+                t1c = join(patdir, p + "_t1ce.nii.gz")
+                t2 = join(patdir, p + "_t2.nii.gz")
+                flair = join(patdir, p + "_flair.nii.gz")
+                seg = join(patdir, p + "_seg.nii.gz")
+
+                assert all([
+                    isfile(t1),
+                    isfile(t1c),
+                    isfile(t2),
+                    isfile(flair),
+                    isfile(seg)
+                ]), "%s" % patient_name
+
+                shutil.copy(t1, join(target_imagesTr, patient_name + "_0000.nii.gz"))
+                shutil.copy(t1c, join(target_imagesTr, patient_name + "_0001.nii.gz"))
+                shutil.copy(t2, join(target_imagesTr, patient_name + "_0002.nii.gz"))
+                shutil.copy(flair, join(target_imagesTr, patient_name + "_0003.nii.gz"))
+
+                copy_BraTS_segmentation_and_convert_labels(seg, join(target_labelsTr, patient_name + ".nii.gz"))
+
+        json_dict = OrderedDict()
+        json_dict['name'] = "BraTS2019"
+        json_dict['description'] = "nothing"
+        json_dict['tensorImageSize'] = "4D"
+        json_dict['reference'] = "see BraTS2019"
+        json_dict['licence'] = "see BraTS2019 license"
+        json_dict['release'] = "0.0"
+        json_dict['modality'] = {
+            "0": "T1",
+            "1": "T1ce",
+            "2": "T2",
+            "3": "FLAIR"
+        }
+        json_dict['labels'] = {
+            "0": "background",
+            "1": "edema",
+            "2": "non-enhancing",
+            "3": "enhancing",
+        }
+        json_dict['numTraining'] = len(patient_names)
+        json_dict['numTest'] = 0
+        json_dict['training'] = [{'image': "./imagesTr/%s.nii.gz" % i, "label": "./labelsTr/%s.nii.gz" % i} for i in
+                                patient_names]
+        json_dict['test'] = []
+
+        save_json(json_dict, join(target_base, "dataset.json"))
+    print("DONE")
diff --git a/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/__init__.py b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/__init__.py
new file mode 100644
index 00000000000..0a18c579d8b
--- /dev/null
+++ b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/__init__.py
@@ -0,0 +1,19 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2018 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+#
\ No newline at end of file
diff --git a/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/folds/fold0_validation.txt b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/folds/fold0_validation.txt
new file mode 100644
index 00000000000..57eeeb651c5
--- /dev/null
+++ b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/folds/fold0_validation.txt
@@ -0,0 +1,67 @@
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diff --git a/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/folds/fold1_validation.txt b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/folds/fold1_validation.txt
new file mode 100644
index 00000000000..d24f39b67c4
--- /dev/null
+++ b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/folds/fold1_validation.txt
@@ -0,0 +1,67 @@
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diff --git a/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/folds/fold2_validation.txt b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/folds/fold2_validation.txt
new file mode 100644
index 00000000000..c468e57417d
--- /dev/null
+++ b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/folds/fold2_validation.txt
@@ -0,0 +1,67 @@
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diff --git a/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/folds/fold3_validation.txt b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/folds/fold3_validation.txt
new file mode 100644
index 00000000000..171a51a02a8
--- /dev/null
+++ b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/folds/fold3_validation.txt
@@ -0,0 +1,67 @@
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diff --git a/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/folds/fold4_validation.txt b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/folds/fold4_validation.txt
new file mode 100644
index 00000000000..0fc2a8bc9cc
--- /dev/null
+++ b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/folds/fold4_validation.txt
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diff --git a/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/postprocess.py b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/postprocess.py
new file mode 100644
index 00000000000..e5590bdb338
--- /dev/null
+++ b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/postprocess.py
@@ -0,0 +1,72 @@
+# coding=utf-8
+# Copyright (c) 2020 NVIDIA CORPORATION. All rights reserved.
+# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+from multiprocessing import Pool
+import os
+from nnunet.inference.segmentation_export import save_segmentation_nifti_from_softmax
+
+def load_predictions(predictions, dictionaries, validation_indices):
+    assert len(predictions) == len(dictionaries),"Number of predictions does not match number of samples in validation set!"
+    padded_shape = [224,224,160]
+    results = [None for i in range(len(predictions))]
+    for i in range(len(predictions)):
+        qsl_idx = validation_indices[i]
+        prediction = predictions[qsl_idx]
+        assert qsl_idx >= 0 and qsl_idx < len(predictions), "Invalid qsl_idx!"
+        raw_shape = list(dictionaries[qsl_idx]["size_after_cropping"])
+        # Remove the padded part
+        pad_before = [(p - r) // 2 for p, r in zip(padded_shape, raw_shape)]
+        pad_after = [-(p - r - b) for p, r, b in zip(padded_shape, raw_shape, pad_before)]
+        result_shape = (4,) + tuple(padded_shape)
+        result = np.reshape(prediction, result_shape).astype(np.float32)
+        results[qsl_idx] = result[:, pad_before[0]:pad_after[0], pad_before[1]:pad_after[1], pad_before[2]:pad_after[2]]
+    assert all([i is not None for i in results]), "Missing some results!"
+    return results
+
+def postprocess_output(predictions, dictionaries, validation_indices, output_folder, output_files):
+    processed_predictions = load_predictions(predictions, dictionaries, validation_indices)
+    print("Running postprocessing with multiple threads...")
+    force_separate_z=None
+    interp_order=3
+    interp_order_z=0
+    num_threads_nifti_save = 12
+    all_in_gpu = "None"
+    print("Saving predictions...")
+    pool = Pool(num_threads_nifti_save)
+    results = []
+    for i, output_filename in enumerate(output_files):
+        print(i, "/", len(output_files))
+        output_filename = os.path.join(output_folder, output_filename + ".nii.gz")
+        softmax_mean = processed_predictions[i]
+        dct = dictionaries[i]
+        bytes_per_voxel = 4
+        if all_in_gpu:
+            bytes_per_voxel = 2  # if all_in_gpu then the return value is half (float16)
+        if np.prod(softmax_mean.shape) > (2e9 / bytes_per_voxel * 0.85):  # * 0.85 just to be save
+            print(
+                "This output is too large for python process-process communication. Saving output temporarily to disk")
+            np.save(output_filename[:-7] + ".npy", softmax_mean)
+            softmax_mean = output_filename[:-7] + ".npy"
+
+        results.append(pool.starmap_async(save_segmentation_nifti_from_softmax,
+                                          ((softmax_mean, output_filename, dct, interp_order, None, None, None,
+                                            None, None, force_separate_z, interp_order_z),)
+                                          ))
+
+    _ = [i.get() for i in results]
+    pool.close()
+    pool.join()
diff --git a/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/preprocess.py b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/preprocess.py
new file mode 100644
index 00000000000..048eb0e91cb
--- /dev/null
+++ b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/preprocess.py
@@ -0,0 +1,109 @@
+# coding=utf-8
+# Copyright (c) 2020 NVIDIA CORPORATION. All rights reserved.
+# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This file has been copied from 
+# https://github.com/mlcommons/inference/blob/r0.7/vision/medical_imaging/3d-unet/preprocess.py
+
+import argparse
+import numpy
+import os
+import pickle
+import sys
+import torch
+
+from batchgenerators.augmentations.utils import pad_nd_image
+from batchgenerators.utilities.file_and_folder_operations import subfiles
+from nnunet.training.model_restore import load_model_and_checkpoint_files
+from nnunet.inference.predict import preprocess_multithreaded
+
+def preprocess_MLPerf(model, checkpoint_name, folds, fp16, list_of_lists, output_filenames, preprocessing_folder, num_threads_preprocessing):
+    assert len(list_of_lists) == len(output_filenames)
+    print("loading parameters for folds", folds)
+    trainer, params = load_model_and_checkpoint_files(model, folds, fp16, checkpoint_name=checkpoint_name)
+
+    print("starting preprocessing generator")
+    preprocessing = preprocess_multithreaded(trainer, list_of_lists, output_filenames, num_threads_preprocessing, None)
+    print("Preprocessing images...")
+    all_output_files = []
+
+    for preprocessed in preprocessing:
+        output_filename, (d, dct) = preprocessed
+
+        all_output_files.append(output_filename)
+        if isinstance(d, str):
+            data = np.load(d)
+            os.remove(d)
+            d = data
+
+        # Pad to the desired full volume
+        d = pad_nd_image(d, trainer.patch_size, "constant", None, False, None)
+
+        with open(os.path.join(preprocessing_folder, output_filename+ ".pkl"), "wb") as f:
+            pickle.dump([d, dct], f)
+        f.close()
+
+    return  all_output_files
+
+
+def preprocess_setup(preprocessed_data_dir):
+    print("Preparing for preprocessing data...")
+
+    # Validation set is fold 1
+    fold = 1
+    import sys
+    import os
+    CURRENT_DIR = os.path.split(os.path.abspath(__file__))[0]
+    #validation_fold_file = '/workspace/intelai_models/inference/nnUNet/folds/fold1_validation.txt'
+    validation_fold_file = os.path.join(CURRENT_DIR, 'folds/fold1_validation.txt')
+    # Make sure the model exists
+    model_dir = 'build/result/nnUNet/3d_fullres/Task043_BraTS2019/nnUNetTrainerV2__nnUNetPlansv2.mlperf.1'
+    model_path = os.path.join(model_dir, "plans.pkl")
+    assert os.path.isfile(model_path), "Cannot find the model file {:}!".format(model_path)
+    checkpoint_name = "model_final_checkpoint"
+
+    # Other settings
+    fp16 = False
+    num_threads_preprocessing = 12
+    raw_data_dir = 'build/raw_data/nnUNet_raw_data/Task043_BraTS2019/imagesTr'
+
+    # Open list containing validation images from specific fold (e.g. 1)
+    validation_files = []
+    with open(validation_fold_file) as f:
+        for line in f:
+            validation_files.append(line.rstrip())
+
+    # Create output and preprocessed directory
+    if not os.path.isdir(preprocessed_data_dir):
+        os.makedirs(preprocessed_data_dir)
+
+    # Create list of images locations (i.e. 4 images per case => 4 modalities)
+    all_files = subfiles(raw_data_dir, suffix=".nii.gz", join=False, sort=True)
+    list_of_lists = [[os.path.join(raw_data_dir, i) for i in all_files if i[:len(j)].startswith(j) and
+                      len(i) == (len(j) + 12)] for j in validation_files]
+
+    # Preprocess images, returns filenames list
+    # This runs in multiprocess
+    print("Actually preprocessing data...")
+    
+    preprocessed_files = preprocess_MLPerf(model_dir, checkpoint_name, fold, fp16, list_of_lists,
+        validation_files, preprocessed_data_dir, num_threads_preprocessing)
+
+    print("Saving metadata of the preprocessed data...")
+    with open(os.path.join(preprocessed_data_dir, "preprocessed_files.pkl"), "wb") as f:
+        pickle.dump(preprocessed_files, f)
+
+    print("Preprocessed data saved to {:}".format(preprocessed_data_dir))
+    print("Done!")
diff --git a/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/setup.py b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/setup.py
new file mode 100644
index 00000000000..bf4d5981497
--- /dev/null
+++ b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/nnUNet/setup.py
@@ -0,0 +1,81 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2021 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# SPDX-License-Identifier: EPL-2.0
+#
+
+import os, shutil
+import argparse
+import sys
+import zipfile
+#import sys
+#print(sys.path)
+#sys.path.append('/home/sys_dltest/lpot/lz/frameworks.ai.models.intel-models/models/image_segmentation/tensorflow/3d_unet_mlperf')
+from nnUNet.Task043_BraTS_2019 import task_setup
+from nnUNet.preprocess import preprocess_setup
+
+BUILD_DIR = 'build'
+RAW_DATA_DIR = BUILD_DIR + '/raw_data'
+PREPROCESSED_DATA_DIR = BUILD_DIR + '/preprocessed_data'
+POSTPROCESSED_DATA_DIR = BUILD_DIR + '/postprocessed_data'
+MODEL_DIR = BUILD_DIR + '/model'
+RESULT_DIR = BUILD_DIR + '/result'
+TF_MODEL = '224_224_160.pb'
+OTHER_FILES = 'fold_1.zip'
+
+def create_directories():
+    print("Creating directories")
+    if not os.path.isdir(BUILD_DIR):
+        os.makedirs(BUILD_DIR)
+    if not os.path.isdir(RAW_DATA_DIR):
+        os.makedirs(RAW_DATA_DIR)
+    if not os.path.isdir(PREPROCESSED_DATA_DIR):
+        os.makedirs(PREPROCESSED_DATA_DIR)
+    if not os.path.isdir(POSTPROCESSED_DATA_DIR):
+        os.makedirs(POSTPROCESSED_DATA_DIR)
+    if not os.path.isdir(RESULT_DIR):
+        os.makedirs(RESULT_DIR)
+    if not os.path.isdir(MODEL_DIR):
+        os.makedirs(MODEL_DIR)
+
+def download_model(input_graph):
+    pwd = os.getcwd()
+    os.chdir(os.path.join(pwd, MODEL_DIR))
+    if input_graph == 'NONE':
+        print("Downloading TF model from Zenodo")
+        if not os.path.isfile(TF_MODEL):
+            os.system('wget -O 224_224_160.pb https://zenodo.org/record/3928991/files/224_224_160.pb?download=1;')
+    os.chdir(os.path.join(pwd, RESULT_DIR))
+    if not os.path.isfile(OTHER_FILES):
+        os.system('wget -O fold_1.zip https://zenodo.org/record/3904106/files/fold_1.zip?download=1;')
+        zip_file = "fold_1.zip"
+        #legacy bitmap issue https://bugzilla.redhat.com/show_bug.cgi?id=1802689
+        if (not os.path.isfile(OTHER_FILES)):
+            os.system('curl -O --output fold_1.zip https://zenodo.org/record/3904106/files/fold_1.zip')
+        try:
+            with zipfile.ZipFile(zip_file) as z:
+                z.extractall()
+                print("Extracted all")
+        except:
+            print("Could not extract fold_1.zip")
+    os.chdir(pwd)
+
+def setup(downloaded_data_dir, input_graph='NONE'):
+    create_directories()
+    download_model(input_graph)
+    task_setup(downloaded_data_dir)
+    preprocess_setup(PREPROCESSED_DATA_DIR)
diff --git a/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/requirements.txt b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/requirements.txt
new file mode 100644
index 00000000000..4e85853747e
--- /dev/null
+++ b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/requirements.txt
@@ -0,0 +1 @@
+nnunet
diff --git a/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/run_benchmark.sh b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/run_benchmark.sh
new file mode 100644
index 00000000000..7e869e7cca7
--- /dev/null
+++ b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/run_benchmark.sh
@@ -0,0 +1,61 @@
+#!/bin/bash
+set -x
+
+function main {
+
+  init_params "$@"
+  export BUILD_DIR=${dataset_location}
+  export nnUNet_preprocessed=${BUILD_DIR}/preprocessed_data
+  export nnUNet_raw_data_base=${BUILD_DIR}/raw_data
+  export RESULTS_FOLDER=${BUILD_DIR}/result
+  run_benchmark
+
+}
+
+# init params
+function init_params {
+  iters=100
+  batch_size=1
+  for var in "$@"
+  do
+    case $var in
+      --mode=*)
+          mode=$(echo $var |cut -f2 -d=)
+      ;;
+      --dataset_location=*)
+          dataset_location=$(echo $var |cut -f2 -d=)
+      ;;
+      --input_model=*)
+          input_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --batch_size=*)
+          batch_size=$(echo $var |cut -f2 -d=)
+      ;;
+      *)
+          echo "Error: No such parameter: ${var}"
+          exit 1
+      ;;
+    esac
+  done
+
+}
+
+
+# run_benchmark
+function run_benchmark {
+    if [[ ${bfloat16} == "true" ]]; then
+        extra_cmd="--bfloat16"
+    else
+        extra_cmd=""
+    fi
+
+    python main.py \
+      --input-model=${input_model} \
+      --data-location=${dataset_location} \
+      --calib-preprocess=${BUILD_DIR}/calib_preprocess \
+      --batch-size=${batch_size} \
+      --mode=${mode} \
+      ${extra_cmd}
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/run_quant.sh b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/run_quant.sh
new file mode 100644
index 00000000000..79256545613
--- /dev/null
+++ b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/run_quant.sh
@@ -0,0 +1,48 @@
+#!/bin/bash
+set -x
+
+function main {
+
+  init_params "$@"
+  export BUILD_DIR=${dataset_location}
+  export nnUNet_preprocessed=${BUILD_DIR}/preprocessed_data
+  export nnUNet_raw_data_base=${BUILD_DIR}/raw_data
+  export RESULTS_FOLDER=${BUILD_DIR}/result
+  run_tuning
+
+}
+
+# init params
+function init_params {
+  for var in "$@"
+  do
+    case $var in
+      --dataset_location=*)
+          dataset_location=$(echo $var |cut -f2 -d=)
+      ;;
+      --input_model=*)
+          input_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --output_model=*)
+          output_model=$(echo $var |cut -f2 -d=)
+      ;;
+      *)
+          echo "Error: No such parameter: ${var}"
+          exit 1
+      ;;
+    esac
+  done
+
+}
+
+# run_tuning
+function run_tuning {
+    python main.py \
+      --input-model=${input_model} \
+      --output-model=${output_model} \
+      --data-location=${dataset_location} \
+      --calib-preprocess=${BUILD_DIR}/calib_preprocess \
+      --mode=tune 
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/README.md b/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/README.md
new file mode 100644
index 00000000000..2802279a8c3
--- /dev/null
+++ b/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/README.md
@@ -0,0 +1,131 @@
+Step-by-Step
+============
+
+This document is used to list steps of reproducing TensorFlow style transfer Intel® Neural Compressor tuning zoo result.
+This example can run on Intel CPUs and GPUs.
+
+# Prerequisite
+
+## Prerequisite
+
+### Installation
+```shell
+# Install Intel® Neural Compressor
+pip install neural-compressor
+```
+### Install Intel Tensorflow
+```shell
+pip install intel-tensorflow
+```
+> Note: Supported Tensorflow [Version](../../../../../../README.md#supported-frameworks).
+
+### Install Additional Dependency packages
+```shell
+cd examples/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq 
+pip install -r requirements.txt
+```
+
+### Install Intel Extension for Tensorflow
+#### Quantizing the model on Intel GPU(Mandatory to install ITEX)
+Intel Extension for Tensorflow is mandatory to be installed for quantizing the model on Intel GPUs.
+
+```shell
+pip install --upgrade intel-extension-for-tensorflow[xpu]
+```
+For any more details, please follow the procedure in [install-gpu-drivers](https://github.com/intel/intel-extension-for-tensorflow/blob/main/docs/install/install_for_xpu.md#install-gpu-drivers)
+
+#### Quantizing the model on Intel CPU(Optional to install ITEX)
+Intel Extension for Tensorflow for Intel CPUs is experimental currently. It's not mandatory for quantizing the model on Intel CPUs.
+
+```shell
+pip install --upgrade intel-extension-for-tensorflow[cpu]
+```
+
+> **Note**: 
+> The version compatibility of stock Tensorflow and ITEX can be checked [here](https://github.com/intel/intel-extension-for-tensorflow#compatibility-table). Please make sure you have installed compatible Tensorflow and ITEX.
+
+### 2. Prepare Pretrained model
+
+#### Automated approach
+Run the `prepare_model.py` script located in `./examples/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq`.
+
+```
+usage: prepare_model.py [-h] [--model_path MODEL_PATH]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --model_path MODEL_PATH directory to put models, default is ./model
+```
+
+#### Manual approach
+
+```shell
+wget https://storage.googleapis.com/download.magenta.tensorflow.org/models/arbitrary_style_transfer.tar.gz
+tar -xvzf arbitrary_style_transfer.tar.gz ./model
+```
+
+### 3. Prepare Dataset
+There are two folders named style_images and content_images in current folder. Please use these two folders to generated stylized images for test. And you can also prepare your own style_images or content_images.
+
+
+# Run Command
+  ```shell
+  python style_tune.py --output_dir=./result --style_images_paths=./style_images --content_images_paths=./content_images --input_model=./model/model.ckpt
+  ```
+
+
+## Quantization Config
+
+The Quantization Config class has default parameters setting for running on Intel CPUs. If running this example on Intel GPUs, the 'backend' parameter should be set to 'itex' and the 'device' parameter should be set to 'gpu'.
+
+```
+config = PostTrainingQuantConfig(
+    device="gpu",
+    backend="itex",
+    ...
+    )
+```
+
+## Quantization
+  ```shell
+  bash run_quant.sh --dataset_location=style_images/,content_images/ --input_model=./model/model.ckpt --output_model=saved_model
+  ```
+## Benchmark
+  ```shell
+  bash run_benchmark.sh --dataset_location=style_images/,content_images/ --input_model=saved_model.pb --batch_size=1
+  ```
+
+Details of enabling Intel® Neural Compressor on style transfer for Tensorflow.
+=========================
+
+This is a tutorial of how to enable style_transfer model with Intel® Neural Compressor.
+## User Code Analysis
+1. User specifies fp32 *model*, calibration dataset *q_dataloader*, evaluation dataset *eval_dataloader* and metric in tuning.metric field of model-specific yaml config file.
+
+2. User specifies fp32 *model*, calibration dataset *q_dataloader* and a custom *eval_func* which encapsulates the evaluation dataset and metric by itself.
+
+For style_transfer, we applied the latter one because we don't have metric for style transfer model.The first one is to implement the q_dataloader and implement a fake *eval_func*. As neural_compressor have implement a style_transfer dataset, so only eval_func should be prepared after load the graph
+
+### Evaluation Part Adaption
+As style transfer don't have a metric to measure the accuracy, we only implement a fake eval_func
+```python
+def eval_func(model):
+    return 1.
+```
+
+Here we set the input tensor and output tensors name into *inputs* and *outputs* field. In this case we only calibration and quantize the model without tune the accuracy
+
+### Code update
+
+After prepare step is done, we just need add 2 lines to get the quantized model.
+```python
+from neural_compressor import quantization
+from neural_compressor.config import PostTrainingQuantConfig
+conf = PostTrainingQuantConfig(inputs=['style_input', 'content_input'],
+                                outputs=['transformer/expand/conv3/conv/Sigmoid'],
+                                calibration_sampling_size=[50, 100])
+quantized_model = quantization.fit(args.input_graph, conf=conf, calib_dataloader=dataloader,
+            eval_dataloader==dataloader)
+```
+
+The Intel® Neural Compressor quantizer.fit() function will return a best quantized model during timeout constrain.
diff --git a/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/content_images/colva_beach_sq.jpg b/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/content_images/colva_beach_sq.jpg
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HcmV?d00001

diff --git a/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/data_process.py b/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/data_process.py
new file mode 100644
index 00000000000..e71f6577cac
--- /dev/null
+++ b/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/data_process.py
@@ -0,0 +1,489 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2024 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import os
+import glob
+import collections
+
+import numpy as np
+import tensorflow as tf
+
+from abc import abstractmethod
+from neural_compressor.common import logger
+from neural_compressor.tensorflow.utils.data import default_collate
+
+
+class StyleTransferDataset(object):
+    """Dataset used for style transfer task on tensorflow/inteltensorflow/tensorflow_itex backend.
+
+    This Dataset is to construct a dataset from two specific image holders representing
+        content image folder and style image folder.
+    """
+
+    def __init__(
+        self,
+        content_folder,
+        style_folder,
+        crop_ratio=0.1,
+        resize_shape=(256, 256),
+        image_format="jpg",
+        transform=None,
+        filter=None,
+    ):
+        """Initialize `StyleTransferDataset` class.
+
+        Args:
+            content_folder (str): Root directory of content images.
+            style_folder (str): Root directory of style images.
+            crop_ratio (float, default=0.1): Cropped ratio to each side.
+            resize_shape (tuple, default=(256, 256)): Target size of image.
+            image_format (str, default='jpg'): Target image format.
+            transform (transform object, default=None): Transform to process input data.
+            filter (Filter objects, default=None): Filter out examples according to specific conditions.
+        """
+        self.transform = transform
+        self.content_folder = content_folder
+        self.style_folder = style_folder
+        self.resize_shape = resize_shape
+        self.crop_ratio = crop_ratio
+        self.content_images = glob.glob(os.path.join(content_folder, "*" + image_format))
+        self.style_images = glob.glob(os.path.join(style_folder, "*" + image_format))
+        self.image_list = []
+        for content in self.content_images:
+            for style in self.style_images:
+                self.image_list.append((content, style))
+
+    def __len__(self):
+        """Return the length of dataset."""
+        return len(self.image_list)
+
+    def __getitem__(self, index):
+        """Return the item of dataset according to the given index."""
+        from PIL import Image
+
+        content_image, style_image = self.image_list[index]
+        content_image = Image.open(content_image)
+        style_image = Image.open(style_image)
+        width, height = style_image.size
+        crop_ratio = self.crop_ratio
+        crop_box = (crop_ratio * height, crop_ratio * width, (1 - crop_ratio) * height, (1 - crop_ratio) * width)
+        content_image = np.asarray(content_image.resize(self.resize_shape))
+        style_image = np.asarray(style_image.resize(self.resize_shape))
+        if content_image.max() > 1.0:
+            content_image = content_image / 255.0
+        if style_image.max() > 1.0:
+            style_image = style_image / 255.0
+
+        return (content_image, style_image), 0
+
+
+class ComposeTransform(object):
+    """Composes several transforms together.
+
+    Args:
+        transform_list (list of Transform objects):  list of transforms to compose
+
+    Returns:
+        sample (tuple): tuple of processed image and label
+    """
+
+    def __init__(self, transform_list):
+        """Initialize `ComposeTransform` class."""
+        self.transform_list = transform_list
+
+    def __call__(self, sample):
+        """Call transforms in transform_list."""
+        for transform in self.transform_list:
+            sample = transform(sample)
+        return sample
+
+class ParseDecodeVocTransform():
+    """Parse features in Example proto.
+
+    Returns:
+        tuple of parsed image and labels
+    """
+
+    def __call__(self, sample):
+        """Parse decode voc."""
+
+        # Currently only supports jpeg and png.
+        # Need to use this logic because the shape is not known for
+        # tf.image.decode_image and we rely on this info to
+        # extend label if necessary.
+        def _decode_image(content, channels):
+            """Decode the image with content."""
+            return tf.cond(
+                tf.image.is_jpeg(content),
+                lambda: tf.image.decode_jpeg(content, channels),
+                lambda: tf.image.decode_png(content, channels),
+            )
+
+        features = {
+            "image/encoded": tf.compat.v1.FixedLenFeature((), tf.string, default_value=""),
+            "image/filename": tf.compat.v1.FixedLenFeature((), tf.string, default_value=""),
+            "image/format": tf.compat.v1.FixedLenFeature((), tf.string, default_value="jpeg"),
+            "image/height": tf.compat.v1.FixedLenFeature((), tf.int64, default_value=0),
+            "image/width": tf.compat.v1.FixedLenFeature((), tf.int64, default_value=0),
+            "image/segmentation/class/encoded": tf.compat.v1.FixedLenFeature((), tf.string, default_value=""),
+            "image/segmentation/class/format": tf.compat.v1.FixedLenFeature((), tf.string, default_value="png"),
+        }
+
+        parsed_features = tf.compat.v1.parse_single_example(sample, features)
+
+        image = _decode_image(parsed_features["image/encoded"], channels=3)
+
+        label = None
+        label = _decode_image(parsed_features["image/segmentation/class/encoded"], channels=1)
+
+        sample = {
+            "image": image,
+        }
+
+        label.set_shape([None, None, 1])
+
+        sample["labels_class"] = label
+
+        return sample["image"], sample["labels_class"]
+
+
+class BaseMetric(object):
+    """The base class of Metric."""
+
+    def __init__(self, metric, single_output=False, hvd=None):
+        """Initialize the basic metric.
+
+        Args:
+            metric: The metric class.
+            single_output: Whether the output is single or not, defaults to False.
+            hvd: The Horovod class for distributed training, defaults to None.
+        """
+        self._metric_cls = metric
+        self._single_output = single_output
+        self._hvd = hvd
+
+    def __call__(self, *args, **kwargs):
+        """Evaluate the model predictions, and the reference.
+
+        Returns:
+            The class itself.
+        """
+        self._metric = self._metric_cls(*args, **kwargs)
+        return self
+
+    @abstractmethod
+    def update(self, preds, labels=None, sample_weight=None):
+        """Update the state that need to be evaluated.
+
+        Args:
+            preds: The prediction result.
+            labels: The reference. Defaults to None.
+            sample_weight: The sampling weight. Defaults to None.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def reset(self):
+        """Clear the predictions and labels.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def result(self):
+        """Evaluate the difference between predictions and labels.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @property
+    def metric(self):
+        """Return its metric class.
+
+        Returns:
+            The metric class.
+        """
+        return self._metric_cls
+
+    @property
+    def hvd(self):
+        """Return its hvd class.
+
+        Returns:
+            The hvd class.
+        """
+        return self._hvd
+
+    @hvd.setter
+    def hvd(self, hvd):
+        """Set its hvd.
+
+        Args:
+            hvd: The Horovod class for distributed training.
+        """
+        self._hvd = hvd
+
+
+class TopKMetric(BaseMetric):
+    """Compute Top-k Accuracy classification score for Tensorflow model.
+
+    This metric computes the number of times where the correct label is among
+    the top k labels predicted.
+
+    Attributes:
+        k (int): The number of most likely outcomes considered to find the correct label.
+        num_correct: The number of predictions that were correct classified.
+        num_sample: The total number of predictions.
+    """
+
+    def __init__(self, k=1):
+        """Initialize the k, number of samples and correct predictions.
+
+        Args:
+            k: The number of most likely outcomes considered to find the correct label.
+        """
+        self.k = k
+        self.num_correct = 0
+        self.num_sample = 0
+
+    def update(self, preds, labels, sample_weight=None):
+        """Add the predictions and labels.
+
+        Args:
+            preds: The predictions.
+            labels: The labels corresponding to the predictions.
+            sample_weight: The sample weight.
+        """
+        preds, labels = TopKMetric._topk_shape_validate(preds, labels)
+
+        labels = labels.reshape([len(labels)])
+        with tf.Graph().as_default() as acc_graph:
+            topk = tf.nn.in_top_k(
+                predictions=tf.constant(preds, dtype=tf.float32), targets=tf.constant(labels, dtype=tf.int32), k=self.k
+            )
+            fp32_topk = tf.cast(topk, tf.float32)
+            correct_tensor = tf.reduce_sum(input_tensor=fp32_topk)
+
+            with tf.compat.v1.Session() as acc_sess:
+                correct = acc_sess.run(correct_tensor)
+
+        self.num_sample += len(labels)
+        self.num_correct += correct
+
+    def reset(self):
+        """Reset the number of samples and correct predictions."""
+        self.num_correct = 0
+        self.num_sample = 0
+
+    def result(self):
+        """Compute the top-k score.
+
+        Returns:
+            The top-k score.
+        """
+        if self.num_sample == 0:
+            logger.warning("Sample num during evaluation is 0.")
+            return 0
+        elif getattr(self, "_hvd", None) is not None:  # pragma: no cover
+            allgather_num_correct = sum(self._hvd.allgather_object(self.num_correct))
+            allgather_num_sample = sum(self._hvd.allgather_object(self.num_sample))
+            return allgather_num_correct / allgather_num_sample
+        return self.num_correct / self.num_sample
+
+    @staticmethod
+    def _topk_shape_validate(preds, labels):
+        # preds shape can be Nxclass_num or class_num(N=1 by default)
+        # it's more suitable for 'Accuracy' with preds shape Nx1(or 1) output from argmax
+        if isinstance(preds, int):
+            preds = [preds]
+            preds = np.array(preds)
+        elif isinstance(preds, np.ndarray):
+            preds = np.array(preds)
+        elif isinstance(preds, list):
+            preds = np.array(preds)
+            preds = preds.reshape((-1, preds.shape[-1]))
+
+        # consider labels just int value 1x1
+        if isinstance(labels, int):
+            labels = [labels]
+            labels = np.array(labels)
+        elif isinstance(labels, tuple):
+            labels = np.array([labels])
+            labels = labels.reshape((labels.shape[-1], -1))
+        elif isinstance(labels, list):
+            if isinstance(labels[0], int):
+                labels = np.array(labels)
+                labels = labels.reshape((labels.shape[0], 1))
+            elif isinstance(labels[0], tuple):
+                labels = np.array(labels)
+                labels = labels.reshape((labels.shape[-1], -1))
+            else:
+                labels = np.array(labels)
+        # labels most have 2 axis, 2 cases: N(or Nx1 sparse) or Nxclass_num(one-hot)
+        # only support 2 dimension one-shot labels
+        # or 1 dimension one-hot class_num will confuse with N
+
+        if len(preds.shape) == 1:
+            N = 1
+            class_num = preds.shape[0]
+            preds = preds.reshape([-1, class_num])
+        elif len(preds.shape) >= 2:
+            N = preds.shape[0]
+            preds = preds.reshape([N, -1])
+            class_num = preds.shape[1]
+
+        label_N = labels.shape[0]
+        assert label_N == N, "labels batch size should same with preds"
+        labels = labels.reshape([N, -1])
+        # one-hot labels will have 2 dimension not equal 1
+        if labels.shape[1] != 1:
+            labels = labels.argsort()[..., -1:]
+        return preds, labels
+    
+
+class TFDataLoader(object):  # pragma: no cover
+    """Tensorflow dataloader class.
+
+    In tensorflow1.x dataloader is coupled with the graph, but it also support feed_dict
+    method to do session run, this dataloader is designed to satisfy the usage of feed dict
+    in tf1.x. Although it's a general dataloader and can be used in MXNet and PyTorch.
+
+    Args:
+        dataset: obj. wrapper of needed data.
+        batch_size: int. batch size
+    """
+
+    def __init__(self, dataset, batch_size=1, last_batch="rollover"):
+        """Initialize `TFDataDataLoader` class."""
+        self.dataset = dataset
+        self.last_batch = last_batch
+        self.batch_size = batch_size
+        dataset = dataset.batch(batch_size)
+
+    def batch(self, batch_size, last_batch="rollover"):
+        """Dataset return data per batch."""
+        drop_last = False if last_batch == "rollover" else True
+        self.batch_size = batch_size
+        self.dataset = self.dataset.batch(batch_size, drop_last)
+
+    def __iter__(self):
+        """Iterate dataloader."""
+        return self._generate_dataloader(
+            self.dataset,
+            batch_size=self.batch_size,
+            last_batch=self.last_batch,
+        )
+
+    def _generate_dataloader(
+        self,
+        dataset,
+        batch_size=1,
+        last_batch="rollover",
+        collate_fn=None,
+        sampler=None,
+        batch_sampler=None,
+        num_workers=None,
+        pin_memory=None,
+        distributed=False,
+    ):
+        """Yield data."""
+        drop_last = False if last_batch == "rollover" else True
+
+        def check_dynamic_shape(element_spec):
+            if isinstance(element_spec, collections.abc.Sequence):
+                return any([check_dynamic_shape(ele) for ele in element_spec])
+            elif isinstance(element_spec, tf.TensorSpec):
+                return True if element_spec.shape.num_elements() is None else False
+            else:
+                raise ValueError("unrecognized element spec...")
+
+        def squeeze_output(output):
+            if isinstance(output, collections.abc.Sequence):
+                return [squeeze_output(ele) for ele in output]
+            elif isinstance(output, np.ndarray):
+                return np.squeeze(output, axis=0)
+            else:
+                raise ValueError("not supported output format....")
+
+        if tf.executing_eagerly():
+            index = 0
+            outputs = []
+            for iter_tensors in dataset:
+                samples = []
+                iter_inputs, iter_labels = iter_tensors[0], iter_tensors[1]
+                if isinstance(iter_inputs, tf.Tensor):
+                    samples.append(iter_inputs.numpy())
+                else:
+                    samples.append(tuple(iter_input.numpy() for iter_input in iter_inputs))
+                if isinstance(iter_labels, tf.Tensor):
+                    samples.append(iter_labels.numpy())
+                else:
+                    samples.append([np.array(l) for l in iter_labels])
+                index += 1
+                outputs.append(samples)
+                if index == batch_size:
+                    outputs = default_collate(outputs)
+                    yield outputs
+                    outputs = []
+                    index = 0
+            if len(outputs) > 0:
+                outputs = default_collate(outputs)
+                yield outputs
+        else:
+            try_single_batch = check_dynamic_shape(dataset.element_spec)
+            dataset = dataset.batch(1 if try_single_batch else batch_size, drop_last)
+            ds_iterator = tf.compat.v1.data.make_one_shot_iterator(dataset)
+            iter_tensors = ds_iterator.get_next()
+            data_config = tf.compat.v1.ConfigProto()
+            data_config.use_per_session_threads = 1
+            data_config.intra_op_parallelism_threads = 1
+            data_config.inter_op_parallelism_threads = 16
+            data_sess = tf.compat.v1.Session(config=data_config)
+            # pylint: disable=no-name-in-module
+            from tensorflow.python.framework.errors_impl import OutOfRangeError
+
+            while True:
+                if not try_single_batch:
+                    try:
+                        outputs = data_sess.run(iter_tensors)
+                        yield outputs
+                    except OutOfRangeError:
+                        data_sess.close()
+                        return
+                else:
+                    try:
+                        outputs = []
+                        for i in range(0, batch_size):
+                            outputs.append(squeeze_output(data_sess.run(iter_tensors)))
+                        outputs = default_collate(outputs)
+                        yield outputs
+                    except OutOfRangeError:
+                        if len(outputs) == 0:
+                            data_sess.close()
+                            return
+                        else:
+                            outputs = default_collate(outputs)
+                            yield outputs
+                            data_sess.close()
+                            return
diff --git a/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/main.py b/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/main.py
new file mode 100644
index 00000000000..577e9094209
--- /dev/null
+++ b/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/main.py
@@ -0,0 +1,208 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2023 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+#
+
+import os
+import io
+import skimage.io
+import glob
+import numpy as np
+import tensorflow.compat.v1 as tf
+from PIL import Image
+import time
+
+from data_process import (
+    TFDataLoader,
+    StyleTransferDataset,
+    ComposeTransform,
+    ParseDecodeVocTransform,
+)
+
+flags = tf.flags
+flags.DEFINE_string('style_images_paths', None, 'Paths to the style images'
+                    'for evaluation.')
+flags.DEFINE_string('content_images_paths', None, 'Paths to the content images'
+                    'for evaluation.')
+flags.DEFINE_string('output_dir', './result', 'Output stylized image directory.')
+
+flags.DEFINE_string('output_model', None, 'Output model directory.')
+
+flags.DEFINE_string('input_model', None, 'Output directory.')
+
+flags.DEFINE_integer('batch_size', 1, 'batch_size')
+
+flags.DEFINE_bool('tune', False, 'if use tune')
+
+FLAGS = flags.FLAGS
+
+def load_img(path, resize_shape=(256, 256), crop_ratio=0.1):
+    img = Image.open(path)
+    width, height = img.size
+    crop_box = (crop_ratio*height, crop_ratio*width, (1-crop_ratio)*height, (1-crop_ratio)*width)
+    img = np.asarray(img.crop(crop_box).resize(resize_shape))
+    if img.max() > 1.0:
+        img = img / 255.
+    img = img.astype(np.float32)[np.newaxis, ...]
+    return img
+
+def save_image(image, output_file, save_format='jpeg'):
+    image = np.uint8(image * 255.0)
+    buf = io.BytesIO()
+    skimage.io.imsave(buf, np.squeeze(image, 0), format=save_format)
+    buf.seek(0)
+    f = tf.gfile.GFile(output_file, 'w')
+    f.write(buf.getvalue())
+    f.close()
+
+def image_style_transfer(sess, content_img_path, style_img_path):
+    stylized_images = sess.graph.get_tensor_by_name('import/import/transformer/expand/conv3/conv/Sigmoid:0')
+    style_img_np = load_img(style_img_path, crop_ratio=0)
+    content_img_np = load_img(content_img_path, crop_ratio=0)
+    stylized_image_res = sess.run(
+        stylized_images,
+        feed_dict={
+            'import/import/style_input:0': style_img_np,
+            'import/import/content_input:0': content_img_np})
+    # saves stylized image.
+    save_image(stylized_image_res, os.path.join(FLAGS.output_dir, 'stylized_image.jpg'))
+
+def main(args=None):
+    tf.logging.set_verbosity(tf.logging.INFO)
+    if not tf.gfile.Exists(FLAGS.output_dir):
+        tf.gfile.MkDir(FLAGS.output_dir)
+
+    with tf.Session() as sess:
+        if FLAGS.input_model.rsplit('.', 1)[-1] == 'ckpt':
+            style_img_ph = tf.placeholder(tf.float32, shape=[None, 256, 256, 3], name='style_input')
+            content_img_ph = tf.placeholder(tf.float32, shape=[None, 256, 256, 3], name='content_input')
+            # import meta_graph
+            meta_data_path = FLAGS.input_model + '.meta'
+            saver = tf.train.import_meta_graph(meta_data_path, clear_devices=True)
+
+            sess.run(tf.global_variables_initializer())
+            saver.restore(sess, FLAGS.input_model)
+            graph_def = sess.graph.as_graph_def()
+
+            replace_style = 'style_image_processing/ResizeBilinear_2'
+            replace_content = 'batch_processing/batch'
+            for node in graph_def.node:
+                for idx, input_name in enumerate(node.input):
+                    # replace style input and content input nodes to  placeholder
+                    if replace_content == input_name:
+                        node.input[idx] = 'content_input'
+                    if replace_style == input_name:
+                        node.input[idx] = 'style_input'
+
+            if FLAGS.tune:
+                from neural_compressor.tensorflow.quantization.utils.utility import _parse_ckpt_bn_input
+                _parse_ckpt_bn_input(graph_def)
+            output_name = 'transformer/expand/conv3/conv/Sigmoid'
+            frozen_graph = tf.graph_util.convert_variables_to_constants(sess, graph_def, [output_name])
+        # use frozen pb instead
+        elif FLAGS.input_model.rsplit('.', 1)[-1] == 'pb':
+            with open(FLAGS.input_model, 'rb') as f:
+                frozen_graph = tf.GraphDef()
+                frozen_graph.ParseFromString(f.read())
+        else:
+            print("not supported model format")
+            exit(-1)
+
+        if FLAGS.tune:
+            with tf.Graph().as_default() as graph:
+                tf.import_graph_def(frozen_graph, name='')
+                from neural_compressor.common import set_random_seed
+                from neural_compressor.tensorflow import StaticQuantConfig, quantize_model
+
+                set_random_seed(9527)
+                dataset = StyleTransferDataset(
+                    content_folder=FLAGS.content_images_paths.strip(),
+                    style_folder=FLAGS.style_images_paths.strip(),
+                    transform=ComposeTransform(transform_list= [
+                        ParseDecodeVocTransform(),
+                        ]
+                    )
+                )
+                calib_dataloader = TFDataLoader(dataset=dataset, batch_size=FLAGS.batch_size)
+                
+                quant_config = StaticQuantConfig()
+                q_model = quantize_model(graph, quant_config, calib_dataloader)
+                q_model.save(FLAGS.output_model)
+                frozen_graph= q_model.graph_def
+
+    # validate the quantized model here
+    with tf.Graph().as_default(), tf.Session() as sess:
+        if FLAGS.tune:
+            # create dataloader using default style_transfer dataset
+            # generate stylized images
+            dataset = StyleTransferDataset(
+                    content_folder=FLAGS.content_images_paths.strip(),
+                    style_folder=FLAGS.style_images_paths.strip(),
+                    crop_ratio=0.2,
+                    resize_shape=(256, 256)
+                )
+        else: 
+            from neural_compressor.tensorflow.utils import DummyDatasetV2
+            dataset = DummyDatasetV2(input_shape=[(256, 256, 3), (256, 256, 3)], label_shape=(1, ))
+
+        dataloader = TFDataLoader(dataset=dataset, batch_size=FLAGS.batch_size)
+        tf.import_graph_def(frozen_graph, name='')
+        style_transfer(sess, dataloader)
+
+def add_import_to_name(sess, name, try_cnt=2):
+    for i in range(0, try_cnt):
+        try:
+            sess.graph.get_tensor_by_name(name)   
+            return name
+        except:
+            name = 'import/' + name
+
+    raise ValueError('can not find tensor by name')
+
+# validate and save the files
+def style_transfer(sess, dataloader):
+      time_list = []
+      output_name = add_import_to_name(sess, 'transformer/expand/conv3/conv/Sigmoid:0', 3)
+      style_name = add_import_to_name(sess, 'style_input:0', 3)
+      content_name = add_import_to_name(sess, 'content_input:0', 3)
+
+      stylized_images = sess.graph.get_tensor_by_name(output_name)
+      
+      for idx, ((content_img_np, style_img_np), _) in enumerate(dataloader):
+          start_time = time.time()
+          stylized_image_res = sess.run(
+              stylized_images,
+              feed_dict={
+                  style_name: style_img_np,
+                  content_name: content_img_np})
+          duration = time.time() - start_time
+          time_list.append(duration)
+          if idx + 1 == 20:
+              break
+      warm_up = 1
+      throughput = (len(time_list) - warm_up)/ np.array(time_list[warm_up:]).sum()
+      print('Batch size = {}'.format(FLAGS.batch_size)) 
+      print('Latency: {:.3f} ms'.format(np.array(time_list[warm_up:]).mean() * 1000)) 
+      print('Throughput: {:.3f} images/sec'.format(throughput)) 
+
+
+def run_tuning():
+    tf.disable_v2_behavior()
+    tf.app.run(main)
+
+if __name__ == '__main__':
+    run_tuning()
diff --git a/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/prepare_model.py b/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/prepare_model.py
new file mode 100644
index 00000000000..74182ad5f37
--- /dev/null
+++ b/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/prepare_model.py
@@ -0,0 +1,33 @@
+import os
+import argparse
+import enum
+import tarfile
+import abc
+
+def get_pretrained_model(destination):
+    """
+    Obtains a ready to use style_transfer model file.
+    Args:
+        destination: path to where the file should be stored
+    """
+    url = "https://storage.googleapis.com/download.magenta.tensorflow.org/models/ \
+           arbitrary_style_transfer.tar.gz"
+
+    os.system("curl -o arbitrary_style_transfer.tar.gz {0}".format(url))
+    with tarfile.open("arbitrary_style_transfer.tar.gz") as tar:
+        if not os.path.exists(destination):
+            os.makedirs(destination)
+        tar.extractall(destination)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Prepare pre-trained model for style transfer model')
+    parser.add_argument('--model_path', type=str, default='./model', help='directory to put models, default is ./model')
+
+    args = parser.parse_args()
+    model_path = args.model_path
+    try:
+        get_pretrained_model(model_path)
+    except AttributeError:
+        print("The model fetched failed.")
+
diff --git a/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/requirements.txt b/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/requirements.txt
new file mode 100644
index 00000000000..1e5d462dcd4
--- /dev/null
+++ b/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/requirements.txt
@@ -0,0 +1,2 @@
+scikit-image
+Pillow>=8.2.0
diff --git a/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/run_benchmark.sh b/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/run_benchmark.sh
new file mode 100644
index 00000000000..41fee820958
--- /dev/null
+++ b/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/run_benchmark.sh
@@ -0,0 +1,61 @@
+#!/bin/bash
+set -x
+
+function main {
+
+  init_params "$@"
+  run_benchmark
+
+}
+
+# init params
+function init_params {
+  iters=100
+  for var in "$@"
+  do
+    case $var in
+      --topology=*)
+          topology=$(echo $var |cut -f2 -d=)
+      ;;
+      --dataset_location=*)
+          dataset_location=$(echo $var |cut -f2 -d=)
+      ;;
+      --input_model=*)
+          input_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --mode=*)
+          mode=$(echo $var |cut -f2 -d=)
+      ;;
+      --batch_size=*)
+          batch_size=$(echo $var |cut -f2 -d=)
+      ;;
+      --iters=*)
+          iters=$(echo ${var} |cut -f2 -d=)
+      ;;
+      *)
+          echo "Error: No such parameter: ${var}"
+          exit 1
+      ;;
+    esac
+  done
+
+}
+
+
+# run_tuning
+function run_benchmark {
+    style_images=$(echo ${dataset_location} | awk -F ',' '{print $1}')
+    content_images=$(echo ${dataset_location} | awk -F ',' '{print $2}')
+    echo "$style_images, $content_images"
+
+    python main.py \
+            --input_model "${input_model}" \
+            --style_images_paths "${style_images}" \
+            --content_images_paths "${content_images}" \
+            --batch_size "${batch_size}" \
+            --tune=False \
+            --output_model "${output_model}"
+
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/run_quant.sh b/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/run_quant.sh
new file mode 100644
index 00000000000..4fdfdd2e8a5
--- /dev/null
+++ b/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/run_quant.sh
@@ -0,0 +1,50 @@
+#!/bin/bash
+# set -x
+
+function main {
+
+  init_params "$@"
+
+  run_tuning
+
+}
+
+# init params
+function init_params {
+
+  for var in "$@"
+  do
+    case $var in
+      --topology=*)
+          topology=$(echo $var |cut -f2 -d=)
+      ;;
+      --dataset_location=*)
+          dataset_location=$(echo "$var" |cut -f2 -d=)
+      ;;
+      --input_model=*)
+          input_model=$(echo "$var" |cut -f2 -d=)
+      ;;
+      --output_model=*)
+          output_model=$(echo "$var" |cut -f2 -d=)
+      ;;
+    esac
+  done
+
+}
+
+# run_tuning
+function run_tuning {
+    style_images=$(echo ${dataset_location} | awk -F ',' '{print $1}')
+    content_images=$(echo ${dataset_location} | awk -F ',' '{print $2}')
+    echo "$style_images, $content_images"
+
+    python main.py \
+            --input_model "${input_model}" \
+            --style_images_paths "${style_images}" \
+            --content_images_paths "${content_images}" \
+            --config "./conf.yaml" \
+            --tune=True \
+            --output_model "${output_model}"
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/style_images/kanagawa_great_wave.jpg b/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/style_images/kanagawa_great_wave.jpg
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HcmV?d00001


From 00ef38723ec8e83b5f06a0dab73d78fd3f13c620 Mon Sep 17 00:00:00 2001
From: zehao-intel <zehao.huang@intel.com>
Date: Tue, 9 Jul 2024 13:51:32 +0800
Subject: [PATCH 02/14] add keras models

Signed-off-by: zehao-intel <zehao.huang@intel.com>
---
 examples/.config/model_params_keras_3x.json   |    25 +
 .../.config/model_params_tensorflow_3x.json   |     7 +
 .../cv/mobilenet_v2/quantization/ptq/main.py  |     1 -
 .../imagenet_prepare/build_imagenet_data.py   |   567 +
 .../download_and_convert_imagenet.sh          |   100 +
 .../cv/imagenet_prepare/download_imagenet.sh  |    99 +
 .../imagenet_lsvrc_2015_synsets.txt           |  1000 +
 .../cv/imagenet_prepare/imagenet_metadata.txt | 21842 ++++++++++++++++
 .../inception_v3/quantization/ptq/README.md   |    65 +
 .../quantization/ptq/data_process.py          |   543 +
 .../cv/inception_v3/quantization/ptq/main.py  |   143 +
 .../quantization/ptq/prepare_model.py         |    35 +
 .../quantization/ptq/requirements.txt         |     2 +
 .../quantization/ptq/run_benchmark.sh         |    51 +
 .../quantization/ptq/run_quant.sh             |    40 +
 .../mobilenet_v2/quantization/ptq/README.md   |    75 +
 .../quantization/ptq/data_process.py          |   543 +
 .../cv/mobilenet_v2/quantization/ptq/main.py  |   141 +
 .../quantization/ptq/prepare_model.py         |    35 +
 .../quantization/ptq/requirements.txt         |     2 +
 .../quantization/ptq/run_benchmark.sh         |    51 +
 .../quantization/ptq/run_quant.sh             |    40 +
 .../tensorflow/keras/cv/prepare_dataset.sh    |    71 +
 .../resnet_v2_50/quantization/ptq/README.md   |    76 +
 .../quantization/ptq/data_process.py          |   543 +
 .../cv/resnet_v2_50/quantization/ptq/main.py  |   141 +
 .../quantization/ptq/prepare_model.py         |    35 +
 .../quantization/ptq/requirements.txt         |     2 +
 .../quantization/ptq/run_benchmark.sh         |    50 +
 .../quantization/ptq/run_quant.sh             |    40 +
 30 files changed, 26364 insertions(+), 1 deletion(-)
 create mode 100644 examples/.config/model_params_keras_3x.json
 create mode 100644 examples/3.x_api/tensorflow/keras/cv/imagenet_prepare/build_imagenet_data.py
 create mode 100644 examples/3.x_api/tensorflow/keras/cv/imagenet_prepare/download_and_convert_imagenet.sh
 create mode 100644 examples/3.x_api/tensorflow/keras/cv/imagenet_prepare/download_imagenet.sh
 create mode 100644 examples/3.x_api/tensorflow/keras/cv/imagenet_prepare/imagenet_lsvrc_2015_synsets.txt
 create mode 100644 examples/3.x_api/tensorflow/keras/cv/imagenet_prepare/imagenet_metadata.txt
 create mode 100644 examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/README.md
 create mode 100644 examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/data_process.py
 create mode 100644 examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/main.py
 create mode 100644 examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/prepare_model.py
 create mode 100644 examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/requirements.txt
 create mode 100644 examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/run_benchmark.sh
 create mode 100644 examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/run_quant.sh
 create mode 100644 examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/README.md
 create mode 100644 examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/data_process.py
 create mode 100644 examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/main.py
 create mode 100644 examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/prepare_model.py
 create mode 100644 examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/requirements.txt
 create mode 100644 examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/run_benchmark.sh
 create mode 100644 examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/run_quant.sh
 create mode 100644 examples/3.x_api/tensorflow/keras/cv/prepare_dataset.sh
 create mode 100644 examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/README.md
 create mode 100644 examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/data_process.py
 create mode 100644 examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/main.py
 create mode 100644 examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/prepare_model.py
 create mode 100644 examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/requirements.txt
 create mode 100644 examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/run_benchmark.sh
 create mode 100644 examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/run_quant.sh

diff --git a/examples/.config/model_params_keras_3x.json b/examples/.config/model_params_keras_3x.json
new file mode 100644
index 00000000000..65b0466667e
--- /dev/null
+++ b/examples/.config/model_params_keras_3x.json
@@ -0,0 +1,25 @@
+{
+  "keras": {
+    "resnetv2_50": {
+      "model_src_dir": "keras/cv/resnet_v2_50/quantization/ptq",
+      "dataset_location": "/tf_dataset/dataset/imagenet",
+      "input_model": "/tf_dataset2/models/tensorflow/resnetv2_50_keras/saved_model",
+      "main_script": "main.py",
+      "batch_size": 32
+    },
+    "inception_v3": {
+      "model_src_dir": "keras/cv/inception_v3/quantization/ptq",
+      "dataset_location": "/tf_dataset/dataset/imagenet",
+      "input_model": "/tf_dataset2/models/tensorflow/inception_v3_keras/saved_model",
+      "main_script": "main.py",
+      "batch_size": 32
+    },
+    "mobilenet_v2": {
+      "model_src_dir": "keras/cv/mobilenet_v2/quantization/ptq",
+      "dataset_location": "/tf_dataset/dataset/imagenet",
+      "input_model": "/tf_dataset2/models/tensorflow/mobilenet_v2_keras/saved_model",
+      "main_script": "main.py",
+      "batch_size": 32
+    }
+  }
+}
diff --git a/examples/.config/model_params_tensorflow_3x.json b/examples/.config/model_params_tensorflow_3x.json
index f81b8abfeb3..69b5bb11059 100644
--- a/examples/.config/model_params_tensorflow_3x.json
+++ b/examples/.config/model_params_tensorflow_3x.json
@@ -72,6 +72,13 @@
       "main_script": "main.py",
       "batch_size": 32
     },
+    "resnetv2_50": {
+      "model_src_dir": "cv/resnet_v2_50/quantization/ptq",
+      "dataset_location": "/tf_dataset/dataset/imagenet",
+      "input_model": "/tf_dataset/pre-train-model-slim/pbfile/frozen_pb/frozen_resnet_v2_50.pb",
+      "main_script": "main.py",
+      "batch_size": 32
+    },
     "vgg16": {
       "model_src_dir": "cv/vgg16/quantization/ptq",
       "dataset_location": "/tf_dataset/dataset/imagenet",
diff --git a/examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/main.py b/examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/main.py
index 024225431f4..d22a9bd4494 100644
--- a/examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/main.py
+++ b/examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/main.py
@@ -117,7 +117,6 @@ def run(self):
             q_model.save(args.output_graph)
 
         if args.benchmark:
-            dataloader = create_dataloader('tensorflow', dataloader_args)
             dataset = ImageRecordDataset(
                 root=args.dataset_location, 
                 transform=ComposeTransform(transform_list= [
diff --git a/examples/3.x_api/tensorflow/keras/cv/imagenet_prepare/build_imagenet_data.py b/examples/3.x_api/tensorflow/keras/cv/imagenet_prepare/build_imagenet_data.py
new file mode 100644
index 00000000000..c52d2bd4218
--- /dev/null
+++ b/examples/3.x_api/tensorflow/keras/cv/imagenet_prepare/build_imagenet_data.py
@@ -0,0 +1,567 @@
+# Copyright 2016 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Converts ImageNet data to TFRecords file format with Example protos.
+
+The raw ImageNet data set is expected to reside in JPEG files located in the
+following directory structure.
+
+  data_dir/n01440764/ILSVRC2012_val_00000293.JPEG
+  data_dir/n01440764/ILSVRC2012_val_00000543.JPEG
+  ...
+
+where 'n01440764' is the unique synset label associated with
+these images.
+
+The training data set consists of 1000 sub-directories (i.e. labels)
+each containing 1200 JPEG images for a total of 1.2M JPEG images.
+
+The evaluation data set consists of 1000 sub-directories (i.e. labels)
+each containing 50 JPEG images for a total of 50K JPEG images.
+
+This TensorFlow script converts the training and evaluation data into
+a sharded data set consisting of 1024 and 128 TFRecord files, respectively.
+
+  train_directory/train-00000-of-01024
+  train_directory/train-00001-of-01024
+  ...
+  train_directory/train-00127-of-01024
+
+and
+
+  validation_directory/validation-00000-of-00128
+  validation_directory/validation-00001-of-00128
+  ...
+  validation_directory/validation-00127-of-00128
+
+Each validation TFRecord file contains ~390 records. Each training TFREcord
+file contains ~1250 records. Each record within the TFRecord file is a
+serialized Example proto. The Example proto contains the following fields:
+
+  image/encoded: string containing JPEG encoded image in RGB colorspace
+  image/height: integer, image height in pixels
+  image/width: integer, image width in pixels
+  image/colorspace: string, specifying the colorspace, always 'RGB'
+  image/channels: integer, specifying the number of channels, always 3
+  image/format: string, specifying the format, always'JPEG'
+
+  image/filename: string containing the basename of the image file
+            e.g. 'n01440764_10026.JPEG' or 'ILSVRC2012_val_00000293.JPEG'
+  image/class/label: integer specifying the index in a classification layer.
+    The label ranges from [1, 1000] where 0 is not used.
+  image/class/synset: string specifying the unique ID of the label,
+    e.g. 'n01440764'
+  image/class/text: string specifying the human-readable version of the label
+    e.g. 'red fox, Vulpes vulpes'
+
+Note that the length of xmin is identical to the length of xmax, ymin and ymax
+for each example.
+
+Running this script using 16 threads may take around ~2.5 hours on a HP Z420.
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from datetime import datetime
+import os
+import random
+import sys
+import threading
+
+import numpy as np
+from six.moves import xrange  # pylint: disable=redefined-builtin
+import tensorflow as tf
+tf.compat.v1.disable_eager_execution()
+
+
+tf.compat.v1.app.flags.DEFINE_string('raw_directory', None,
+                           'Raw data directory')
+
+tf.compat.v1.app.flags.DEFINE_string('output_directory', None,
+                           'Output data directory')
+
+tf.compat.v1.app.flags.DEFINE_integer('shards', 1,
+                            'Number of shards in TFRecord files.')
+
+tf.compat.v1.app.flags.DEFINE_string('subset', 'validation',
+                           'Subset of imagenet, can be validation/train')
+
+tf.compat.v1.app.flags.DEFINE_integer('num_threads', 1,
+                            'Number of threads to preprocess the images.')
+
+# The labels file contains a list of valid labels are held in this file.
+# Assumes that the file contains entries as such:
+#   n01440764
+#   n01443537
+#   n01484850
+# where each line corresponds to a label expressed as a synset. We map
+# each synset contained in the file to an integer (based on the alphabetical
+# ordering). See below for details.
+tf.compat.v1.app.flags.DEFINE_string('labels_file',
+                           'imagenet_lsvrc_2015_synsets.txt',
+                           'Labels file')
+
+# This file containing mapping from synset to human-readable label.
+# Assumes each line of the file looks like:
+#
+#   n02119247    black fox
+#   n02119359    silver fox
+#   n02119477    red fox, Vulpes fulva
+#
+# where each line corresponds to a unique mapping. Note that each line is
+# formatted as <synset>\t<human readable label>.
+tf.compat.v1.app.flags.DEFINE_string('imagenet_metadata_file',
+                           'imagenet_metadata.txt',
+                           'ImageNet metadata file')
+
+FLAGS = tf.compat.v1.app.flags.FLAGS
+
+
+def _int64_feature(value):
+  """Wrapper for inserting int64 features into Example proto."""
+  if not isinstance(value, list):
+    value = [value]
+  return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
+
+
+def _float_feature(value):
+  """Wrapper for inserting float features into Example proto."""
+  if not isinstance(value, list):
+    value = [value]
+  return tf.train.Feature(float_list=tf.train.FloatList(value=value))
+
+
+def _bytes_feature(value):
+  """Wrapper for inserting bytes features into Example proto."""
+  return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
+
+
+def _convert_to_example(filename, image_buffer, label, synset, human,
+                        height, width):
+  """Build an Example proto for an example.
+
+  Args:
+    filename: string, path to an image file, e.g., '/path/to/example.JPG'
+    image_buffer: string, JPEG encoding of RGB image
+    label: integer, identifier for the ground truth for the network
+    synset: string, unique WordNet ID specifying the label, e.g., 'n02323233'
+    human: string, human-readable label, e.g., 'red fox, Vulpes vulpes'
+    height: integer, image height in pixels
+    width: integer, image width in pixels
+  Returns:
+    Example proto
+  """
+
+  colorspace = b'RGB'
+  channels = 3
+  image_format = b'JPEG'
+
+  example = tf.train.Example(features=tf.train.Features(feature={
+      'image/height': _int64_feature(height),
+      'image/width': _int64_feature(width),
+      'image/colorspace': _bytes_feature(colorspace),
+      'image/channels': _int64_feature(channels),
+      'image/class/label': _int64_feature(label),
+      'image/class/synset': _bytes_feature(bytes(synset,'utf-8')),
+      'image/class/text': _bytes_feature(bytes(human,'utf-8')),
+      'image/format': _bytes_feature(image_format),
+      'image/filename': _bytes_feature(bytes(os.path.basename(filename),'utf-8')),
+      'image/encoded': _bytes_feature(image_buffer)}))
+  return example
+
+
+class ImageCoder(object):
+  """Helper class that provides TensorFlow image coding utilities."""
+
+  def __init__(self):
+    # Create a single Session to run all image coding calls.
+    self._sess = tf.compat.v1.Session()
+
+    # Initializes function that converts PNG to JPEG data.
+    self._png_data = tf.compat.v1.placeholder(dtype=tf.string)
+    image = tf.image.decode_png(self._png_data, channels=3)
+    self._png_to_jpeg = tf.image.encode_jpeg(image, format='rgb', quality=100)
+
+    # Initializes function that converts CMYK JPEG data to RGB JPEG data.
+    self._cmyk_data = tf.compat.v1.placeholder(dtype=tf.string)
+    image = tf.image.decode_jpeg(self._cmyk_data, channels=0)
+    self._cmyk_to_rgb = tf.image.encode_jpeg(image, format='rgb', quality=100)
+
+    # Initializes function that decodes RGB JPEG data.
+    self._decode_jpeg_data = tf.compat.v1.placeholder(dtype=tf.string)
+    self._decode_jpeg = tf.image.decode_jpeg(self._decode_jpeg_data, channels=3)
+
+  def png_to_jpeg(self, image_data):
+    return self._sess.run(self._png_to_jpeg,
+                          feed_dict={self._png_data: image_data})
+
+  def cmyk_to_rgb(self, image_data):
+    return self._sess.run(self._cmyk_to_rgb,
+                          feed_dict={self._cmyk_data: image_data})
+
+  def decode_jpeg(self, image_data):
+    image = self._sess.run(self._decode_jpeg,
+                           feed_dict={self._decode_jpeg_data: image_data})
+    assert len(image.shape) == 3
+    assert image.shape[2] == 3
+    return image
+
+
+def _is_png(filename):
+  """Determine if a file contains a PNG format image.
+
+  Args:
+    filename: string, path of the image file.
+
+  Returns:
+    boolean indicating if the image is a PNG.
+  """
+  # File list from:
+  # https://groups.google.com/forum/embed/?place=forum/torch7#!topic/torch7/fOSTXHIESSU
+  return 'n02105855_2933.JPEG' in filename
+
+
+def _is_cmyk(filename):
+  """Determine if file contains a CMYK JPEG format image.
+
+  Args:
+    filename: string, path of the image file.
+
+  Returns:
+    boolean indicating if the image is a JPEG encoded with CMYK color space.
+  """
+  # File list from:
+  # https://github.com/cytsai/ilsvrc-cmyk-image-list
+  blacklist = ['n01739381_1309.JPEG', 'n02077923_14822.JPEG',
+               'n02447366_23489.JPEG', 'n02492035_15739.JPEG',
+               'n02747177_10752.JPEG', 'n03018349_4028.JPEG',
+               'n03062245_4620.JPEG', 'n03347037_9675.JPEG',
+               'n03467068_12171.JPEG', 'n03529860_11437.JPEG',
+               'n03544143_17228.JPEG', 'n03633091_5218.JPEG',
+               'n03710637_5125.JPEG', 'n03961711_5286.JPEG',
+               'n04033995_2932.JPEG', 'n04258138_17003.JPEG',
+               'n04264628_27969.JPEG', 'n04336792_7448.JPEG',
+               'n04371774_5854.JPEG', 'n04596742_4225.JPEG',
+               'n07583066_647.JPEG', 'n13037406_4650.JPEG']
+  return filename.split('/')[-1] in blacklist
+
+
+def _process_image(filename, coder):
+  """Process a single image file.
+
+  Args:
+    filename: string, path to an image file e.g., '/path/to/example.JPG'.
+    coder: instance of ImageCoder to provide TensorFlow image coding utils.
+  Returns:
+    image_buffer: string, JPEG encoding of RGB image.
+    height: integer, image height in pixels.
+    width: integer, image width in pixels.
+  """
+  # Read the image file.
+  image_data = tf.io.gfile.GFile(filename, 'rb').read()
+
+  # Clean the dirty data.
+  if _is_png(filename):
+    # 1 image is a PNG.
+    print('Converting PNG to JPEG for %s' % filename)
+    image_data = coder.png_to_jpeg(image_data)
+  elif _is_cmyk(filename):
+    # 22 JPEG images are in CMYK colorspace.
+    print('Converting CMYK to RGB for %s' % filename)
+    image_data = coder.cmyk_to_rgb(image_data)
+
+  # Decode the RGB JPEG.
+  image = coder.decode_jpeg(image_data)
+
+  # Check that image converted to RGB
+  assert len(image.shape) == 3
+  height = image.shape[0]
+  width = image.shape[1]
+  assert image.shape[2] == 3
+
+  return image_data, height, width
+
+
+def _process_image_files_batch(coder, thread_index, ranges, name, filenames,
+                               synsets, labels, humans, num_shards):
+  """Processes and saves list of images as TFRecord in 1 thread.
+
+  Args:
+    coder: instance of ImageCoder to provide TensorFlow image coding utils.
+    thread_index: integer, unique batch to run index is within [0, len(ranges)).
+    ranges: list of pairs of integers specifying ranges of each batches to
+      analyze in parallel.
+    name: string, unique identifier specifying the data set
+    filenames: list of strings; each string is a path to an image file
+    synsets: list of strings; each string is a unique WordNet ID
+    labels: list of integer; each integer identifies the ground truth
+    humans: list of strings; each string is a human-readable label
+    num_shards: integer number of shards for this data set.
+  """
+  # Each thread produces N shards where N = int(num_shards / num_threads).
+  # For instance, if num_shards = 128, and the num_threads = 2, then the first
+  # thread would produce shards [0, 64).
+  num_threads = len(ranges)
+  assert not num_shards % num_threads
+  num_shards_per_batch = int(num_shards / num_threads)
+
+  shard_ranges = np.linspace(ranges[thread_index][0],
+                             ranges[thread_index][1],
+                             num_shards_per_batch + 1).astype(int)
+  num_files_in_thread = ranges[thread_index][1] - ranges[thread_index][0]
+
+  counter = 0
+  for s in xrange(num_shards_per_batch):
+    # Generate a sharded version of the file name, e.g. 'train-00002-of-00010'
+    shard = thread_index * num_shards_per_batch + s
+    output_filename = '%s-%.5d-of-%.5d' % (name, shard, num_shards)
+    output_file = os.path.join(FLAGS.output_directory, output_filename)
+    writer = tf.io.TFRecordWriter(output_file)
+
+    shard_counter = 0
+    files_in_shard = np.arange(shard_ranges[s], shard_ranges[s + 1], dtype=int) # HERE
+    for i in files_in_shard:
+      filename = filenames[i]
+      label = labels[i]
+      synset = synsets[i]
+      human = humans[i]
+
+      image_buffer, height, width = _process_image(filename, coder)
+
+      example = _convert_to_example(filename, image_buffer, label, synset, human, height, width)
+      writer.write(example.SerializeToString())
+      shard_counter += 1
+      counter += 1
+
+      if not counter % 1000:
+        print('%s [thread %d]: Processed %d of %d images in thread batch.' %
+              (datetime.now(), thread_index, counter, num_files_in_thread))
+        sys.stdout.flush()
+
+    writer.close()
+    print('%s [thread %d]: Wrote %d images to %s' %
+          (datetime.now(), thread_index, shard_counter, output_file))
+    sys.stdout.flush()
+    shard_counter = 0
+  print('%s [thread %d]: Wrote %d images to %d shards.' %
+        (datetime.now(), thread_index, counter, num_files_in_thread))
+  sys.stdout.flush()
+
+
+def _process_image_files(name, filenames, synsets, labels, humans, num_shards):
+  """Process and save list of images as TFRecord of Example protos.
+
+  Args:
+    name: string, unique identifier specifying the data set
+    filenames: list of strings; each string is a path to an image file
+    synsets: list of strings; each string is a unique WordNet ID
+    labels: list of integer; each integer identifies the ground truth
+    humans: list of strings; each string is a human-readable label
+    num_shards: integer number of shards for this data set.
+  """
+  assert len(filenames) == len(synsets)
+  assert len(filenames) == len(labels)
+  assert len(filenames) == len(humans)
+
+  # Break all images into batches with a [ranges[i][0], ranges[i][1]].
+  spacing = np.linspace(0, len(filenames), FLAGS.num_threads + 1).astype(np.int)
+  ranges = []
+  threads = []
+  for i in xrange(len(spacing) - 1):
+    ranges.append([spacing[i], spacing[i+1]])
+
+  # Launch a thread for each batch.
+  print('Launching %d threads for spacings: %s' % (FLAGS.num_threads, ranges))
+  sys.stdout.flush()
+
+  # Create a mechanism for monitoring when all threads are finished.
+  coord = tf.train.Coordinator()
+
+  # Create a generic TensorFlow-based utility for converting all image codings.
+  coder = ImageCoder()
+
+  threads = []
+  for thread_index in xrange(len(ranges)):
+    args = (coder, thread_index, ranges, name, filenames,
+            synsets, labels, humans, num_shards)
+    t = threading.Thread(target=_process_image_files_batch, args=args)
+    t.start()
+    threads.append(t)
+
+  # Wait for all the threads to terminate.
+  coord.join(threads)
+  print('%s: Finished writing all %d images in data set.' %
+        (datetime.now(), len(filenames)))
+  sys.stdout.flush()
+
+
+def _find_image_files(data_dir, labels_file):
+  """Build a list of all images files and labels in the data set.
+
+  Args:
+    data_dir: string, path to the root directory of images.
+
+      Assumes that the ImageNet data set resides in JPEG files located in
+      the following directory structure.
+
+        data_dir/n01440764/ILSVRC2012_val_00000293.JPEG
+        data_dir/n01440764/ILSVRC2012_val_00000543.JPEG
+
+      where 'n01440764' is the unique synset label associated with these images.
+
+    labels_file: string, path to the labels file.
+
+      The list of valid labels are held in this file. Assumes that the file
+      contains entries as such:
+        n01440764
+        n01443537
+        n01484850
+      where each line corresponds to a label expressed as a synset. We map
+      each synset contained in the file to an integer (based on the alphabetical
+      ordering) starting with the integer 1 corresponding to the synset
+      contained in the first line.
+
+      The reason we start the integer labels at 1 is to reserve label 0 as an
+      unused background class.
+
+  Returns:
+    filenames: list of strings; each string is a path to an image file.
+    synsets: list of strings; each string is a unique WordNet ID.
+    labels: list of integer; each integer identifies the ground truth.
+  """
+  print('Determining list of input files and labels from %s.' % data_dir)
+  challenge_synsets = [l.strip() for l in
+                       tf.compat.v1.gfile.FastGFile(labels_file, 'r').readlines()]
+
+  labels = []
+  filenames = []
+  synsets = []
+
+  # Leave label index 0 empty as a background class.
+  label_index = 1
+
+  # Construct the list of JPEG files and labels.
+  for synset in challenge_synsets:
+    jpeg_file_path = '%s/%s/*.JPEG' % (data_dir, synset)
+    matching_files = tf.io.gfile.glob(jpeg_file_path)
+
+    labels.extend([label_index] * len(matching_files))
+    synsets.extend([synset] * len(matching_files))
+    filenames.extend(matching_files)
+
+    if not label_index % 100:
+      print('Finished finding files in %d of %d classes.' % (
+          label_index, len(challenge_synsets)))
+    label_index += 1
+
+  # Shuffle the ordering of all image files in order to guarantee
+  # random ordering of the images with respect to label in the
+  # saved TFRecord files. Make the randomization repeatable.
+  shuffled_index = range(len(filenames))
+  random.seed(12345)
+
+  random.shuffle(list(range(len(shuffled_index))))
+
+  filenames = [filenames[i] for i in shuffled_index]
+  synsets = [synsets[i] for i in shuffled_index]
+  labels = [labels[i] for i in shuffled_index]
+
+  print('Found %d JPEG files across %d labels inside %s.' %
+        (len(filenames), len(challenge_synsets), data_dir))
+  return filenames, synsets, labels
+
+
+def _find_human_readable_labels(synsets, synset_to_human):
+  """Build a list of human-readable labels.
+
+  Args:
+    synsets: list of strings; each string is a unique WordNet ID.
+    synset_to_human: dict of synset to human labels, e.g.,
+      'n02119022' --> 'red fox, Vulpes vulpes'
+
+  Returns:
+    List of human-readable strings corresponding to each synset.
+  """
+  humans = []
+  for s in synsets:
+    assert s in synset_to_human, ('Failed to find: %s' % s)
+    humans.append(synset_to_human[s])
+  return humans
+
+
+def _process_dataset(name, directory, num_shards, synset_to_human):
+  """Process a complete data set and save it as a TFRecord.
+
+  Args:
+    name: string, unique identifier specifying the data set.
+    directory: string, root path to the data set.
+    num_shards: integer number of shards for this data set.
+    synset_to_human: dict of synset to human labels, e.g.,
+      'n02119022' --> 'red fox, Vulpes vulpes'
+  """
+  filenames, synsets, labels = _find_image_files(directory, FLAGS.labels_file)
+  humans = _find_human_readable_labels(synsets, synset_to_human)
+
+  _process_image_files(name, filenames, synsets, labels,
+                       humans, num_shards)
+
+
+def _build_synset_lookup(imagenet_metadata_file):
+  """Build lookup for synset to human-readable label.
+
+  Args:
+    imagenet_metadata_file: string, path to file containing mapping from
+      synset to human-readable label.
+
+      Assumes each line of the file looks like:
+
+        n02119247    black fox
+        n02119359    silver fox
+        n02119477    red fox, Vulpes fulva
+
+      where each line corresponds to a unique mapping. Note that each line is
+      formatted as <synset>\t<human readable label>.
+
+  Returns:
+    Dictionary of synset to human labels, such as:
+      'n02119022' --> 'red fox, Vulpes vulpes'
+  """
+  lines = tf.compat.v1.gfile.FastGFile(imagenet_metadata_file, 'r').readlines()
+  synset_to_human = {}
+  for l in lines:
+    if l:
+      parts = l.strip().split('\t')
+      assert len(parts) == 2
+      synset = parts[0]
+      human = parts[1]
+      synset_to_human[synset] = human
+  return synset_to_human
+
+
+def main(unused_argv):
+  assert not FLAGS.shards % FLAGS.num_threads, (
+      'Please make the FLAGS.num_threads commensurate with FLAGS.shards')
+
+  print('Saving results to %s' % FLAGS.output_directory)
+
+  # Build a map from synset to human-readable label.
+  synset_to_human = _build_synset_lookup(FLAGS.imagenet_metadata_file)
+
+  if(FLAGS.raw_directory != None):
+      _process_dataset(FLAGS.subset, FLAGS.raw_directory,FLAGS.shards, synset_to_human)
+
+if __name__ == '__main__':
+  tf.compat.v1.app.run()
diff --git a/examples/3.x_api/tensorflow/keras/cv/imagenet_prepare/download_and_convert_imagenet.sh b/examples/3.x_api/tensorflow/keras/cv/imagenet_prepare/download_and_convert_imagenet.sh
new file mode 100644
index 00000000000..f9baa85ab07
--- /dev/null
+++ b/examples/3.x_api/tensorflow/keras/cv/imagenet_prepare/download_and_convert_imagenet.sh
@@ -0,0 +1,100 @@
+#!/bin/bash
+# Copyright 2016 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+# Script to download and preprocess ImageNet Challenge 2012
+# training and validation data set.
+#
+# The final output of this script are sharded TFRecord files containing
+# serialized Example protocol buffers. See build_imagenet_data.py for
+# details of how the Example protocol buffers contain the ImageNet data.
+#
+# The final output of this script appears as such:
+#
+#   data_dir/train-00000-of-01024
+#   data_dir/train-00001-of-01024
+#    ...
+#   data_dir/train-00127-of-01024
+#
+# and
+#
+#   data_dir/validation-00000-of-00128
+#   data_dir/validation-00001-of-00128
+#   ...
+#   data_dir/validation-00127-of-00128
+#
+# Note that this script may take several hours to run to completion. The
+# conversion of the ImageNet data to TFRecords alone takes 2-3 hours depending
+# on the speed of your machine. Please be patient.
+#
+# **IMPORTANT**
+# To download the raw images, the user must create an account with image-net.org
+# and generate a username and access_key. The latter two are required for
+# downloading the raw images.
+#
+
+set -e
+
+if [ -z "$1" ]; then
+  echo "usage download_and_convert_imagenet.sh [data dir]"
+  exit
+fi
+
+# Create the output and temporary directories.
+DATA_DIR="${1%/}"
+SCRATCH_DIR="${DATA_DIR}/raw-data/"
+mkdir -p "${DATA_DIR}"
+mkdir -p "${SCRATCH_DIR}"
+WORK_DIR="$0.runfiles/__main__"
+
+# Download the ImageNet data.
+LABELS_FILE="${WORK_DIR}/datasets/imagenet_lsvrc_2015_synsets.txt"
+DOWNLOAD_SCRIPT="${WORK_DIR}/datasets/download_imagenet.sh"
+"${DOWNLOAD_SCRIPT}" "${SCRATCH_DIR}" "${LABELS_FILE}"
+
+# Note the locations of the train and validation data.
+TRAIN_DIRECTORY="${SCRATCH_DIR}train/"
+VALIDATION_DIRECTORY="${SCRATCH_DIR}validation/"
+
+# Preprocess the validation data by moving the images into the appropriate
+# sub-directory based on the label (synset) of the image.
+echo "Organizing the validation data into sub-directories."
+PREPROCESS_VAL_SCRIPT="${WORK_DIR}/datasets/preprocess_imagenet_validation_data.py"
+VAL_LABELS_FILE="${WORK_DIR}/datasets/imagenet_2012_validation_synset_labels.txt"
+
+"${PREPROCESS_VAL_SCRIPT}" "${VALIDATION_DIRECTORY}" "${VAL_LABELS_FILE}"
+
+# Convert the XML files for bounding box annotations into a single CSV.
+echo "Extracting bounding box information from XML."
+BOUNDING_BOX_SCRIPT="${WORK_DIR}/datasets/process_bounding_boxes.py"
+BOUNDING_BOX_FILE="${SCRATCH_DIR}/imagenet_2012_bounding_boxes.csv"
+BOUNDING_BOX_DIR="${SCRATCH_DIR}bounding_boxes/"
+
+"${BOUNDING_BOX_SCRIPT}" "${BOUNDING_BOX_DIR}" "${LABELS_FILE}" \
+ | sort >"${BOUNDING_BOX_FILE}"
+echo "Finished downloading and preprocessing the ImageNet data."
+
+# Build the TFRecords version of the ImageNet data.
+BUILD_SCRIPT="${WORK_DIR}/build_imagenet_data"
+OUTPUT_DIRECTORY="${DATA_DIR}"
+IMAGENET_METADATA_FILE="${WORK_DIR}/datasets/imagenet_metadata.txt"
+
+"${BUILD_SCRIPT}" \
+  --train_directory="${TRAIN_DIRECTORY}" \
+  --validation_directory="${VALIDATION_DIRECTORY}" \
+  --output_directory="${OUTPUT_DIRECTORY}" \
+  --imagenet_metadata_file="${IMAGENET_METADATA_FILE}" \
+  --labels_file="${LABELS_FILE}" \
+  --bounding_box_file="${BOUNDING_BOX_FILE}"
diff --git a/examples/3.x_api/tensorflow/keras/cv/imagenet_prepare/download_imagenet.sh b/examples/3.x_api/tensorflow/keras/cv/imagenet_prepare/download_imagenet.sh
new file mode 100644
index 00000000000..c780e179f93
--- /dev/null
+++ b/examples/3.x_api/tensorflow/keras/cv/imagenet_prepare/download_imagenet.sh
@@ -0,0 +1,99 @@
+#!/bin/bash
+# Copyright 2016 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+# Script to download ImageNet Challenge 2012 training and validation data set.
+#
+# Downloads and decompresses raw images and bounding boxes.
+#
+# **IMPORTANT**
+# To download the raw images, the user must create an account with image-net.org
+# and generate a username and access_key. The latter two are required for
+# downloading the raw images.
+#
+# usage:
+#  ./download_imagenet.sh [dirname]
+set -e
+
+if [ "x$IMAGENET_ACCESS_KEY" == x -o "x$IMAGENET_USERNAME" == x ]; then
+  cat <<END
+In order to download the imagenet data, you have to create an account with
+image-net.org. This will get you a username and an access key. You can set the
+IMAGENET_USERNAME and IMAGENET_ACCESS_KEY environment variables, or you can
+enter the credentials here.
+END
+  read -p "Username: " IMAGENET_USERNAME
+  read -p "Access key: " IMAGENET_ACCESS_KEY
+fi
+
+OUTDIR="${1:-./imagenet-data}"
+SYNSETS_FILE="${2:-./synsets.txt}"
+
+echo "Saving downloaded files to $OUTDIR"
+mkdir -p "${OUTDIR}"
+CURRENT_DIR=$(pwd)
+BBOX_DIR="${OUTDIR}bounding_boxes"
+mkdir -p "${BBOX_DIR}"
+cd "${OUTDIR}"
+
+# Download and process all of the ImageNet bounding boxes.
+BASE_URL="http://www.image-net.org/challenges/LSVRC/2012/nnoupb"
+
+# See here for details: http://www.image-net.org/download-bboxes
+BOUNDING_BOX_ANNOTATIONS="${BASE_URL}/ILSVRC2012_bbox_train_v2.tar.gz"
+BBOX_TAR_BALL="${BBOX_DIR}/annotations.tar.gz"
+echo "Downloading bounding box annotations."
+wget "${BOUNDING_BOX_ANNOTATIONS}" -O "${BBOX_TAR_BALL}"
+echo "Uncompressing bounding box annotations ..."
+tar xzf "${BBOX_TAR_BALL}" -C "${BBOX_DIR}"
+
+LABELS_ANNOTATED="${BBOX_DIR}/*"
+NUM_XML=$(ls -1 ${LABELS_ANNOTATED} | wc -l)
+echo "Identified ${NUM_XML} bounding box annotations."
+
+# Download and uncompress all images from the ImageNet 2012 validation dataset.
+VALIDATION_TARBALL="ILSVRC2012_img_val.tar"
+OUTPUT_PATH="${OUTDIR}validation/"
+mkdir -p "${OUTPUT_PATH}"
+cd "${OUTDIR}/.."
+echo "Downloading ${VALIDATION_TARBALL} to ${OUTPUT_PATH}."
+wget -nd -c "${BASE_URL}/${VALIDATION_TARBALL}"
+tar xf "${VALIDATION_TARBALL}" -C "${OUTPUT_PATH}"
+
+# Download all images from the ImageNet 2012 train dataset.
+TRAIN_TARBALL="ILSVRC2012_img_train.tar"
+OUTPUT_PATH="${OUTDIR}train/"
+mkdir -p "${OUTPUT_PATH}"
+cd "${OUTDIR}/.."
+echo "Downloading ${TRAIN_TARBALL} to ${OUTPUT_PATH}."
+wget -nd -c "${BASE_URL}/${TRAIN_TARBALL}"
+
+# Un-compress the individual tar-files within the train tar-file.
+echo "Uncompressing individual train tar-balls in the training data."
+
+while read SYNSET; do
+  echo "Processing: ${SYNSET}"
+
+  # Create a directory and delete anything there.
+  mkdir -p "${OUTPUT_PATH}/${SYNSET}"
+  rm -rf "${OUTPUT_PATH}/${SYNSET}/*"
+
+  # Uncompress into the directory.
+  tar xf "${TRAIN_TARBALL}" "${SYNSET}.tar"
+  tar xf "${SYNSET}.tar" -C "${OUTPUT_PATH}/${SYNSET}/"
+  rm -f "${SYNSET}.tar"
+
+  echo "Finished processing: ${SYNSET}"
+done < "${SYNSETS_FILE}"
diff --git a/examples/3.x_api/tensorflow/keras/cv/imagenet_prepare/imagenet_lsvrc_2015_synsets.txt b/examples/3.x_api/tensorflow/keras/cv/imagenet_prepare/imagenet_lsvrc_2015_synsets.txt
new file mode 100644
index 00000000000..88aa58f966b
--- /dev/null
+++ b/examples/3.x_api/tensorflow/keras/cv/imagenet_prepare/imagenet_lsvrc_2015_synsets.txt
@@ -0,0 +1,1000 @@
+n01440764
+n01443537
+n01484850
+n01491361
+n01494475
+n01496331
+n01498041
+n01514668
+n01514859
+n01518878
+n01530575
+n01531178
+n01532829
+n01534433
+n01537544
+n01558993
+n01560419
+n01580077
+n01582220
+n01592084
+n01601694
+n01608432
+n01614925
+n01616318
+n01622779
+n01629819
+n01630670
+n01631663
+n01632458
+n01632777
+n01641577
+n01644373
+n01644900
+n01664065
+n01665541
+n01667114
+n01667778
+n01669191
+n01675722
+n01677366
+n01682714
+n01685808
+n01687978
+n01688243
+n01689811
+n01692333
+n01693334
+n01694178
+n01695060
+n01697457
+n01698640
+n01704323
+n01728572
+n01728920
+n01729322
+n01729977
+n01734418
+n01735189
+n01737021
+n01739381
+n01740131
+n01742172
+n01744401
+n01748264
+n01749939
+n01751748
+n01753488
+n01755581
+n01756291
+n01768244
+n01770081
+n01770393
+n01773157
+n01773549
+n01773797
+n01774384
+n01774750
+n01775062
+n01776313
+n01784675
+n01795545
+n01796340
+n01797886
+n01798484
+n01806143
+n01806567
+n01807496
+n01817953
+n01818515
+n01819313
+n01820546
+n01824575
+n01828970
+n01829413
+n01833805
+n01843065
+n01843383
+n01847000
+n01855032
+n01855672
+n01860187
+n01871265
+n01872401
+n01873310
+n01877812
+n01882714
+n01883070
+n01910747
+n01914609
+n01917289
+n01924916
+n01930112
+n01943899
+n01944390
+n01945685
+n01950731
+n01955084
+n01968897
+n01978287
+n01978455
+n01980166
+n01981276
+n01983481
+n01984695
+n01985128
+n01986214
+n01990800
+n02002556
+n02002724
+n02006656
+n02007558
+n02009229
+n02009912
+n02011460
+n02012849
+n02013706
+n02017213
+n02018207
+n02018795
+n02025239
+n02027492
+n02028035
+n02033041
+n02037110
+n02051845
+n02056570
+n02058221
+n02066245
+n02071294
+n02074367
+n02077923
+n02085620
+n02085782
+n02085936
+n02086079
+n02086240
+n02086646
+n02086910
+n02087046
+n02087394
+n02088094
+n02088238
+n02088364
+n02088466
+n02088632
+n02089078
+n02089867
+n02089973
+n02090379
+n02090622
+n02090721
+n02091032
+n02091134
+n02091244
+n02091467
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diff --git a/examples/3.x_api/tensorflow/keras/cv/imagenet_prepare/imagenet_metadata.txt b/examples/3.x_api/tensorflow/keras/cv/imagenet_prepare/imagenet_metadata.txt
new file mode 100644
index 00000000000..18e15288c3b
--- /dev/null
+++ b/examples/3.x_api/tensorflow/keras/cv/imagenet_prepare/imagenet_metadata.txt
@@ -0,0 +1,21842 @@
+n00004475	organism, being
+n00005787	benthos
+n00006024	heterotroph
+n00006484	cell
+n00007846	person, individual, someone, somebody, mortal, soul
+n00015388	animal, animate being, beast, brute, creature, fauna
+n00017222	plant, flora, plant life
+n00021265	food, nutrient
+n00021939	artifact, artefact
+n00120010	hop
+n00141669	check-in
+n00288000	dressage
+n00288190	curvet, vaulting
+n00288384	piaffe
+n00324978	funambulism, tightrope walking
+n00326094	rock climbing
+n00433458	contact sport
+n00433661	outdoor sport, field sport
+n00433802	gymnastics, gymnastic exercise
+n00434075	acrobatics, tumbling
+n00439826	track and field
+n00440039	track, running
+n00440218	jumping
+n00440382	broad jump, long jump
+n00440509	high jump
+n00440643	Fosbury flop
+n00440747	skiing
+n00440941	cross-country skiing
+n00441073	ski jumping
+n00441824	water sport, aquatics
+n00442115	swimming, swim
+n00442437	bathe
+n00442847	dip, plunge
+n00442981	dive, diving
+n00443231	floating, natation
+n00443375	dead-man's float, prone float
+n00443517	belly flop, belly flopper, belly whop, belly whopper
+n00443692	cliff diving
+n00443803	flip
+n00443917	gainer, full gainer
+n00444142	half gainer
+n00444340	jackknife
+n00444490	swan dive, swallow dive
+n00444651	skin diving, skin-dive
+n00444846	scuba diving
+n00444937	snorkeling, snorkel diving
+n00445055	surfing, surfboarding, surfriding
+n00445226	water-skiing
+n00445351	rowing, row
+n00445685	sculling
+n00445802	boxing, pugilism, fisticuffs
+n00446311	professional boxing
+n00446411	in-fighting
+n00446493	fight
+n00446632	rope-a-dope
+n00446804	spar, sparring
+n00446980	archery
+n00447073	sledding
+n00447221	tobogganing
+n00447361	luging
+n00447463	bobsledding
+n00447540	wrestling, rassling, grappling
+n00447957	Greco-Roman wrestling
+n00448126	professional wrestling
+n00448232	sumo
+n00448466	skating
+n00448640	ice skating
+n00448748	figure skating
+n00448872	rollerblading
+n00448958	roller skating
+n00449054	skateboarding
+n00449168	speed skating
+n00449295	racing
+n00449517	auto racing, car racing
+n00449695	boat racing
+n00449796	hydroplane racing
+n00449892	camel racing
+n00449977	greyhound racing
+n00450070	horse racing
+n00450335	riding, horseback riding, equitation
+n00450700	equestrian sport
+n00450866	pony-trekking
+n00450998	showjumping, stadium jumping
+n00451186	cross-country riding, cross-country jumping
+n00451370	cycling
+n00451563	bicycling
+n00451635	motorcycling
+n00451768	dune cycling
+n00451866	blood sport
+n00452034	bullfighting, tauromachy
+n00452152	cockfighting
+n00452293	hunt, hunting
+n00452734	battue
+n00452864	beagling
+n00453126	coursing
+n00453313	deer hunting, deer hunt
+n00453396	ducking, duck hunting
+n00453478	fox hunting, foxhunt
+n00453631	pigsticking
+n00453935	fishing, sportfishing
+n00454237	angling
+n00454395	fly-fishing
+n00454493	troll, trolling
+n00454624	casting, cast
+n00454855	bait casting
+n00454983	fly casting
+n00455076	overcast
+n00455173	surf casting, surf fishing
+n00456465	day game
+n00463246	athletic game
+n00463543	ice hockey, hockey, hockey game
+n00464277	tetherball
+n00464478	water polo
+n00464651	outdoor game
+n00464894	golf, golf game
+n00466273	professional golf
+n00466377	round of golf, round
+n00466524	medal play, stroke play
+n00466630	match play
+n00466712	miniature golf
+n00466880	croquet
+n00467320	quoits, horseshoes
+n00467536	shuffleboard, shovelboard
+n00467719	field game
+n00467995	field hockey, hockey
+n00468299	shinny, shinney
+n00468480	football, football game
+n00469651	American football, American football game
+n00470554	professional football
+n00470682	touch football
+n00470830	hurling
+n00470966	rugby, rugby football, rugger
+n00471437	ball game, ballgame
+n00471613	baseball, baseball game
+n00474568	ball
+n00474657	professional baseball
+n00474769	hardball
+n00474881	perfect game
+n00475014	no-hit game, no-hitter
+n00475142	one-hitter, 1-hitter
+n00475273	two-hitter, 2-hitter
+n00475403	three-hitter, 3-hitter
+n00475535	four-hitter, 4-hitter
+n00475661	five-hitter, 5-hitter
+n00475787	softball, softball game
+n00476140	rounders
+n00476235	stickball, stickball game
+n00476389	cricket
+n00477392	lacrosse
+n00477639	polo
+n00477827	pushball
+n00478262	soccer, association football
+n00479076	court game
+n00479440	handball
+n00479616	racquetball
+n00479734	fives
+n00479887	squash, squash racquets, squash rackets
+n00480211	volleyball, volleyball game
+n00480366	jai alai, pelota
+n00480508	badminton
+n00480885	battledore, battledore and shuttlecock
+n00480993	basketball, basketball game, hoops
+n00481803	professional basketball
+n00481938	deck tennis
+n00482122	netball
+n00482298	tennis, lawn tennis
+n00483205	professional tennis
+n00483313	singles
+n00483409	singles
+n00483508	doubles
+n00483605	doubles
+n00483705	royal tennis, real tennis, court tennis
+n00483848	pallone
+n00523513	sport, athletics
+n00812526	clasp, clench, clutch, clutches, grasp, grip, hold
+n00825773	judo
+n00887544	team sport
+n01035504	Last Supper, Lord's Supper
+n01035667	Seder, Passover supper
+n01055165	camping, encampment, bivouacking, tenting
+n01314388	pest
+n01314663	critter
+n01314781	creepy-crawly
+n01314910	darter
+n01315213	peeper
+n01315330	homeotherm, homoiotherm, homotherm
+n01315581	poikilotherm, ectotherm
+n01315805	range animal
+n01316422	scavenger
+n01316579	bottom-feeder, bottom-dweller
+n01316734	bottom-feeder
+n01316949	work animal
+n01317089	beast of burden, jument
+n01317294	draft animal
+n01317391	pack animal, sumpter
+n01317541	domestic animal, domesticated animal
+n01317813	feeder
+n01317916	feeder
+n01318053	stocker
+n01318279	hatchling
+n01318381	head
+n01318478	migrator
+n01318660	molter, moulter
+n01318894	pet
+n01319001	stayer
+n01319187	stunt
+n01319467	marine animal, marine creature, sea animal, sea creature
+n01319685	by-catch, bycatch
+n01320872	female
+n01321123	hen
+n01321230	male
+n01321456	adult
+n01321579	young, offspring
+n01321770	orphan
+n01321854	young mammal
+n01322221	baby
+n01322343	pup, whelp
+n01322508	wolf pup, wolf cub
+n01322604	puppy
+n01322685	cub, young carnivore
+n01322898	lion cub
+n01322983	bear cub
+n01323068	tiger cub
+n01323155	kit
+n01323261	suckling
+n01323355	sire
+n01323493	dam
+n01323599	thoroughbred, purebred, pureblood
+n01323781	giant
+n01324305	mutant
+n01324431	carnivore
+n01324610	herbivore
+n01324799	insectivore
+n01324916	acrodont
+n01325060	pleurodont
+n01326291	microorganism, micro-organism
+n01327909	monohybrid
+n01329186	arbovirus, arborvirus
+n01330126	adenovirus
+n01330497	arenavirus
+n01332181	Marburg virus
+n01333082	Arenaviridae
+n01333483	vesiculovirus
+n01333610	Reoviridae
+n01334217	variola major, variola major virus
+n01334690	viroid, virusoid
+n01335218	coliphage
+n01337191	paramyxovirus
+n01337734	poliovirus
+n01338685	herpes, herpes virus
+n01339083	herpes simplex 1, HS1, HSV-1, HSV-I
+n01339336	herpes zoster, herpes zoster virus
+n01339471	herpes varicella zoster, herpes varicella zoster virus
+n01339801	cytomegalovirus, CMV
+n01340014	varicella zoster virus
+n01340522	polyoma, polyoma virus
+n01340785	lyssavirus
+n01340935	reovirus
+n01341090	rotavirus
+n01342269	moneran, moneron
+n01347583	archaebacteria, archaebacterium, archaeobacteria, archeobacteria
+n01349735	bacteroid
+n01350226	Bacillus anthracis, anthrax bacillus
+n01350701	Yersinia pestis
+n01351170	Brucella
+n01351315	spirillum, spirilla
+n01357328	botulinus, botulinum, Clostridium botulinum
+n01357507	clostridium perfringens
+n01358572	cyanobacteria, blue-green algae
+n01359762	trichodesmium
+n01362336	nitric bacteria, nitrobacteria
+n01363719	spirillum
+n01365474	Francisella, genus Francisella
+n01365885	gonococcus, Neisseria gonorrhoeae
+n01366700	Corynebacterium diphtheriae, C. diphtheriae, Klebs-Loeffler bacillus
+n01367772	enteric bacteria, enterobacteria, enterics, entric
+n01368672	klebsiella
+n01369358	Salmonella typhimurium
+n01369484	typhoid bacillus, Salmonella typhosa, Salmonella typhi
+n01374703	nitrate bacterium, nitric bacterium
+n01374846	nitrite bacterium, nitrous bacterium
+n01375204	actinomycete
+n01376237	streptomyces
+n01376437	Streptomyces erythreus
+n01376543	Streptomyces griseus
+n01377278	tubercle bacillus, Mycobacterium tuberculosis
+n01377510	pus-forming bacteria
+n01377694	streptobacillus
+n01378545	myxobacteria, myxobacterium, myxobacter, gliding bacteria, slime bacteria
+n01379389	staphylococcus, staphylococci, staph
+n01380610	diplococcus
+n01380754	pneumococcus, Diplococcus pneumoniae
+n01381044	streptococcus, streptococci, strep
+n01382033	spirochete, spirochaete
+n01384084	planktonic algae
+n01384164	zooplankton
+n01384687	parasite
+n01385017	endoparasite, entoparasite, entozoan, entozoon, endozoan
+n01385330	ectoparasite, ectozoan, ectozoon, epizoan, epizoon
+n01386007	pathogen
+n01386182	commensal
+n01386354	myrmecophile
+n01387065	protoctist
+n01389507	protozoan, protozoon
+n01390123	sarcodinian, sarcodine
+n01390763	heliozoan
+n01392275	endameba
+n01392380	ameba, amoeba
+n01393486	globigerina
+n01394040	testacean
+n01394492	arcella
+n01394771	difflugia
+n01395254	ciliate, ciliated protozoan, ciliophoran
+n01396048	paramecium, paramecia
+n01396617	stentor
+n01397114	alga, algae
+n01397690	arame
+n01397871	seagrass
+n01400247	golden algae
+n01400391	yellow-green algae
+n01402600	brown algae
+n01403457	kelp
+n01404365	fucoid, fucoid algae
+n01404495	fucoid
+n01405007	fucus
+n01405616	bladderwrack, Ascophyllum nodosum
+n01407798	green algae, chlorophyte
+n01410457	pond scum
+n01411450	chlorella
+n01412694	stonewort
+n01413457	desmid
+n01414216	sea moss
+n01415626	eukaryote, eucaryote
+n01415920	prokaryote, procaryote
+n01416213	zooid
+n01418498	Leishmania, genus Leishmania
+n01418620	zoomastigote, zooflagellate
+n01419332	polymastigote
+n01419573	costia, Costia necatrix
+n01419888	giardia
+n01421333	cryptomonad, cryptophyte
+n01421807	sporozoan
+n01422185	sporozoite
+n01422335	trophozoite
+n01422450	merozoite
+n01423302	coccidium, eimeria
+n01423617	gregarine
+n01424420	plasmodium, Plasmodium vivax, malaria parasite
+n01425223	leucocytozoan, leucocytozoon
+n01427399	microsporidian
+n01429172	Ostariophysi, order Ostariophysi
+n01438208	cypriniform fish
+n01438581	loach
+n01439121	cyprinid, cyprinid fish
+n01439514	carp
+n01439808	domestic carp, Cyprinus carpio
+n01440160	leather carp
+n01440242	mirror carp
+n01440467	European bream, Abramis brama
+n01440764	tench, Tinca tinca
+n01441117	dace, Leuciscus leuciscus
+n01441272	chub, Leuciscus cephalus
+n01441425	shiner
+n01441910	common shiner, silversides, Notropis cornutus
+n01442450	roach, Rutilus rutilus
+n01442710	rudd, Scardinius erythrophthalmus
+n01442972	minnow, Phoxinus phoxinus
+n01443243	gudgeon, Gobio gobio
+n01443537	goldfish, Carassius auratus
+n01443831	crucian carp, Carassius carassius, Carassius vulgaris
+n01444339	electric eel, Electrophorus electric
+n01444783	catostomid
+n01445429	buffalo fish, buffalofish
+n01445593	black buffalo, Ictiobus niger
+n01445857	hog sucker, hog molly, Hypentelium nigricans
+n01446152	redhorse, redhorse sucker
+n01446589	cyprinodont
+n01446760	killifish
+n01447139	mummichog, Fundulus heteroclitus
+n01447331	striped killifish, mayfish, may fish, Fundulus majalis
+n01447658	rivulus
+n01447946	flagfish, American flagfish, Jordanella floridae
+n01448291	swordtail, helleri, topminnow, Xyphophorus helleri
+n01448594	guppy, rainbow fish, Lebistes reticulatus
+n01448951	topminnow, poeciliid fish, poeciliid, live-bearer
+n01449374	mosquitofish, Gambusia affinis
+n01449712	platy, Platypoecilus maculatus
+n01449980	mollie, molly
+n01450661	squirrelfish
+n01450950	reef squirrelfish, Holocentrus coruscus
+n01451115	deepwater squirrelfish, Holocentrus bullisi
+n01451295	Holocentrus ascensionis
+n01451426	soldierfish, soldier-fish
+n01451863	anomalops, flashlight fish
+n01452345	flashlight fish, Photoblepharon palpebratus
+n01453087	John Dory, Zeus faber
+n01453475	boarfish, Capros aper
+n01453742	boarfish
+n01454545	cornetfish
+n01454856	stickleback, prickleback
+n01455317	three-spined stickleback, Gasterosteus aculeatus
+n01455461	ten-spined stickleback, Gasterosteus pungitius
+n01455778	pipefish, needlefish
+n01456137	dwarf pipefish, Syngnathus hildebrandi
+n01456454	deepwater pipefish, Cosmocampus profundus
+n01456756	seahorse, sea horse
+n01457082	snipefish, bellows fish
+n01457407	shrimpfish, shrimp-fish
+n01457852	trumpetfish, Aulostomus maculatus
+n01458746	pellicle
+n01458842	embryo, conceptus, fertilized egg
+n01459791	fetus, foetus
+n01460303	abortus
+n01461315	spawn
+n01461646	blastula, blastosphere
+n01462042	blastocyst, blastodermic vessicle
+n01462544	gastrula
+n01462803	morula
+n01464844	yolk, vitellus
+n01466257	chordate
+n01467336	cephalochordate
+n01467804	lancelet, amphioxus
+n01468238	tunicate, urochordate, urochord
+n01468712	ascidian
+n01469103	sea squirt
+n01469723	salp, salpa
+n01470145	doliolum
+n01470479	larvacean
+n01470733	appendicularia
+n01470895	ascidian tadpole
+n01471682	vertebrate, craniate
+n01472303	Amniota
+n01472502	amniote
+n01473806	aquatic vertebrate
+n01474283	jawless vertebrate, jawless fish, agnathan
+n01474864	ostracoderm
+n01475232	heterostracan
+n01475940	anaspid
+n01476418	conodont
+n01477080	cyclostome
+n01477525	lamprey, lamprey eel, lamper eel
+n01477875	sea lamprey, Petromyzon marinus
+n01478511	hagfish, hag, slime eels
+n01478969	Myxine glutinosa
+n01479213	eptatretus
+n01479820	gnathostome
+n01480106	placoderm
+n01480516	cartilaginous fish, chondrichthian
+n01480880	holocephalan, holocephalian
+n01481331	chimaera
+n01481498	rabbitfish, Chimaera monstrosa
+n01482071	elasmobranch, selachian
+n01482330	shark
+n01483021	cow shark, six-gilled shark, Hexanchus griseus
+n01483522	mackerel shark
+n01483830	porbeagle, Lamna nasus
+n01484097	mako, mako shark
+n01484285	shortfin mako, Isurus oxyrhincus
+n01484447	longfin mako, Isurus paucus
+n01484562	bonito shark, blue pointed, Isurus glaucus
+n01484850	great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias
+n01485479	basking shark, Cetorhinus maximus
+n01486010	thresher, thrasher, thresher shark, fox shark, Alopius vulpinus
+n01486540	carpet shark, Orectolobus barbatus
+n01486838	nurse shark, Ginglymostoma cirratum
+n01487506	sand tiger, sand shark, Carcharias taurus, Odontaspis taurus
+n01488038	whale shark, Rhincodon typus
+n01488918	requiem shark
+n01489501	bull shark, cub shark, Carcharhinus leucas
+n01489709	sandbar shark, Carcharhinus plumbeus
+n01489920	blacktip shark, sandbar shark, Carcharhinus limbatus
+n01490112	whitetip shark, oceanic whitetip shark, white-tipped shark, Carcharinus longimanus
+n01490360	dusky shark, Carcharhinus obscurus
+n01490670	lemon shark, Negaprion brevirostris
+n01491006	blue shark, great blue shark, Prionace glauca
+n01491361	tiger shark, Galeocerdo cuvieri
+n01491661	soupfin shark, soupfin, soup-fin, Galeorhinus zyopterus
+n01491874	dogfish
+n01492357	smooth dogfish
+n01492569	smoothhound, smoothhound shark, Mustelus mustelus
+n01492708	American smooth dogfish, Mustelus canis
+n01492860	Florida smoothhound, Mustelus norrisi
+n01493146	whitetip shark, reef whitetip shark, Triaenodon obseus
+n01493541	spiny dogfish
+n01493829	Atlantic spiny dogfish, Squalus acanthias
+n01494041	Pacific spiny dogfish, Squalus suckleyi
+n01494475	hammerhead, hammerhead shark
+n01494757	smooth hammerhead, Sphyrna zygaena
+n01494882	smalleye hammerhead, Sphyrna tudes
+n01495006	shovelhead, bonnethead, bonnet shark, Sphyrna tiburo
+n01495493	angel shark, angelfish, Squatina squatina, monkfish
+n01495701	ray
+n01496331	electric ray, crampfish, numbfish, torpedo
+n01497118	sawfish
+n01497413	smalltooth sawfish, Pristis pectinatus
+n01497738	guitarfish
+n01498041	stingray
+n01498406	roughtail stingray, Dasyatis centroura
+n01498699	butterfly ray
+n01498989	eagle ray
+n01499396	spotted eagle ray, spotted ray, Aetobatus narinari
+n01499732	cownose ray, cow-nosed ray, Rhinoptera bonasus
+n01500091	manta, manta ray, devilfish
+n01500476	Atlantic manta, Manta birostris
+n01500854	devil ray, Mobula hypostoma
+n01501160	skate
+n01501641	grey skate, gray skate, Raja batis
+n01501777	little skate, Raja erinacea
+n01501948	thorny skate, Raja radiata
+n01502101	barndoor skate, Raja laevis
+n01503061	bird
+n01503976	dickeybird, dickey-bird, dickybird, dicky-bird
+n01504179	fledgling, fledgeling
+n01504344	nestling, baby bird
+n01514668	cock
+n01514752	gamecock, fighting cock
+n01514859	hen
+n01514926	nester
+n01515078	night bird
+n01515217	night raven
+n01515303	bird of passage
+n01516212	archaeopteryx, archeopteryx, Archaeopteryx lithographica
+n01517389	archaeornis
+n01517565	ratite, ratite bird, flightless bird
+n01517966	carinate, carinate bird, flying bird
+n01518878	ostrich, Struthio camelus
+n01519563	cassowary
+n01519873	emu, Dromaius novaehollandiae, Emu novaehollandiae
+n01520576	kiwi, apteryx
+n01521399	rhea, Rhea americana
+n01521756	rhea, nandu, Pterocnemia pennata
+n01522450	elephant bird, aepyornis
+n01523105	moa
+n01524359	passerine, passeriform bird
+n01524761	nonpasserine bird
+n01525720	oscine, oscine bird
+n01526521	songbird, songster
+n01526766	honey eater, honeysucker
+n01527194	accentor
+n01527347	hedge sparrow, sparrow, dunnock, Prunella modularis
+n01527617	lark
+n01527917	skylark, Alauda arvensis
+n01528396	wagtail
+n01528654	pipit, titlark, lark
+n01528845	meadow pipit, Anthus pratensis
+n01529672	finch
+n01530439	chaffinch, Fringilla coelebs
+n01530575	brambling, Fringilla montifringilla
+n01531178	goldfinch, Carduelis carduelis
+n01531344	linnet, lintwhite, Carduelis cannabina
+n01531512	siskin, Carduelis spinus
+n01531639	red siskin, Carduelis cucullata
+n01531811	redpoll, Carduelis flammea
+n01531971	redpoll, Carduelis hornemanni
+n01532325	New World goldfinch, goldfinch, yellowbird, Spinus tristis
+n01532511	pine siskin, pine finch, Spinus pinus
+n01532829	house finch, linnet, Carpodacus mexicanus
+n01533000	purple finch, Carpodacus purpureus
+n01533339	canary, canary bird
+n01533481	common canary, Serinus canaria
+n01533651	serin
+n01533893	crossbill, Loxia curvirostra
+n01534155	bullfinch, Pyrrhula pyrrhula
+n01534433	junco, snowbird
+n01534582	dark-eyed junco, slate-colored junco, Junco hyemalis
+n01534762	New World sparrow
+n01535140	vesper sparrow, grass finch, Pooecetes gramineus
+n01535469	white-throated sparrow, whitethroat, Zonotrichia albicollis
+n01535690	white-crowned sparrow, Zonotrichia leucophrys
+n01536035	chipping sparrow, Spizella passerina
+n01536186	field sparrow, Spizella pusilla
+n01536334	tree sparrow, Spizella arborea
+n01536644	song sparrow, Melospiza melodia
+n01536780	swamp sparrow, Melospiza georgiana
+n01537134	bunting
+n01537544	indigo bunting, indigo finch, indigo bird, Passerina cyanea
+n01537895	ortolan, ortolan bunting, Emberiza hortulana
+n01538059	reed bunting, Emberiza schoeniclus
+n01538200	yellowhammer, yellow bunting, Emberiza citrinella
+n01538362	yellow-breasted bunting, Emberiza aureola
+n01538630	snow bunting, snowbird, snowflake, Plectrophenax nivalis
+n01538955	honeycreeper
+n01539272	banana quit
+n01539573	sparrow, true sparrow
+n01539925	English sparrow, house sparrow, Passer domesticus
+n01540090	tree sparrow, Passer montanus
+n01540233	grosbeak, grossbeak
+n01540566	evening grosbeak, Hesperiphona vespertina
+n01540832	hawfinch, Coccothraustes coccothraustes
+n01541102	pine grosbeak, Pinicola enucleator
+n01541386	cardinal, cardinal grosbeak, Richmondena Cardinalis, Cardinalis cardinalis, redbird
+n01541760	pyrrhuloxia, Pyrrhuloxia sinuata
+n01541922	towhee
+n01542168	chewink, cheewink, Pipilo erythrophthalmus
+n01542433	green-tailed towhee, Chlorura chlorura
+n01542786	weaver, weaverbird, weaver finch
+n01543175	baya, Ploceus philippinus
+n01543383	whydah, whidah, widow bird
+n01543632	Java sparrow, Java finch, ricebird, Padda oryzivora
+n01543936	avadavat, amadavat
+n01544208	grassfinch, grass finch
+n01544389	zebra finch, Poephila castanotis
+n01544704	honeycreeper, Hawaiian honeycreeper
+n01545574	lyrebird
+n01546039	scrubbird, scrub-bird, scrub bird
+n01546506	broadbill
+n01546921	tyrannid
+n01547832	New World flycatcher, flycatcher, tyrant flycatcher, tyrant bird
+n01548301	kingbird, Tyrannus tyrannus
+n01548492	Arkansas kingbird, western kingbird
+n01548694	Cassin's kingbird, Tyrannus vociferans
+n01548865	eastern kingbird
+n01549053	grey kingbird, gray kingbird, petchary, Tyrannus domenicensis domenicensis
+n01549430	pewee, peewee, peewit, pewit, wood pewee, Contopus virens
+n01549641	western wood pewee, Contopus sordidulus
+n01549886	phoebe, phoebe bird, Sayornis phoebe
+n01550172	vermillion flycatcher, firebird, Pyrocephalus rubinus mexicanus
+n01550761	cotinga, chatterer
+n01551080	cock of the rock, Rupicola rupicola
+n01551300	cock of the rock, Rupicola peruviana
+n01551711	manakin
+n01552034	bellbird
+n01552333	umbrella bird, Cephalopterus ornatus
+n01552813	ovenbird
+n01553142	antbird, ant bird
+n01553527	ant thrush
+n01553762	ant shrike
+n01554017	spotted antbird, Hylophylax naevioides
+n01554448	woodhewer, woodcreeper, wood-creeper, tree creeper
+n01555004	pitta
+n01555305	scissortail, scissortailed flycatcher, Muscivora-forficata
+n01555809	Old World flycatcher, true flycatcher, flycatcher
+n01556182	spotted flycatcher, Muscicapa striata, Muscicapa grisola
+n01556514	thickhead, whistler
+n01557185	thrush
+n01557962	missel thrush, mistle thrush, mistletoe thrush, Turdus viscivorus
+n01558149	song thrush, mavis, throstle, Turdus philomelos
+n01558307	fieldfare, snowbird, Turdus pilaris
+n01558461	redwing, Turdus iliacus
+n01558594	blackbird, merl, merle, ouzel, ousel, European blackbird, Turdus merula
+n01558765	ring ouzel, ring blackbird, ring thrush, Turdus torquatus
+n01558993	robin, American robin, Turdus migratorius
+n01559160	clay-colored robin, Turdus greyi
+n01559477	hermit thrush, Hylocichla guttata
+n01559639	veery, Wilson's thrush, Hylocichla fuscescens
+n01559804	wood thrush, Hylocichla mustelina
+n01560105	nightingale, Luscinia megarhynchos
+n01560280	thrush nightingale, Luscinia luscinia
+n01560419	bulbul
+n01560636	Old World chat, chat
+n01560793	stonechat, Saxicola torquata
+n01560935	whinchat, Saxicola rubetra
+n01561181	solitaire
+n01561452	redstart, redtail
+n01561732	wheatear
+n01562014	bluebird
+n01562265	robin, redbreast, robin redbreast, Old World robin, Erithacus rubecola
+n01562451	bluethroat, Erithacus svecicus
+n01563128	warbler
+n01563449	gnatcatcher
+n01563746	kinglet
+n01563945	goldcrest, golden-crested kinglet, Regulus regulus
+n01564101	gold-crowned kinglet, Regulus satrata
+n01564217	ruby-crowned kinglet, ruby-crowned wren, Regulus calendula
+n01564394	Old World warbler, true warbler
+n01564773	blackcap, Silvia atricapilla
+n01564914	greater whitethroat, whitethroat, Sylvia communis
+n01565078	lesser whitethroat, whitethroat, Sylvia curruca
+n01565345	wood warbler, Phylloscopus sibilatrix
+n01565599	sedge warbler, sedge bird, sedge wren, reedbird, Acrocephalus schoenobaenus
+n01565930	wren warbler
+n01566207	tailorbird, Orthotomus sutorius
+n01566645	babbler, cackler
+n01567133	New World warbler, wood warbler
+n01567678	parula warbler, northern parula, Parula americana
+n01567879	Wilson's warbler, Wilson's blackcap, Wilsonia pusilla
+n01568132	flycatching warbler
+n01568294	American redstart, redstart, Setophaga ruticilla
+n01568720	Cape May warbler, Dendroica tigrina
+n01568892	yellow warbler, golden warbler, yellowbird, Dendroica petechia
+n01569060	Blackburn, Blackburnian warbler, Dendroica fusca
+n01569262	Audubon's warbler, Audubon warbler, Dendroica auduboni
+n01569423	myrtle warbler, myrtle bird, Dendroica coronata
+n01569566	blackpoll, Dendroica striate
+n01569836	New World chat, chat
+n01569971	yellow-breasted chat, Icteria virens
+n01570267	ovenbird, Seiurus aurocapillus
+n01570421	water thrush
+n01570676	yellowthroat
+n01570839	common yellowthroat, Maryland yellowthroat, Geothlypis trichas
+n01571410	riflebird, Ptloris paradisea
+n01571904	New World oriole, American oriole, oriole
+n01572328	northern oriole, Icterus galbula
+n01572489	Baltimore oriole, Baltimore bird, hangbird, firebird, Icterus galbula galbula
+n01572654	Bullock's oriole, Icterus galbula bullockii
+n01572782	orchard oriole, Icterus spurius
+n01573074	meadowlark, lark
+n01573240	eastern meadowlark, Sturnella magna
+n01573360	western meadowlark, Sturnella neglecta
+n01573627	cacique, cazique
+n01573898	bobolink, ricebird, reedbird, Dolichonyx oryzivorus
+n01574045	New World blackbird, blackbird
+n01574390	grackle, crow blackbird
+n01574560	purple grackle, Quiscalus quiscula
+n01574801	rusty blackbird, rusty grackle, Euphagus carilonus
+n01575117	cowbird
+n01575401	red-winged blackbird, redwing, Agelaius phoeniceus
+n01575745	Old World oriole, oriole
+n01576076	golden oriole, Oriolus oriolus
+n01576358	fig-bird
+n01576695	starling
+n01577035	common starling, Sturnus vulgaris
+n01577458	rose-colored starling, rose-colored pastor, Pastor sturnus, Pastor roseus
+n01577659	myna, mynah, mina, minah, myna bird, mynah bird
+n01577941	crested myna, Acridotheres tristis
+n01578180	hill myna, Indian grackle, grackle, Gracula religiosa
+n01578575	corvine bird
+n01579028	crow
+n01579149	American crow, Corvus brachyrhyncos
+n01579260	raven, Corvus corax
+n01579410	rook, Corvus frugilegus
+n01579578	jackdaw, daw, Corvus monedula
+n01579729	chough
+n01580077	jay
+n01580379	Old World jay
+n01580490	common European jay, Garullus garullus
+n01580772	New World jay
+n01580870	blue jay, jaybird, Cyanocitta cristata
+n01581166	Canada jay, grey jay, gray jay, camp robber, whisker jack, Perisoreus canadensis
+n01581434	Rocky Mountain jay, Perisoreus canadensis capitalis
+n01581730	nutcracker
+n01581874	common nutcracker, Nucifraga caryocatactes
+n01581984	Clark's nutcracker, Nucifraga columbiana
+n01582220	magpie
+n01582398	European magpie, Pica pica
+n01582498	American magpie, Pica pica hudsonia
+n01582856	Australian magpie
+n01583209	butcherbird
+n01583495	currawong, bell magpie
+n01583828	piping crow, piping crow-shrike, Gymnorhina tibicen
+n01584225	wren, jenny wren
+n01584695	winter wren, Troglodytes troglodytes
+n01584853	house wren, Troglodytes aedon
+n01585121	marsh wren
+n01585287	long-billed marsh wren, Cistothorus palustris
+n01585422	sedge wren, short-billed marsh wren, Cistothorus platensis
+n01585715	rock wren, Salpinctes obsoletus
+n01586020	Carolina wren, Thryothorus ludovicianus
+n01586374	cactus wren
+n01586941	mockingbird, mocker, Mimus polyglotktos
+n01587278	blue mockingbird, Melanotis caerulescens
+n01587526	catbird, grey catbird, gray catbird, Dumetella carolinensis
+n01587834	thrasher, mocking thrush
+n01588002	brown thrasher, brown thrush, Toxostoma rufums
+n01588431	New Zealand wren
+n01588725	rock wren, Xenicus gilviventris
+n01588996	rifleman bird, Acanthisitta chloris
+n01589286	creeper, tree creeper
+n01589718	brown creeper, American creeper, Certhia americana
+n01589893	European creeper, Certhia familiaris
+n01590220	wall creeper, tichodrome, Tichodroma muriaria
+n01591005	European nuthatch, Sitta europaea
+n01591123	red-breasted nuthatch, Sitta canadensis
+n01591301	white-breasted nuthatch, Sitta carolinensis
+n01591697	titmouse, tit
+n01592084	chickadee
+n01592257	black-capped chickadee, blackcap, Parus atricapillus
+n01592387	tufted titmouse, Parus bicolor
+n01592540	Carolina chickadee, Parus carolinensis
+n01592694	blue tit, tomtit, Parus caeruleus
+n01593028	bushtit, bush tit
+n01593282	wren-tit, Chamaea fasciata
+n01593553	verdin, Auriparus flaviceps
+n01594004	fairy bluebird, bluebird
+n01594372	swallow
+n01594787	barn swallow, chimney swallow, Hirundo rustica
+n01594968	cliff swallow, Hirundo pyrrhonota
+n01595168	tree swallow, tree martin, Hirundo nigricans
+n01595450	white-bellied swallow, tree swallow, Iridoprocne bicolor
+n01595624	martin
+n01595974	house martin, Delichon urbica
+n01596273	bank martin, bank swallow, sand martin, Riparia riparia
+n01596608	purple martin, Progne subis
+n01597022	wood swallow, swallow shrike
+n01597336	tanager
+n01597737	scarlet tanager, Piranga olivacea, redbird, firebird
+n01597906	western tanager, Piranga ludoviciana
+n01598074	summer tanager, summer redbird, Piranga rubra
+n01598271	hepatic tanager, Piranga flava hepatica
+n01598588	shrike
+n01598988	butcherbird
+n01599159	European shrike, Lanius excubitor
+n01599269	northern shrike, Lanius borealis
+n01599388	white-rumped shrike, Lanius ludovicianus excubitorides
+n01599556	loggerhead shrike, Lanius lucovicianus
+n01599741	migrant shrike, Lanius ludovicianus migrans
+n01600085	bush shrike
+n01600341	black-fronted bush shrike, Chlorophoneus nigrifrons
+n01600657	bowerbird, catbird
+n01601068	satin bowerbird, satin bird, Ptilonorhynchus violaceus
+n01601410	great bowerbird, Chlamydera nuchalis
+n01601694	water ouzel, dipper
+n01602080	European water ouzel, Cinclus aquaticus
+n01602209	American water ouzel, Cinclus mexicanus
+n01602630	vireo
+n01602832	red-eyed vireo, Vireo olivaceous
+n01603000	solitary vireo, Vireo solitarius
+n01603152	blue-headed vireo, Vireo solitarius solitarius
+n01603600	waxwing
+n01603812	cedar waxwing, cedarbird, Bombycilla cedrorun
+n01603953	Bohemian waxwing, Bombycilla garrulus
+n01604330	bird of prey, raptor, raptorial bird
+n01604968	Accipitriformes, order Accipitriformes
+n01605630	hawk
+n01606097	eyas
+n01606177	tiercel, tercel, tercelet
+n01606522	goshawk, Accipiter gentilis
+n01606672	sparrow hawk, Accipiter nisus
+n01606809	Cooper's hawk, blue darter, Accipiter cooperii
+n01606978	chicken hawk, hen hawk
+n01607309	buteonine
+n01607429	redtail, red-tailed hawk, Buteo jamaicensis
+n01607600	rough-legged hawk, roughleg, Buteo lagopus
+n01607812	red-shouldered hawk, Buteo lineatus
+n01607962	buzzard, Buteo buteo
+n01608265	honey buzzard, Pernis apivorus
+n01608432	kite
+n01608814	black kite, Milvus migrans
+n01609062	swallow-tailed kite, swallow-tailed hawk, Elanoides forficatus
+n01609391	white-tailed kite, Elanus leucurus
+n01609751	harrier
+n01609956	marsh harrier, Circus Aeruginosus
+n01610100	Montagu's harrier, Circus pygargus
+n01610226	marsh hawk, northern harrier, hen harrier, Circus cyaneus
+n01610552	harrier eagle, short-toed eagle
+n01610955	falcon
+n01611472	peregrine, peregrine falcon, Falco peregrinus
+n01611674	falcon-gentle, falcon-gentil
+n01611800	gyrfalcon, gerfalcon, Falco rusticolus
+n01611969	kestrel, Falco tinnunculus
+n01612122	sparrow hawk, American kestrel, kestrel, Falco sparverius
+n01612275	pigeon hawk, merlin, Falco columbarius
+n01612476	hobby, Falco subbuteo
+n01612628	caracara
+n01612955	Audubon's caracara, Polyborus cheriway audubonii
+n01613177	carancha, Polyborus plancus
+n01613294	eagle, bird of Jove
+n01613615	young bird
+n01613807	eaglet
+n01614038	harpy, harpy eagle, Harpia harpyja
+n01614343	golden eagle, Aquila chrysaetos
+n01614556	tawny eagle, Aquila rapax
+n01614925	bald eagle, American eagle, Haliaeetus leucocephalus
+n01615121	sea eagle
+n01615303	Kamchatkan sea eagle, Stellar's sea eagle, Haliaeetus pelagicus
+n01615458	ern, erne, grey sea eagle, gray sea eagle, European sea eagle, white-tailed sea eagle, Haliatus albicilla
+n01615703	fishing eagle, Haliaeetus leucorhyphus
+n01616086	osprey, fish hawk, fish eagle, sea eagle, Pandion haliaetus
+n01616318	vulture
+n01616551	Aegypiidae, family Aegypiidae
+n01616764	Old World vulture
+n01617095	griffon vulture, griffon, Gyps fulvus
+n01617443	bearded vulture, lammergeier, lammergeyer, Gypaetus barbatus
+n01617766	Egyptian vulture, Pharaoh's chicken, Neophron percnopterus
+n01618082	black vulture, Aegypius monachus
+n01618503	secretary bird, Sagittarius serpentarius
+n01618922	New World vulture, cathartid
+n01619310	buzzard, turkey buzzard, turkey vulture, Cathartes aura
+n01619536	condor
+n01619835	Andean condor, Vultur gryphus
+n01620135	California condor, Gymnogyps californianus
+n01620414	black vulture, carrion crow, Coragyps atratus
+n01620735	king vulture, Sarcorhamphus papa
+n01621127	owl, bird of Minerva, bird of night, hooter
+n01621635	owlet
+n01622120	little owl, Athene noctua
+n01622352	horned owl
+n01622483	great horned owl, Bubo virginianus
+n01622779	great grey owl, great gray owl, Strix nebulosa
+n01622959	tawny owl, Strix aluco
+n01623110	barred owl, Strix varia
+n01623425	screech owl, Otus asio
+n01623615	screech owl
+n01623706	scops owl
+n01623880	spotted owl, Strix occidentalis
+n01624115	Old World scops owl, Otus scops
+n01624212	Oriental scops owl, Otus sunia
+n01624305	hoot owl
+n01624537	hawk owl, Surnia ulula
+n01624833	long-eared owl, Asio otus
+n01625121	laughing owl, laughing jackass, Sceloglaux albifacies
+n01625562	barn owl, Tyto alba
+n01627424	amphibian
+n01628331	Ichyostega
+n01628770	urodele, caudate
+n01629276	salamander
+n01629819	European fire salamander, Salamandra salamandra
+n01629962	spotted salamander, fire salamander, Salamandra maculosa
+n01630148	alpine salamander, Salamandra atra
+n01630284	newt, triton
+n01630670	common newt, Triturus vulgaris
+n01630901	red eft, Notophthalmus viridescens
+n01631175	Pacific newt
+n01631354	rough-skinned newt, Taricha granulosa
+n01631512	California newt, Taricha torosa
+n01631663	eft
+n01632047	ambystomid, ambystomid salamander
+n01632308	mole salamander, Ambystoma talpoideum
+n01632458	spotted salamander, Ambystoma maculatum
+n01632601	tiger salamander, Ambystoma tigrinum
+n01632777	axolotl, mud puppy, Ambystoma mexicanum
+n01632952	waterdog
+n01633406	hellbender, mud puppy, Cryptobranchus alleganiensis
+n01633781	giant salamander, Megalobatrachus maximus
+n01634227	olm, Proteus anguinus
+n01634522	mud puppy, Necturus maculosus
+n01635027	dicamptodon, dicamptodontid
+n01635176	Pacific giant salamander, Dicamptodon ensatus
+n01635480	olympic salamander, Rhyacotriton olympicus
+n01636127	lungless salamander, plethodont
+n01636352	eastern red-backed salamander, Plethodon cinereus
+n01636510	western red-backed salamander, Plethodon vehiculum
+n01636829	dusky salamander
+n01637112	climbing salamander
+n01637338	arboreal salamander, Aneides lugubris
+n01637615	slender salamander, worm salamander
+n01637932	web-toed salamander
+n01638194	Shasta salamander, Hydromantes shastae
+n01638329	limestone salamander, Hydromantes brunus
+n01638722	amphiuma, congo snake, congo eel, blind eel
+n01639187	siren
+n01639765	frog, toad, toad frog, anuran, batrachian, salientian
+n01640846	true frog, ranid
+n01641206	wood-frog, wood frog, Rana sylvatica
+n01641391	leopard frog, spring frog, Rana pipiens
+n01641577	bullfrog, Rana catesbeiana
+n01641739	green frog, spring frog, Rana clamitans
+n01641930	cascades frog, Rana cascadae
+n01642097	goliath frog, Rana goliath
+n01642257	pickerel frog, Rana palustris
+n01642391	tarahumara frog, Rana tarahumarae
+n01642539	grass frog, Rana temporaria
+n01642943	leptodactylid frog, leptodactylid
+n01643255	robber frog
+n01643507	barking frog, robber frog, Hylactophryne augusti
+n01643896	crapaud, South American bullfrog, Leptodactylus pentadactylus
+n01644373	tree frog, tree-frog
+n01644900	tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui
+n01645466	Liopelma hamiltoni
+n01645776	true toad
+n01646292	bufo
+n01646388	agua, agua toad, Bufo marinus
+n01646555	European toad, Bufo bufo
+n01646648	natterjack, Bufo calamita
+n01646802	American toad, Bufo americanus
+n01646902	Eurasian green toad, Bufo viridis
+n01647033	American green toad, Bufo debilis
+n01647180	Yosemite toad, Bufo canorus
+n01647303	Texas toad, Bufo speciosus
+n01647466	southwestern toad, Bufo microscaphus
+n01647640	western toad, Bufo boreas
+n01648139	obstetrical toad, midwife toad, Alytes obstetricans
+n01648356	midwife toad, Alytes cisternasi
+n01648620	fire-bellied toad, Bombina bombina
+n01649170	spadefoot, spadefoot toad
+n01649412	western spadefoot, Scaphiopus hammondii
+n01649556	southern spadefoot, Scaphiopus multiplicatus
+n01649726	plains spadefoot, Scaphiopus bombifrons
+n01650167	tree toad, tree frog, tree-frog
+n01650690	spring peeper, Hyla crucifer
+n01650901	Pacific tree toad, Hyla regilla
+n01651059	canyon treefrog, Hyla arenicolor
+n01651285	chameleon tree frog
+n01651487	cricket frog
+n01651641	northern cricket frog, Acris crepitans
+n01651778	eastern cricket frog, Acris gryllus
+n01652026	chorus frog
+n01652297	lowland burrowing treefrog, northern casque-headed frog, Pternohyla fodiens
+n01653026	western narrow-mouthed toad, Gastrophryne olivacea
+n01653223	eastern narrow-mouthed toad, Gastrophryne carolinensis
+n01653509	sheep frog
+n01653773	tongueless frog
+n01654083	Surinam toad, Pipa pipa, Pipa americana
+n01654637	African clawed frog, Xenopus laevis
+n01654863	South American poison toad
+n01655344	caecilian, blindworm
+n01661091	reptile, reptilian
+n01661592	anapsid, anapsid reptile
+n01661818	diapsid, diapsid reptile
+n01662060	Diapsida, subclass Diapsida
+n01662622	chelonian, chelonian reptile
+n01662784	turtle
+n01663401	sea turtle, marine turtle
+n01663782	green turtle, Chelonia mydas
+n01664065	loggerhead, loggerhead turtle, Caretta caretta
+n01664369	ridley
+n01664492	Atlantic ridley, bastard ridley, bastard turtle, Lepidochelys kempii
+n01664674	Pacific ridley, olive ridley, Lepidochelys olivacea
+n01664990	hawksbill turtle, hawksbill, hawkbill, tortoiseshell turtle, Eretmochelys imbricata
+n01665541	leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea
+n01665932	snapping turtle
+n01666228	common snapping turtle, snapper, Chelydra serpentina
+n01666585	alligator snapping turtle, alligator snapper, Macroclemys temmincki
+n01667114	mud turtle
+n01667432	musk turtle, stinkpot
+n01667778	terrapin
+n01668091	diamondback terrapin, Malaclemys centrata
+n01668436	red-bellied terrapin, red-bellied turtle, redbelly, Pseudemys rubriventris
+n01668665	slider, yellow-bellied terrapin, Pseudemys scripta
+n01668892	cooter, river cooter, Pseudemys concinna
+n01669191	box turtle, box tortoise
+n01669372	Western box turtle, Terrapene ornata
+n01669654	painted turtle, painted terrapin, painted tortoise, Chrysemys picta
+n01670092	tortoise
+n01670535	European tortoise, Testudo graeca
+n01670802	giant tortoise
+n01671125	gopher tortoise, gopher turtle, gopher, Gopherus polypemus
+n01671479	desert tortoise, Gopherus agassizii
+n01671705	Texas tortoise
+n01672032	soft-shelled turtle, pancake turtle
+n01672432	spiny softshell, Trionyx spiniferus
+n01672611	smooth softshell, Trionyx muticus
+n01673282	tuatara, Sphenodon punctatum
+n01674216	saurian
+n01674464	lizard
+n01674990	gecko
+n01675352	flying gecko, fringed gecko, Ptychozoon homalocephalum
+n01675722	banded gecko
+n01676755	iguanid, iguanid lizard
+n01677366	common iguana, iguana, Iguana iguana
+n01677747	marine iguana, Amblyrhynchus cristatus
+n01678043	desert iguana, Dipsosaurus dorsalis
+n01678343	chuckwalla, Sauromalus obesus
+n01678657	zebra-tailed lizard, gridiron-tailed lizard, Callisaurus draconoides
+n01679005	fringe-toed lizard, Uma notata
+n01679307	earless lizard
+n01679626	collared lizard
+n01679962	leopard lizard
+n01680264	spiny lizard
+n01680478	fence lizard
+n01680655	western fence lizard, swift, blue-belly, Sceloporus occidentalis
+n01680813	eastern fence lizard, pine lizard, Sceloporus undulatus
+n01680983	sagebrush lizard, Sceloporus graciosus
+n01681328	side-blotched lizard, sand lizard, Uta stansburiana
+n01681653	tree lizard, Urosaurus ornatus
+n01681940	horned lizard, horned toad, horny frog
+n01682172	Texas horned lizard, Phrynosoma cornutum
+n01682435	basilisk
+n01682714	American chameleon, anole, Anolis carolinensis
+n01683201	worm lizard
+n01683558	night lizard
+n01684133	skink, scincid, scincid lizard
+n01684578	western skink, Eumeces skiltonianus
+n01684741	mountain skink, Eumeces callicephalus
+n01685439	teiid lizard, teiid
+n01685808	whiptail, whiptail lizard
+n01686044	racerunner, race runner, six-lined racerunner, Cnemidophorus sexlineatus
+n01686220	plateau striped whiptail, Cnemidophorus velox
+n01686403	Chihuahuan spotted whiptail, Cnemidophorus exsanguis
+n01686609	western whiptail, Cnemidophorus tigris
+n01686808	checkered whiptail, Cnemidophorus tesselatus
+n01687128	teju
+n01687290	caiman lizard
+n01687665	agamid, agamid lizard
+n01687978	agama
+n01688243	frilled lizard, Chlamydosaurus kingi
+n01688961	moloch
+n01689081	mountain devil, spiny lizard, Moloch horridus
+n01689411	anguid lizard
+n01689811	alligator lizard
+n01690149	blindworm, slowworm, Anguis fragilis
+n01690466	glass lizard, glass snake, joint snake
+n01691217	legless lizard
+n01691652	Lanthanotus borneensis
+n01691951	venomous lizard
+n01692333	Gila monster, Heloderma suspectum
+n01692523	beaded lizard, Mexican beaded lizard, Heloderma horridum
+n01692864	lacertid lizard, lacertid
+n01693175	sand lizard, Lacerta agilis
+n01693334	green lizard, Lacerta viridis
+n01693783	chameleon, chamaeleon
+n01694178	African chameleon, Chamaeleo chamaeleon
+n01694311	horned chameleon, Chamaeleo oweni
+n01694709	monitor, monitor lizard, varan
+n01694955	African monitor, Varanus niloticus
+n01695060	Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis
+n01696633	crocodilian reptile, crocodilian
+n01697178	crocodile
+n01697457	African crocodile, Nile crocodile, Crocodylus niloticus
+n01697611	Asian crocodile, Crocodylus porosus
+n01697749	Morlett's crocodile
+n01697978	false gavial, Tomistoma schlegeli
+n01698434	alligator, gator
+n01698640	American alligator, Alligator mississipiensis
+n01698782	Chinese alligator, Alligator sinensis
+n01699040	caiman, cayman
+n01699254	spectacled caiman, Caiman sclerops
+n01699675	gavial, Gavialis gangeticus
+n01701551	armored dinosaur
+n01701859	stegosaur, stegosaurus, Stegosaur stenops
+n01702256	ankylosaur, ankylosaurus
+n01702479	Edmontonia
+n01703011	bone-headed dinosaur
+n01703161	pachycephalosaur, pachycephalosaurus
+n01703569	ceratopsian, horned dinosaur
+n01704103	protoceratops
+n01704323	triceratops
+n01704626	styracosaur, styracosaurus
+n01705010	psittacosaur, psittacosaurus
+n01705591	ornithopod, ornithopod dinosaur
+n01705934	hadrosaur, hadrosaurus, duck-billed dinosaur
+n01707294	trachodon, trachodont
+n01708106	saurischian, saurischian dinosaur
+n01708998	sauropod, sauropod dinosaur
+n01709484	apatosaur, apatosaurus, brontosaur, brontosaurus, thunder lizard, Apatosaurus excelsus
+n01709876	barosaur, barosaurus
+n01710177	diplodocus
+n01711160	argentinosaur
+n01712008	theropod, theropod dinosaur, bird-footed dinosaur
+n01712752	ceratosaur, ceratosaurus
+n01713170	coelophysis
+n01713764	tyrannosaur, tyrannosaurus, Tyrannosaurus rex
+n01714231	allosaur, allosaurus
+n01715888	ornithomimid
+n01717016	maniraptor
+n01717229	oviraptorid
+n01717467	velociraptor
+n01718096	deinonychus
+n01718414	utahraptor, superslasher
+n01719403	synapsid, synapsid reptile
+n01721174	dicynodont
+n01721898	pelycosaur
+n01722670	dimetrodon
+n01722998	pterosaur, flying reptile
+n01723579	pterodactyl
+n01724231	ichthyosaur
+n01724840	ichthyosaurus
+n01725086	stenopterygius, Stenopterygius quadrisicissus
+n01725713	plesiosaur, plesiosaurus
+n01726203	nothosaur
+n01726692	snake, serpent, ophidian
+n01727646	colubrid snake, colubrid
+n01728266	hoop snake
+n01728572	thunder snake, worm snake, Carphophis amoenus
+n01728920	ringneck snake, ring-necked snake, ring snake
+n01729322	hognose snake, puff adder, sand viper
+n01729672	leaf-nosed snake
+n01729977	green snake, grass snake
+n01730185	smooth green snake, Opheodrys vernalis
+n01730307	rough green snake, Opheodrys aestivus
+n01730563	green snake
+n01730812	racer
+n01730960	blacksnake, black racer, Coluber constrictor
+n01731137	blue racer, Coluber constrictor flaviventris
+n01731277	horseshoe whipsnake, Coluber hippocrepis
+n01731545	whip-snake, whip snake, whipsnake
+n01731764	coachwhip, coachwhip snake, Masticophis flagellum
+n01731941	California whipsnake, striped racer, Masticophis lateralis
+n01732093	Sonoran whipsnake, Masticophis bilineatus
+n01732244	rat snake
+n01732614	corn snake, red rat snake, Elaphe guttata
+n01732789	black rat snake, blacksnake, pilot blacksnake, mountain blacksnake, Elaphe obsoleta
+n01732989	chicken snake
+n01733214	Indian rat snake, Ptyas mucosus
+n01733466	glossy snake, Arizona elegans
+n01733757	bull snake, bull-snake
+n01733957	gopher snake, Pituophis melanoleucus
+n01734104	pine snake
+n01734418	king snake, kingsnake
+n01734637	common kingsnake, Lampropeltis getulus
+n01734808	milk snake, house snake, milk adder, checkered adder, Lampropeltis triangulum
+n01735189	garter snake, grass snake
+n01735439	common garter snake, Thamnophis sirtalis
+n01735577	ribbon snake, Thamnophis sauritus
+n01735728	Western ribbon snake, Thamnophis proximus
+n01736032	lined snake, Tropidoclonion lineatum
+n01736375	ground snake, Sonora semiannulata
+n01736796	eastern ground snake, Potamophis striatula, Haldea striatula
+n01737021	water snake
+n01737472	common water snake, banded water snake, Natrix sipedon, Nerodia sipedon
+n01737728	water moccasin
+n01737875	grass snake, ring snake, ringed snake, Natrix natrix
+n01738065	viperine grass snake, Natrix maura
+n01738306	red-bellied snake, Storeria occipitamaculata
+n01738601	sand snake
+n01738731	banded sand snake, Chilomeniscus cinctus
+n01739094	black-headed snake
+n01739381	vine snake
+n01739647	lyre snake
+n01739871	Sonoran lyre snake, Trimorphodon lambda
+n01740131	night snake, Hypsiglena torquata
+n01740551	blind snake, worm snake
+n01740885	western blind snake, Leptotyphlops humilis
+n01741232	indigo snake, gopher snake, Drymarchon corais
+n01741442	eastern indigo snake, Drymarchon corais couperi
+n01741562	constrictor
+n01741943	boa
+n01742172	boa constrictor, Constrictor constrictor
+n01742447	rubber boa, tow-headed snake, Charina bottae
+n01742821	rosy boa, Lichanura trivirgata
+n01743086	anaconda, Eunectes murinus
+n01743605	python
+n01743936	carpet snake, Python variegatus, Morelia spilotes variegatus
+n01744100	reticulated python, Python reticulatus
+n01744270	Indian python, Python molurus
+n01744401	rock python, rock snake, Python sebae
+n01744555	amethystine python
+n01745125	elapid, elapid snake
+n01745484	coral snake, harlequin-snake, New World coral snake
+n01745902	eastern coral snake, Micrurus fulvius
+n01746191	western coral snake, Micruroides euryxanthus
+n01746359	coral snake, Old World coral snake
+n01746952	African coral snake, Aspidelaps lubricus
+n01747285	Australian coral snake, Rhynchoelaps australis
+n01747589	copperhead, Denisonia superba
+n01747885	cobra
+n01748264	Indian cobra, Naja naja
+n01748389	asp, Egyptian cobra, Naja haje
+n01748686	black-necked cobra, spitting cobra, Naja nigricollis
+n01748906	hamadryad, king cobra, Ophiophagus hannah, Naja hannah
+n01749244	ringhals, rinkhals, spitting snake, Hemachatus haemachatus
+n01749582	mamba
+n01749742	black mamba, Dendroaspis augusticeps
+n01749939	green mamba
+n01750167	death adder, Acanthophis antarcticus
+n01750437	tiger snake, Notechis scutatus
+n01750743	Australian blacksnake, Pseudechis porphyriacus
+n01751036	krait
+n01751215	banded krait, banded adder, Bungarus fasciatus
+n01751472	taipan, Oxyuranus scutellatus
+n01751748	sea snake
+n01752165	viper
+n01752585	adder, common viper, Vipera berus
+n01752736	asp, asp viper, Vipera aspis
+n01753032	puff adder, Bitis arietans
+n01753180	gaboon viper, Bitis gabonica
+n01753488	horned viper, cerastes, sand viper, horned asp, Cerastes cornutus
+n01753959	pit viper
+n01754370	copperhead, Agkistrodon contortrix
+n01754533	water moccasin, cottonmouth, cottonmouth moccasin, Agkistrodon piscivorus
+n01754876	rattlesnake, rattler
+n01755581	diamondback, diamondback rattlesnake, Crotalus adamanteus
+n01755740	timber rattlesnake, banded rattlesnake, Crotalus horridus horridus
+n01755952	canebrake rattlesnake, canebrake rattler, Crotalus horridus atricaudatus
+n01756089	prairie rattlesnake, prairie rattler, Western rattlesnake, Crotalus viridis
+n01756291	sidewinder, horned rattlesnake, Crotalus cerastes
+n01756508	Western diamondback, Western diamondback rattlesnake, Crotalus atrox
+n01756733	rock rattlesnake, Crotalus lepidus
+n01756916	tiger rattlesnake, Crotalus tigris
+n01757115	Mojave rattlesnake, Crotalus scutulatus
+n01757343	speckled rattlesnake, Crotalus mitchellii
+n01757677	massasauga, massasauga rattler, Sistrurus catenatus
+n01757901	ground rattler, massasauga, Sistrurus miliaris
+n01758141	fer-de-lance, Bothrops atrops
+n01758757	carcase, carcass
+n01758895	carrion
+n01767661	arthropod
+n01768244	trilobite
+n01769347	arachnid, arachnoid
+n01770081	harvestman, daddy longlegs, Phalangium opilio
+n01770393	scorpion
+n01770795	false scorpion, pseudoscorpion
+n01771100	book scorpion, Chelifer cancroides
+n01771417	whip-scorpion, whip scorpion
+n01771766	vinegarroon, Mastigoproctus giganteus
+n01772222	spider
+n01772664	orb-weaving spider
+n01773157	black and gold garden spider, Argiope aurantia
+n01773549	barn spider, Araneus cavaticus
+n01773797	garden spider, Aranea diademata
+n01774097	comb-footed spider, theridiid
+n01774384	black widow, Latrodectus mactans
+n01774750	tarantula
+n01775062	wolf spider, hunting spider
+n01775370	European wolf spider, tarantula, Lycosa tarentula
+n01775730	trap-door spider
+n01776192	acarine
+n01776313	tick
+n01776705	hard tick, ixodid
+n01777304	Ixodes dammini, deer tick
+n01777467	Ixodes neotomae
+n01777649	Ixodes pacificus, western black-legged tick
+n01777909	Ixodes scapularis, black-legged tick
+n01778217	sheep-tick, sheep tick, Ixodes ricinus
+n01778487	Ixodes persulcatus
+n01778621	Ixodes dentatus
+n01778801	Ixodes spinipalpis
+n01779148	wood tick, American dog tick, Dermacentor variabilis
+n01779463	soft tick, argasid
+n01779629	mite
+n01779939	web-spinning mite
+n01780142	acarid
+n01780426	trombidiid
+n01780696	trombiculid
+n01781071	harvest mite, chigger, jigger, redbug
+n01781570	acarus, genus Acarus
+n01781698	itch mite, sarcoptid
+n01781875	rust mite
+n01782209	spider mite, tetranychid
+n01782516	red spider, red spider mite, Panonychus ulmi
+n01783017	myriapod
+n01783706	garden centipede, garden symphilid, symphilid, Scutigerella immaculata
+n01784293	tardigrade
+n01784675	centipede
+n01785667	house centipede, Scutigera coleoptrata
+n01786646	millipede, millepede, milliped
+n01787006	sea spider, pycnogonid
+n01787191	Merostomata, class Merostomata
+n01787835	horseshoe crab, king crab, Limulus polyphemus, Xiphosurus polyphemus
+n01788291	Asian horseshoe crab
+n01788579	eurypterid
+n01788864	tongue worm, pentastomid
+n01789386	gallinaceous bird, gallinacean
+n01789740	domestic fowl, fowl, poultry
+n01790171	Dorking
+n01790304	Plymouth Rock
+n01790398	Cornish, Cornish fowl
+n01790557	Rock Cornish
+n01790711	game fowl
+n01790812	cochin, cochin china
+n01791107	jungle fowl, gallina
+n01791314	jungle cock
+n01791388	jungle hen
+n01791463	red jungle fowl, Gallus gallus
+n01791625	chicken, Gallus gallus
+n01791954	bantam
+n01792042	chick, biddy
+n01792158	cock, rooster
+n01792429	cockerel
+n01792530	capon
+n01792640	hen, biddy
+n01792808	cackler
+n01792955	brood hen, broody, broody hen, setting hen, sitter
+n01793085	mother hen
+n01793159	layer
+n01793249	pullet
+n01793340	spring chicken
+n01793435	Rhode Island red
+n01793565	Dominique, Dominick
+n01793715	Orpington
+n01794158	turkey, Meleagris gallopavo
+n01794344	turkey cock, gobbler, tom, tom turkey
+n01794651	ocellated turkey, Agriocharis ocellata
+n01795088	grouse
+n01795545	black grouse
+n01795735	European black grouse, heathfowl, Lyrurus tetrix
+n01795900	Asian black grouse, Lyrurus mlokosiewiczi
+n01796019	blackcock, black cock
+n01796105	greyhen, grayhen, grey hen, gray hen, heath hen
+n01796340	ptarmigan
+n01796519	red grouse, moorfowl, moorbird, moor-bird, moorgame, Lagopus scoticus
+n01796729	moorhen
+n01797020	capercaillie, capercailzie, horse of the wood, Tetrao urogallus
+n01797307	spruce grouse, Canachites canadensis
+n01797601	sage grouse, sage hen, Centrocercus urophasianus
+n01797886	ruffed grouse, partridge, Bonasa umbellus
+n01798168	sharp-tailed grouse, sprigtail, sprig tail, Pedioecetes phasianellus
+n01798484	prairie chicken, prairie grouse, prairie fowl
+n01798706	greater prairie chicken, Tympanuchus cupido
+n01798839	lesser prairie chicken, Tympanuchus pallidicinctus
+n01798979	heath hen, Tympanuchus cupido cupido
+n01799302	guan
+n01799679	curassow
+n01800195	piping guan
+n01800424	chachalaca
+n01800633	Texas chachalaca, Ortilis vetula macalli
+n01801088	megapode, mound bird, mound-bird, mound builder, scrub fowl
+n01801479	mallee fowl, leipoa, lowan, Leipoa ocellata
+n01801672	mallee hen
+n01801876	brush turkey, Alectura lathami
+n01802159	maleo, Macrocephalon maleo
+n01802721	phasianid
+n01803078	pheasant
+n01803362	ring-necked pheasant, Phasianus colchicus
+n01803641	afropavo, Congo peafowl, Afropavo congensis
+n01803893	argus, argus pheasant
+n01804163	golden pheasant, Chrysolophus pictus
+n01804478	bobwhite, bobwhite quail, partridge
+n01804653	northern bobwhite, Colinus virginianus
+n01804921	Old World quail
+n01805070	migratory quail, Coturnix coturnix, Coturnix communis
+n01805321	monal, monaul
+n01805801	peafowl, bird of Juno
+n01806061	peachick, pea-chick
+n01806143	peacock
+n01806297	peahen
+n01806364	blue peafowl, Pavo cristatus
+n01806467	green peafowl, Pavo muticus
+n01806567	quail
+n01806847	California quail, Lofortyx californicus
+n01807105	tragopan
+n01807496	partridge
+n01807828	Hungarian partridge, grey partridge, gray partridge, Perdix perdix
+n01808140	red-legged partridge, Alectoris ruffa
+n01808291	Greek partridge, rock partridge, Alectoris graeca
+n01808596	mountain quail, mountain partridge, Oreortyx picta palmeri
+n01809106	guinea fowl, guinea, Numida meleagris
+n01809371	guinea hen
+n01809752	hoatzin, hoactzin, stinkbird, Opisthocomus hoazin
+n01810268	tinamou, partridge
+n01810700	columbiform bird
+n01811243	dodo, Raphus cucullatus
+n01811909	pigeon
+n01812187	pouter pigeon, pouter
+n01812337	dove
+n01812662	rock dove, rock pigeon, Columba livia
+n01812866	band-tailed pigeon, band-tail pigeon, bandtail, Columba fasciata
+n01813088	wood pigeon, ringdove, cushat, Columba palumbus
+n01813385	turtledove
+n01813532	Streptopelia turtur
+n01813658	ringdove, Streptopelia risoria
+n01813948	Australian turtledove, turtledove, Stictopelia cuneata
+n01814217	mourning dove, Zenaidura macroura
+n01814370	domestic pigeon
+n01814549	squab
+n01814620	fairy swallow
+n01814755	roller, tumbler, tumbler pigeon
+n01814921	homing pigeon, homer
+n01815036	carrier pigeon
+n01815270	passenger pigeon, Ectopistes migratorius
+n01815601	sandgrouse, sand grouse
+n01816017	painted sandgrouse, Pterocles indicus
+n01816140	pin-tailed sandgrouse, pin-tailed grouse, Pterocles alchata
+n01816474	pallas's sandgrouse, Syrrhaptes paradoxus
+n01816887	parrot
+n01817263	popinjay
+n01817346	poll, poll parrot
+n01817953	African grey, African gray, Psittacus erithacus
+n01818299	amazon
+n01818515	macaw
+n01818832	kea, Nestor notabilis
+n01819115	cockatoo
+n01819313	sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita
+n01819465	pink cockatoo, Kakatoe leadbeateri
+n01819734	cockateel, cockatiel, cockatoo parrot, Nymphicus hollandicus
+n01820052	lovebird
+n01820348	lory
+n01820546	lorikeet
+n01820801	varied Lorikeet, Glossopsitta versicolor
+n01821076	rainbow lorikeet, Trichoglossus moluccanus
+n01821203	parakeet, parrakeet, parroket, paraquet, paroquet, parroquet
+n01821554	Carolina parakeet, Conuropsis carolinensis
+n01821869	budgerigar, budgereegah, budgerygah, budgie, grass parakeet, lovebird, shell parakeet, Melopsittacus undulatus
+n01822300	ring-necked parakeet, Psittacula krameri
+n01822602	cuculiform bird
+n01823013	cuckoo
+n01823414	European cuckoo, Cuculus canorus
+n01823740	black-billed cuckoo, Coccyzus erythropthalmus
+n01824035	roadrunner, chaparral cock, Geococcyx californianus
+n01824344	ani
+n01824575	coucal
+n01824749	crow pheasant, Centropus sinensis
+n01825278	touraco, turaco, turacou, turakoo
+n01825930	coraciiform bird
+n01826364	roller
+n01826680	European roller, Coracias garrulus
+n01826844	ground roller
+n01827403	kingfisher
+n01827793	Eurasian kingfisher, Alcedo atthis
+n01828096	belted kingfisher, Ceryle alcyon
+n01828556	kookaburra, laughing jackass, Dacelo gigas
+n01828970	bee eater
+n01829413	hornbill
+n01829869	hoopoe, hoopoo
+n01830042	Euopean hoopoe, Upupa epops
+n01830479	wood hoopoe
+n01830915	motmot, momot
+n01831360	tody
+n01831712	apodiform bird
+n01832167	swift
+n01832493	European swift, Apus apus
+n01832813	chimney swift, chimney swallow, Chateura pelagica
+n01833112	swiftlet, Collocalia inexpectata
+n01833415	tree swift, crested swift
+n01833805	hummingbird
+n01834177	Archilochus colubris
+n01834540	thornbill
+n01835276	goatsucker, nightjar, caprimulgid
+n01835769	European goatsucker, European nightjar, Caprimulgus europaeus
+n01835918	chuck-will's-widow, Caprimulgus carolinensis
+n01836087	whippoorwill, Caprimulgus vociferus
+n01836673	poorwill, Phalaenoptilus nuttallii
+n01837072	frogmouth
+n01837526	oilbird, guacharo, Steatornis caripensis
+n01838038	piciform bird
+n01838598	woodpecker, peckerwood, pecker
+n01839086	green woodpecker, Picus viridis
+n01839330	downy woodpecker
+n01839598	flicker
+n01839750	yellow-shafted flicker, Colaptes auratus, yellowhammer
+n01839949	gilded flicker, Colaptes chrysoides
+n01840120	red-shafted flicker, Colaptes caper collaris
+n01840412	ivorybill, ivory-billed woodpecker, Campephilus principalis
+n01840775	redheaded woodpecker, redhead, Melanerpes erythrocephalus
+n01841102	sapsucker
+n01841288	yellow-bellied sapsucker, Sphyrapicus varius
+n01841441	red-breasted sapsucker, Sphyrapicus varius ruber
+n01841679	wryneck
+n01841943	piculet
+n01842235	barbet
+n01842504	puffbird
+n01842788	honey guide
+n01843065	jacamar
+n01843383	toucan
+n01843719	toucanet
+n01844231	trogon
+n01844551	quetzal, quetzal bird
+n01844746	resplendent quetzel, resplendent trogon, Pharomacrus mocino
+n01844917	aquatic bird
+n01845132	waterfowl, water bird, waterbird
+n01845477	anseriform bird
+n01846331	duck
+n01847000	drake
+n01847089	quack-quack
+n01847170	duckling
+n01847253	diving duck
+n01847407	dabbling duck, dabbler
+n01847806	mallard, Anas platyrhynchos
+n01847978	black duck, Anas rubripes
+n01848123	teal
+n01848323	greenwing, green-winged teal, Anas crecca
+n01848453	bluewing, blue-winged teal, Anas discors
+n01848555	garganey, Anas querquedula
+n01848648	widgeon, wigeon, Anas penelope
+n01848840	American widgeon, baldpate, Anas americana
+n01848976	shoveler, shoveller, broadbill, Anas clypeata
+n01849157	pintail, pin-tailed duck, Anas acuta
+n01849466	sheldrake
+n01849676	shelduck
+n01849863	ruddy duck, Oxyura jamaicensis
+n01850192	bufflehead, butterball, dipper, Bucephela albeola
+n01850373	goldeneye, whistler, Bucephela clangula
+n01850553	Barrow's goldeneye, Bucephala islandica
+n01850873	canvasback, canvasback duck, Aythya valisineria
+n01851038	pochard, Aythya ferina
+n01851207	redhead, Aythya americana
+n01851375	scaup, scaup duck, bluebill, broadbill
+n01851573	greater scaup, Aythya marila
+n01851731	lesser scaup, lesser scaup duck, lake duck, Aythya affinis
+n01851895	wild duck
+n01852142	wood duck, summer duck, wood widgeon, Aix sponsa
+n01852329	wood drake
+n01852400	mandarin duck, Aix galericulata
+n01852671	muscovy duck, musk duck, Cairina moschata
+n01852861	sea duck
+n01853195	eider, eider duck
+n01853498	scoter, scooter
+n01853666	common scoter, Melanitta nigra
+n01853870	old squaw, oldwife, Clangula hyemalis
+n01854415	merganser, fish duck, sawbill, sheldrake
+n01854700	goosander, Mergus merganser
+n01854838	American merganser, Mergus merganser americanus
+n01855032	red-breasted merganser, Mergus serrator
+n01855188	smew, Mergus albellus
+n01855476	hooded merganser, hooded sheldrake, Lophodytes cucullatus
+n01855672	goose
+n01856072	gosling
+n01856155	gander
+n01856380	Chinese goose, Anser cygnoides
+n01856553	greylag, graylag, greylag goose, graylag goose, Anser anser
+n01856890	blue goose, Chen caerulescens
+n01857079	snow goose
+n01857325	brant, brant goose, brent, brent goose
+n01857512	common brant goose, Branta bernicla
+n01857632	honker, Canada goose, Canadian goose, Branta canadensis
+n01857851	barnacle goose, barnacle, Branta leucopsis
+n01858281	coscoroba
+n01858441	swan
+n01858780	cob
+n01858845	pen
+n01858906	cygnet
+n01859190	mute swan, Cygnus olor
+n01859325	whooper, whooper swan, Cygnus cygnus
+n01859496	tundra swan, Cygnus columbianus
+n01859689	whistling swan, Cygnus columbianus columbianus
+n01859852	Bewick's swan, Cygnus columbianus bewickii
+n01860002	trumpeter, trumpeter swan, Cygnus buccinator
+n01860187	black swan, Cygnus atratus
+n01860497	screamer
+n01860864	horned screamer, Anhima cornuta
+n01861148	crested screamer
+n01861330	chaja, Chauna torquata
+n01861778	mammal, mammalian
+n01862399	female mammal
+n01871265	tusker
+n01871543	prototherian
+n01871875	monotreme, egg-laying mammal
+n01872401	echidna, spiny anteater, anteater
+n01872772	echidna, spiny anteater, anteater
+n01873310	platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus
+n01874434	marsupial, pouched mammal
+n01874928	opossum, possum
+n01875313	common opossum, Didelphis virginiana, Didelphis marsupialis
+n01875610	crab-eating opossum
+n01876034	opossum rat
+n01876326	bandicoot
+n01876667	rabbit-eared bandicoot, rabbit bandicoot, bilby, Macrotis lagotis
+n01877134	kangaroo
+n01877606	giant kangaroo, great grey kangaroo, Macropus giganteus
+n01877812	wallaby, brush kangaroo
+n01878061	common wallaby, Macropus agiles
+n01878335	hare wallaby, kangaroo hare
+n01878639	nail-tailed wallaby, nail-tailed kangaroo
+n01878929	rock wallaby, rock kangaroo
+n01879217	pademelon, paddymelon
+n01879509	tree wallaby, tree kangaroo
+n01879837	musk kangaroo, Hypsiprymnodon moschatus
+n01880152	rat kangaroo, kangaroo rat
+n01880473	potoroo
+n01880716	bettong
+n01880813	jerboa kangaroo, kangaroo jerboa
+n01881171	phalanger, opossum, possum
+n01881564	cuscus
+n01881857	brush-tailed phalanger, Trichosurus vulpecula
+n01882125	flying phalanger, flying opossum, flying squirrel
+n01882714	koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus
+n01883070	wombat
+n01883513	dasyurid marsupial, dasyurid
+n01883920	dasyure
+n01884104	eastern dasyure, Dasyurus quoll
+n01884203	native cat, Dasyurus viverrinus
+n01884476	thylacine, Tasmanian wolf, Tasmanian tiger, Thylacinus cynocephalus
+n01884834	Tasmanian devil, ursine dasyure, Sarcophilus hariisi
+n01885158	pouched mouse, marsupial mouse, marsupial rat
+n01885498	numbat, banded anteater, anteater, Myrmecobius fasciatus
+n01886045	pouched mole, marsupial mole, Notoryctus typhlops
+n01886756	placental, placental mammal, eutherian, eutherian mammal
+n01887474	livestock, stock, farm animal
+n01887623	bull
+n01887787	cow
+n01887896	calf
+n01888045	calf
+n01888181	yearling
+n01888264	buck
+n01888411	doe
+n01889074	insectivore
+n01889520	mole
+n01889849	starnose mole, star-nosed mole, Condylura cristata
+n01890144	brewer's mole, hair-tailed mole, Parascalops breweri
+n01890564	golden mole
+n01890860	shrew mole
+n01891013	Asiatic shrew mole, Uropsilus soricipes
+n01891274	American shrew mole, Neurotrichus gibbsii
+n01891633	shrew, shrewmouse
+n01892030	common shrew, Sorex araneus
+n01892145	masked shrew, Sorex cinereus
+n01892385	short-tailed shrew, Blarina brevicauda
+n01892551	water shrew
+n01892744	American water shrew, Sorex palustris
+n01893021	European water shrew, Neomys fodiens
+n01893164	Mediterranean water shrew, Neomys anomalus
+n01893399	least shrew, Cryptotis parva
+n01893825	hedgehog, Erinaceus europaeus, Erinaceus europeaeus
+n01894207	tenrec, tendrac
+n01894522	tailless tenrec, Tenrec ecaudatus
+n01894956	otter shrew, potamogale, Potamogale velox
+n01896844	eiderdown
+n01897257	aftershaft
+n01897426	sickle feather
+n01897536	contour feather
+n01897667	bastard wing, alula, spurious wing
+n01898593	saddle hackle, saddle feather
+n01899894	encolure
+n01900150	hair
+n01903234	squama
+n01903346	scute
+n01903498	sclerite
+n01904029	plastron
+n01904806	scallop shell
+n01904886	oyster shell
+n01905321	theca
+n01905661	invertebrate
+n01906749	sponge, poriferan, parazoan
+n01907287	choanocyte, collar cell
+n01907738	glass sponge
+n01908042	Venus's flower basket
+n01908958	metazoan
+n01909422	coelenterate, cnidarian
+n01909788	planula
+n01909906	polyp
+n01910252	medusa, medusoid, medusan
+n01910747	jellyfish
+n01911063	scyphozoan
+n01911403	Chrysaora quinquecirrha
+n01911839	hydrozoan, hydroid
+n01912152	hydra
+n01912454	siphonophore
+n01912809	nanomia
+n01913166	Portuguese man-of-war, man-of-war, jellyfish
+n01913346	praya
+n01913440	apolemia
+n01914163	anthozoan, actinozoan
+n01914609	sea anemone, anemone
+n01914830	actinia, actinian, actiniarian
+n01915700	sea pen
+n01915811	coral
+n01916187	gorgonian, gorgonian coral
+n01916388	sea feather
+n01916481	sea fan
+n01916588	red coral
+n01916925	stony coral, madrepore, madriporian coral
+n01917289	brain coral
+n01917611	staghorn coral, stag's-horn coral
+n01917882	mushroom coral
+n01918744	ctenophore, comb jelly
+n01919385	beroe
+n01920051	platyctenean
+n01920438	sea gooseberry
+n01921059	Venus's girdle, Cestum veneris
+n01922303	worm
+n01922717	helminth, parasitic worm
+n01922948	woodworm
+n01923025	woodborer, borer
+n01923404	acanthocephalan, spiny-headed worm
+n01923890	arrowworm, chaetognath
+n01924800	bladder worm
+n01924916	flatworm, platyhelminth
+n01925270	planarian, planaria
+n01925695	fluke, trematode, trematode worm
+n01925916	cercaria
+n01926379	liver fluke, Fasciola hepatica
+n01926689	Fasciolopsis buski
+n01927159	schistosome, blood fluke
+n01927456	tapeworm, cestode
+n01927928	echinococcus
+n01928215	taenia
+n01928517	ribbon worm, nemertean, nemertine, proboscis worm
+n01928865	beard worm, pogonophoran
+n01929186	rotifer
+n01930112	nematode, nematode worm, roundworm
+n01930852	common roundworm, Ascaris lumbricoides
+n01931140	chicken roundworm, Ascaridia galli
+n01931520	pinworm, threadworm, Enterobius vermicularis
+n01931714	eelworm
+n01932151	vinegar eel, vinegar worm, Anguillula aceti, Turbatrix aceti
+n01932936	trichina, Trichinella spiralis
+n01933151	hookworm
+n01933478	filaria
+n01933988	Guinea worm, Dracunculus medinensis
+n01934440	annelid, annelid worm, segmented worm
+n01934844	archiannelid
+n01935176	oligochaete, oligochaete worm
+n01935395	earthworm, angleworm, fishworm, fishing worm, wiggler, nightwalker, nightcrawler, crawler, dew worm, red worm
+n01936391	polychaete, polychete, polychaete worm, polychete worm
+n01936671	lugworm, lug, lobworm
+n01936858	sea mouse
+n01937579	bloodworm
+n01937909	leech, bloodsucker, hirudinean
+n01938454	medicinal leech, Hirudo medicinalis
+n01938735	horseleech
+n01940736	mollusk, mollusc, shellfish
+n01941223	scaphopod
+n01941340	tooth shell, tusk shell
+n01942177	gastropod, univalve
+n01942869	abalone, ear-shell
+n01943087	ormer, sea-ear, Haliotis tuberculata
+n01943541	scorpion shell
+n01943899	conch
+n01944118	giant conch, Strombus gigas
+n01944390	snail
+n01944812	edible snail, Helix pomatia
+n01944955	garden snail
+n01945143	brown snail, Helix aspersa
+n01945340	Helix hortensis
+n01945685	slug
+n01945845	seasnail
+n01946277	neritid, neritid gastropod
+n01946630	nerita
+n01946827	bleeding tooth, Nerita peloronta
+n01947139	neritina
+n01947396	whelk
+n01947997	moon shell, moonshell
+n01948446	periwinkle, winkle
+n01948573	limpet
+n01949085	common limpet, Patella vulgata
+n01949499	keyhole limpet, Fissurella apertura, Diodora apertura
+n01949973	river limpet, freshwater limpet, Ancylus fluviatilis
+n01950731	sea slug, nudibranch
+n01951274	sea hare, Aplysia punctata
+n01951613	Hermissenda crassicornis
+n01952029	bubble shell
+n01952712	physa
+n01953361	cowrie, cowry
+n01953594	money cowrie, Cypraea moneta
+n01953762	tiger cowrie, Cypraea tigris
+n01954516	solenogaster, aplacophoran
+n01955084	chiton, coat-of-mail shell, sea cradle, polyplacophore
+n01955933	bivalve, pelecypod, lamellibranch
+n01956344	spat
+n01956481	clam
+n01956764	seashell
+n01957335	soft-shell clam, steamer, steamer clam, long-neck clam, Mya arenaria
+n01958038	quahog, quahaug, hard-shell clam, hard clam, round clam, Venus mercenaria, Mercenaria mercenaria
+n01958346	littleneck, littleneck clam
+n01958435	cherrystone, cherrystone clam
+n01958531	geoduck
+n01959029	razor clam, jackknife clam, knife-handle
+n01959492	giant clam, Tridacna gigas
+n01959985	cockle
+n01960177	edible cockle, Cardium edule
+n01960459	oyster
+n01961234	Japanese oyster, Ostrea gigas
+n01961600	Virginia oyster
+n01961985	pearl oyster, Pinctada margaritifera
+n01962506	saddle oyster, Anomia ephippium
+n01962788	window oyster, windowpane oyster, capiz, Placuna placenta
+n01963317	ark shell
+n01963479	blood clam
+n01963571	mussel
+n01964049	marine mussel, mytilid
+n01964271	edible mussel, Mytilus edulis
+n01964441	freshwater mussel, freshwater clam
+n01964957	pearly-shelled mussel
+n01965252	thin-shelled mussel
+n01965529	zebra mussel, Dreissena polymorpha
+n01965889	scallop, scollop, escallop
+n01966377	bay scallop, Pecten irradians
+n01966586	sea scallop, giant scallop, Pecten magellanicus
+n01967094	shipworm, teredinid
+n01967308	teredo
+n01967963	piddock
+n01968315	cephalopod, cephalopod mollusk
+n01968897	chambered nautilus, pearly nautilus, nautilus
+n01969726	octopod
+n01970164	octopus, devilfish
+n01970667	paper nautilus, nautilus, Argonaut, Argonauta argo
+n01971094	decapod
+n01971280	squid
+n01971620	loligo
+n01971850	ommastrephes
+n01972131	architeuthis, giant squid
+n01972541	cuttlefish, cuttle
+n01973148	spirula, Spirula peronii
+n01974773	crustacean
+n01975687	malacostracan crustacean
+n01976146	decapod crustacean, decapod
+n01976868	brachyuran
+n01976957	crab
+n01977485	stone crab, Menippe mercenaria
+n01978010	hard-shell crab
+n01978136	soft-shell crab, soft-shelled crab
+n01978287	Dungeness crab, Cancer magister
+n01978455	rock crab, Cancer irroratus
+n01978587	Jonah crab, Cancer borealis
+n01978930	swimming crab
+n01979269	English lady crab, Portunus puber
+n01979526	American lady crab, lady crab, calico crab, Ovalipes ocellatus
+n01979874	blue crab, Callinectes sapidus
+n01980166	fiddler crab
+n01980655	pea crab
+n01981276	king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica
+n01981702	spider crab
+n01982068	European spider crab, king crab, Maja squinado
+n01982347	giant crab, Macrocheira kaempferi
+n01982650	lobster
+n01983048	true lobster
+n01983481	American lobster, Northern lobster, Maine lobster, Homarus americanus
+n01983674	European lobster, Homarus vulgaris
+n01983829	Cape lobster, Homarus capensis
+n01984245	Norway lobster, Nephrops norvegicus
+n01984695	spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish
+n01985128	crayfish, crawfish, crawdad, crawdaddy
+n01985493	Old World crayfish, ecrevisse
+n01985797	American crayfish
+n01986214	hermit crab
+n01986806	shrimp
+n01987076	snapping shrimp, pistol shrimp
+n01987545	prawn
+n01987727	long-clawed prawn, river prawn, Palaemon australis
+n01988203	tropical prawn
+n01988701	krill
+n01988869	Euphausia pacifica
+n01989516	opossum shrimp
+n01989869	stomatopod, stomatopod crustacean
+n01990007	mantis shrimp, mantis crab
+n01990516	squilla, mantis prawn
+n01990800	isopod
+n01991028	woodlouse, slater
+n01991520	pill bug
+n01992262	sow bug
+n01992423	sea louse, sea slater
+n01992773	amphipod
+n01993525	skeleton shrimp
+n01993830	whale louse
+n01994910	daphnia, water flea
+n01995514	fairy shrimp
+n01995686	brine shrimp, Artemia salina
+n01996280	tadpole shrimp
+n01996585	copepod, copepod crustacean
+n01997119	cyclops, water flea
+n01997825	seed shrimp, mussel shrimp, ostracod
+n01998183	barnacle, cirriped, cirripede
+n01998741	acorn barnacle, rock barnacle, Balanus balanoides
+n01999186	goose barnacle, gooseneck barnacle, Lepas fascicularis
+n01999767	onychophoran, velvet worm, peripatus
+n02000954	wading bird, wader
+n02002075	stork
+n02002556	white stork, Ciconia ciconia
+n02002724	black stork, Ciconia nigra
+n02003037	adjutant bird, adjutant, adjutant stork, Leptoptilus dubius
+n02003204	marabou, marabout, marabou stork, Leptoptilus crumeniferus
+n02003577	openbill
+n02003839	jabiru, Jabiru mycteria
+n02004131	saddlebill, jabiru, Ephippiorhynchus senegalensis
+n02004492	policeman bird, black-necked stork, jabiru, Xenorhyncus asiaticus
+n02004855	wood ibis, wood stork, flinthead, Mycteria americana
+n02005399	shoebill, shoebird, Balaeniceps rex
+n02005790	ibis
+n02006063	wood ibis, wood stork, Ibis ibis
+n02006364	sacred ibis, Threskiornis aethiopica
+n02006656	spoonbill
+n02006985	common spoonbill, Platalea leucorodia
+n02007284	roseate spoonbill, Ajaia ajaja
+n02007558	flamingo
+n02008041	heron
+n02008497	great blue heron, Ardea herodius
+n02008643	great white heron, Ardea occidentalis
+n02008796	egret
+n02009229	little blue heron, Egretta caerulea
+n02009380	snowy egret, snowy heron, Egretta thula
+n02009508	little egret, Egretta garzetta
+n02009750	great white heron, Casmerodius albus
+n02009912	American egret, great white heron, Egretta albus
+n02010272	cattle egret, Bubulcus ibis
+n02010453	night heron, night raven
+n02010728	black-crowned night heron, Nycticorax nycticorax
+n02011016	yellow-crowned night heron, Nyctanassa violacea
+n02011281	boatbill, boat-billed heron, broadbill, Cochlearius cochlearius
+n02011460	bittern
+n02011805	American bittern, stake driver, Botaurus lentiginosus
+n02011943	European bittern, Botaurus stellaris
+n02012185	least bittern, Ixobrychus exilis
+n02012849	crane
+n02013177	whooping crane, whooper, Grus americana
+n02013567	courlan, Aramus guarauna
+n02013706	limpkin, Aramus pictus
+n02014237	crested cariama, seriema, Cariama cristata
+n02014524	chunga, seriema, Chunga burmeisteri
+n02014941	rail
+n02015357	weka, maori hen, wood hen
+n02015554	crake
+n02015797	corncrake, land rail, Crex crex
+n02016066	spotted crake, Porzana porzana
+n02016358	gallinule, marsh hen, water hen, swamphen
+n02016659	Florida gallinule, Gallinula chloropus cachinnans
+n02016816	moorhen, Gallinula chloropus
+n02016956	purple gallinule
+n02017213	European gallinule, Porphyrio porphyrio
+n02017475	American gallinule, Porphyrula martinica
+n02017725	notornis, takahe, Notornis mantelli
+n02018027	coot
+n02018207	American coot, marsh hen, mud hen, water hen, Fulica americana
+n02018368	Old World coot, Fulica atra
+n02018795	bustard
+n02019190	great bustard, Otis tarda
+n02019438	plain turkey, Choriotis australis
+n02019929	button quail, button-quail, bustard quail, hemipode
+n02020219	striped button quail, Turnix sylvatica
+n02020578	plain wanderer, Pedionomus torquatus
+n02021050	trumpeter
+n02021281	Brazilian trumpeter, Psophia crepitans
+n02021795	seabird, sea bird, seafowl
+n02022684	shorebird, shore bird, limicoline bird
+n02023341	plover
+n02023855	piping plover, Charadrius melodus
+n02023992	killdeer, kildeer, killdeer plover, Charadrius vociferus
+n02024185	dotterel, dotrel, Charadrius morinellus, Eudromias morinellus
+n02024479	golden plover
+n02024763	lapwing, green plover, peewit, pewit
+n02025043	turnstone
+n02025239	ruddy turnstone, Arenaria interpres
+n02025389	black turnstone, Arenaria-Melanocephala
+n02026059	sandpiper
+n02026629	surfbird, Aphriza virgata
+n02026948	European sandpiper, Actitis hypoleucos
+n02027075	spotted sandpiper, Actitis macularia
+n02027357	least sandpiper, stint, Erolia minutilla
+n02027492	red-backed sandpiper, dunlin, Erolia alpina
+n02027897	greenshank, Tringa nebularia
+n02028035	redshank, Tringa totanus
+n02028175	yellowlegs
+n02028342	greater yellowlegs, Tringa melanoleuca
+n02028451	lesser yellowlegs, Tringa flavipes
+n02028727	pectoral sandpiper, jacksnipe, Calidris melanotos
+n02028900	knot, greyback, grayback, Calidris canutus
+n02029087	curlew sandpiper, Calidris Ferruginea
+n02029378	sanderling, Crocethia alba
+n02029706	upland sandpiper, upland plover, Bartramian sandpiper, Bartramia longicauda
+n02030035	ruff, Philomachus pugnax
+n02030224	reeve
+n02030287	tattler
+n02030568	Polynesian tattler, Heteroscelus incanus
+n02030837	willet, Catoptrophorus semipalmatus
+n02030996	woodcock
+n02031298	Eurasian woodcock, Scolopax rusticola
+n02031585	American woodcock, woodcock snipe, Philohela minor
+n02031934	snipe
+n02032222	whole snipe, Gallinago gallinago
+n02032355	Wilson's snipe, Gallinago gallinago delicata
+n02032480	great snipe, woodcock snipe, Gallinago media
+n02032769	jacksnipe, half snipe, Limnocryptes minima
+n02033041	dowitcher
+n02033208	greyback, grayback, Limnodromus griseus
+n02033324	red-breasted snipe, Limnodromus scolopaceus
+n02033561	curlew
+n02033779	European curlew, Numenius arquata
+n02033882	Eskimo curlew, Numenius borealis
+n02034129	godwit
+n02034295	Hudsonian godwit, Limosa haemastica
+n02034661	stilt, stiltbird, longlegs, long-legs, stilt plover, Himantopus stilt
+n02034971	black-necked stilt, Himantopus mexicanus
+n02035210	black-winged stilt, Himantopus himantopus
+n02035402	white-headed stilt, Himantopus himantopus leucocephalus
+n02035656	kaki, Himantopus novae-zelandiae
+n02036053	stilt, Australian stilt
+n02036228	banded stilt, Cladorhyncus leucocephalum
+n02036711	avocet
+n02037110	oystercatcher, oyster catcher
+n02037464	phalarope
+n02037869	red phalarope, Phalaropus fulicarius
+n02038141	northern phalarope, Lobipes lobatus
+n02038466	Wilson's phalarope, Steganopus tricolor
+n02038993	pratincole, glareole
+n02039171	courser
+n02039497	cream-colored courser, Cursorius cursor
+n02039780	crocodile bird, Pluvianus aegyptius
+n02040266	stone curlew, thick-knee, Burhinus oedicnemus
+n02040505	coastal diving bird
+n02041085	larid
+n02041246	gull, seagull, sea gull
+n02041678	mew, mew gull, sea mew, Larus canus
+n02041875	black-backed gull, great black-backed gull, cob, Larus marinus
+n02042046	herring gull, Larus argentatus
+n02042180	laughing gull, blackcap, pewit, pewit gull, Larus ridibundus
+n02042472	ivory gull, Pagophila eburnea
+n02042759	kittiwake
+n02043063	tern
+n02043333	sea swallow, Sterna hirundo
+n02043808	skimmer
+n02044178	jaeger
+n02044517	parasitic jaeger, arctic skua, Stercorarius parasiticus
+n02044778	skua, bonxie
+n02044908	great skua, Catharacta skua
+n02045369	auk
+n02045596	auklet
+n02045864	razorbill, razor-billed auk, Alca torda
+n02046171	little auk, dovekie, Plautus alle
+n02046759	guillemot
+n02046939	black guillemot, Cepphus grylle
+n02047045	pigeon guillemot, Cepphus columba
+n02047260	murre
+n02047411	common murre, Uria aalge
+n02047517	thick-billed murre, Uria lomvia
+n02047614	puffin
+n02047975	Atlantic puffin, Fratercula arctica
+n02048115	horned puffin, Fratercula corniculata
+n02048353	tufted puffin, Lunda cirrhata
+n02048698	gaviiform seabird
+n02049088	loon, diver
+n02049532	podicipitiform seabird
+n02050004	grebe
+n02050313	great crested grebe, Podiceps cristatus
+n02050442	red-necked grebe, Podiceps grisegena
+n02050586	black-necked grebe, eared grebe, Podiceps nigricollis
+n02050809	dabchick, little grebe, Podiceps ruficollis
+n02051059	pied-billed grebe, Podilymbus podiceps
+n02051474	pelecaniform seabird
+n02051845	pelican
+n02052204	white pelican, Pelecanus erythrorhynchos
+n02052365	Old world white pelican, Pelecanus onocrotalus
+n02052775	frigate bird, man-of-war bird
+n02053083	gannet
+n02053425	solan, solan goose, solant goose, Sula bassana
+n02053584	booby
+n02054036	cormorant, Phalacrocorax carbo
+n02054502	snakebird, anhinga, darter
+n02054711	water turkey, Anhinga anhinga
+n02055107	tropic bird, tropicbird, boatswain bird
+n02055658	sphenisciform seabird
+n02055803	penguin
+n02056228	Adelie, Adelie penguin, Pygoscelis adeliae
+n02056570	king penguin, Aptenodytes patagonica
+n02056728	emperor penguin, Aptenodytes forsteri
+n02057035	jackass penguin, Spheniscus demersus
+n02057330	rock hopper, crested penguin
+n02057731	pelagic bird, oceanic bird
+n02057898	procellariiform seabird
+n02058221	albatross, mollymawk
+n02058594	wandering albatross, Diomedea exulans
+n02058747	black-footed albatross, gooney, gooney bird, goonie, goony, Diomedea nigripes
+n02059162	petrel
+n02059541	white-chinned petrel, Procellaria aequinoctialis
+n02059852	giant petrel, giant fulmar, Macronectes giganteus
+n02060133	fulmar, fulmar petrel, Fulmarus glacialis
+n02060411	shearwater
+n02060569	Manx shearwater, Puffinus puffinus
+n02060889	storm petrel
+n02061217	stormy petrel, northern storm petrel, Hydrobates pelagicus
+n02061560	Mother Carey's chicken, Mother Carey's hen, Oceanites oceanicus
+n02061853	diving petrel
+n02062017	aquatic mammal
+n02062430	cetacean, cetacean mammal, blower
+n02062744	whale
+n02063224	baleen whale, whalebone whale
+n02063662	right whale
+n02064000	bowhead, bowhead whale, Greenland whale, Balaena mysticetus
+n02064338	rorqual, razorback
+n02064816	blue whale, sulfur bottom, Balaenoptera musculus
+n02065026	finback, finback whale, fin whale, common rorqual, Balaenoptera physalus
+n02065263	sei whale, Balaenoptera borealis
+n02065407	lesser rorqual, piked whale, minke whale, Balaenoptera acutorostrata
+n02065726	humpback, humpback whale, Megaptera novaeangliae
+n02066245	grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus
+n02066707	toothed whale
+n02067240	sperm whale, cachalot, black whale, Physeter catodon
+n02067603	pygmy sperm whale, Kogia breviceps
+n02067768	dwarf sperm whale, Kogia simus
+n02068206	beaked whale
+n02068541	bottle-nosed whale, bottlenose whale, bottlenose, Hyperoodon ampullatus
+n02068974	dolphin
+n02069412	common dolphin, Delphinus delphis
+n02069701	bottlenose dolphin, bottle-nosed dolphin, bottlenose
+n02069974	Atlantic bottlenose dolphin, Tursiops truncatus
+n02070174	Pacific bottlenose dolphin, Tursiops gilli
+n02070430	porpoise
+n02070624	harbor porpoise, herring hog, Phocoena phocoena
+n02070776	vaquita, Phocoena sinus
+n02071028	grampus, Grampus griseus
+n02071294	killer whale, killer, orca, grampus, sea wolf, Orcinus orca
+n02071636	pilot whale, black whale, common blackfish, blackfish, Globicephala melaena
+n02072040	river dolphin
+n02072493	narwhal, narwal, narwhale, Monodon monoceros
+n02072798	white whale, beluga, Delphinapterus leucas
+n02073250	sea cow, sirenian mammal, sirenian
+n02073831	manatee, Trichechus manatus
+n02074367	dugong, Dugong dugon
+n02074726	Steller's sea cow, Hydrodamalis gigas
+n02075296	carnivore
+n02075612	omnivore
+n02075927	pinniped mammal, pinniped, pinnatiped
+n02076196	seal
+n02076402	crabeater seal, crab-eating seal
+n02076779	eared seal
+n02077152	fur seal
+n02077384	guadalupe fur seal, Arctocephalus philippi
+n02077658	fur seal
+n02077787	Alaska fur seal, Callorhinus ursinus
+n02077923	sea lion
+n02078292	South American sea lion, Otaria Byronia
+n02078574	California sea lion, Zalophus californianus, Zalophus californicus
+n02078738	Australian sea lion, Zalophus lobatus
+n02079005	Steller sea lion, Steller's sea lion, Eumetopias jubatus
+n02079389	earless seal, true seal, hair seal
+n02079851	harbor seal, common seal, Phoca vitulina
+n02080146	harp seal, Pagophilus groenlandicus
+n02080415	elephant seal, sea elephant
+n02080713	bearded seal, squareflipper square flipper, Erignathus barbatus
+n02081060	hooded seal, bladdernose, Cystophora cristata
+n02081571	walrus, seahorse, sea horse
+n02081798	Atlantic walrus, Odobenus rosmarus
+n02081927	Pacific walrus, Odobenus divergens
+n02082056	Fissipedia
+n02082190	fissiped mammal, fissiped
+n02082791	aardvark, ant bear, anteater, Orycteropus afer
+n02083346	canine, canid
+n02083672	bitch
+n02083780	brood bitch
+n02084071	dog, domestic dog, Canis familiaris
+n02084732	pooch, doggie, doggy, barker, bow-wow
+n02084861	cur, mongrel, mutt
+n02085019	feist, fice
+n02085118	pariah dog, pye-dog, pie-dog
+n02085272	lapdog
+n02085374	toy dog, toy
+n02085620	Chihuahua
+n02085782	Japanese spaniel
+n02085936	Maltese dog, Maltese terrier, Maltese
+n02086079	Pekinese, Pekingese, Peke
+n02086240	Shih-Tzu
+n02086346	toy spaniel
+n02086478	English toy spaniel
+n02086646	Blenheim spaniel
+n02086753	King Charles spaniel
+n02086910	papillon
+n02087046	toy terrier
+n02087122	hunting dog
+n02087314	courser
+n02087394	Rhodesian ridgeback
+n02087551	hound, hound dog
+n02088094	Afghan hound, Afghan
+n02088238	basset, basset hound
+n02088364	beagle
+n02088466	bloodhound, sleuthhound
+n02088632	bluetick
+n02088745	boarhound
+n02088839	coonhound
+n02088992	coondog
+n02089078	black-and-tan coonhound
+n02089232	dachshund, dachsie, badger dog
+n02089468	sausage dog, sausage hound
+n02089555	foxhound
+n02089725	American foxhound
+n02089867	Walker hound, Walker foxhound
+n02089973	English foxhound
+n02090129	harrier
+n02090253	Plott hound
+n02090379	redbone
+n02090475	wolfhound
+n02090622	borzoi, Russian wolfhound
+n02090721	Irish wolfhound
+n02090827	greyhound
+n02091032	Italian greyhound
+n02091134	whippet
+n02091244	Ibizan hound, Ibizan Podenco
+n02091467	Norwegian elkhound, elkhound
+n02091635	otterhound, otter hound
+n02091831	Saluki, gazelle hound
+n02092002	Scottish deerhound, deerhound
+n02092173	staghound
+n02092339	Weimaraner
+n02092468	terrier
+n02093056	bullterrier, bull terrier
+n02093256	Staffordshire bullterrier, Staffordshire bull terrier
+n02093428	American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier
+n02093647	Bedlington terrier
+n02093754	Border terrier
+n02093859	Kerry blue terrier
+n02093991	Irish terrier
+n02094114	Norfolk terrier
+n02094258	Norwich terrier
+n02094433	Yorkshire terrier
+n02094562	rat terrier, ratter
+n02094721	Manchester terrier, black-and-tan terrier
+n02094931	toy Manchester, toy Manchester terrier
+n02095050	fox terrier
+n02095212	smooth-haired fox terrier
+n02095314	wire-haired fox terrier
+n02095412	wirehair, wirehaired terrier, wire-haired terrier
+n02095570	Lakeland terrier
+n02095727	Welsh terrier
+n02095889	Sealyham terrier, Sealyham
+n02096051	Airedale, Airedale terrier
+n02096177	cairn, cairn terrier
+n02096294	Australian terrier
+n02096437	Dandie Dinmont, Dandie Dinmont terrier
+n02096585	Boston bull, Boston terrier
+n02096756	schnauzer
+n02097047	miniature schnauzer
+n02097130	giant schnauzer
+n02097209	standard schnauzer
+n02097298	Scotch terrier, Scottish terrier, Scottie
+n02097474	Tibetan terrier, chrysanthemum dog
+n02097658	silky terrier, Sydney silky
+n02097786	Skye terrier
+n02097967	Clydesdale terrier
+n02098105	soft-coated wheaten terrier
+n02098286	West Highland white terrier
+n02098413	Lhasa, Lhasa apso
+n02098550	sporting dog, gun dog
+n02098806	bird dog
+n02098906	water dog
+n02099029	retriever
+n02099267	flat-coated retriever
+n02099429	curly-coated retriever
+n02099601	golden retriever
+n02099712	Labrador retriever
+n02099849	Chesapeake Bay retriever
+n02099997	pointer, Spanish pointer
+n02100236	German short-haired pointer
+n02100399	setter
+n02100583	vizsla, Hungarian pointer
+n02100735	English setter
+n02100877	Irish setter, red setter
+n02101006	Gordon setter
+n02101108	spaniel
+n02101388	Brittany spaniel
+n02101556	clumber, clumber spaniel
+n02101670	field spaniel
+n02101861	springer spaniel, springer
+n02102040	English springer, English springer spaniel
+n02102177	Welsh springer spaniel
+n02102318	cocker spaniel, English cocker spaniel, cocker
+n02102480	Sussex spaniel
+n02102605	water spaniel
+n02102806	American water spaniel
+n02102973	Irish water spaniel
+n02103181	griffon, wire-haired pointing griffon
+n02103406	working dog
+n02103841	watchdog, guard dog
+n02104029	kuvasz
+n02104184	attack dog
+n02104280	housedog
+n02104365	schipperke
+n02104523	shepherd dog, sheepdog, sheep dog
+n02104882	Belgian sheepdog, Belgian shepherd
+n02105056	groenendael
+n02105162	malinois
+n02105251	briard
+n02105412	kelpie
+n02105505	komondor
+n02105641	Old English sheepdog, bobtail
+n02105855	Shetland sheepdog, Shetland sheep dog, Shetland
+n02106030	collie
+n02106166	Border collie
+n02106382	Bouvier des Flandres, Bouviers des Flandres
+n02106550	Rottweiler
+n02106662	German shepherd, German shepherd dog, German police dog, alsatian
+n02106854	police dog
+n02106966	pinscher
+n02107142	Doberman, Doberman pinscher
+n02107312	miniature pinscher
+n02107420	Sennenhunde
+n02107574	Greater Swiss Mountain dog
+n02107683	Bernese mountain dog
+n02107908	Appenzeller
+n02108000	EntleBucher
+n02108089	boxer
+n02108254	mastiff
+n02108422	bull mastiff
+n02108551	Tibetan mastiff
+n02108672	bulldog, English bulldog
+n02108915	French bulldog
+n02109047	Great Dane
+n02109150	guide dog
+n02109256	Seeing Eye dog
+n02109391	hearing dog
+n02109525	Saint Bernard, St Bernard
+n02109687	seizure-alert dog
+n02109811	sled dog, sledge dog
+n02109961	Eskimo dog, husky
+n02110063	malamute, malemute, Alaskan malamute
+n02110185	Siberian husky
+n02110341	dalmatian, coach dog, carriage dog
+n02110532	liver-spotted dalmatian
+n02110627	affenpinscher, monkey pinscher, monkey dog
+n02110806	basenji
+n02110958	pug, pug-dog
+n02111129	Leonberg
+n02111277	Newfoundland, Newfoundland dog
+n02111500	Great Pyrenees
+n02111626	spitz
+n02111889	Samoyed, Samoyede
+n02112018	Pomeranian
+n02112137	chow, chow chow
+n02112350	keeshond
+n02112497	griffon, Brussels griffon, Belgian griffon
+n02112706	Brabancon griffon
+n02112826	corgi, Welsh corgi
+n02113023	Pembroke, Pembroke Welsh corgi
+n02113186	Cardigan, Cardigan Welsh corgi
+n02113335	poodle, poodle dog
+n02113624	toy poodle
+n02113712	miniature poodle
+n02113799	standard poodle
+n02113892	large poodle
+n02113978	Mexican hairless
+n02114100	wolf
+n02114367	timber wolf, grey wolf, gray wolf, Canis lupus
+n02114548	white wolf, Arctic wolf, Canis lupus tundrarum
+n02114712	red wolf, maned wolf, Canis rufus, Canis niger
+n02114855	coyote, prairie wolf, brush wolf, Canis latrans
+n02115012	coydog
+n02115096	jackal, Canis aureus
+n02115335	wild dog
+n02115641	dingo, warrigal, warragal, Canis dingo
+n02115913	dhole, Cuon alpinus
+n02116185	crab-eating dog, crab-eating fox, Dusicyon cancrivorus
+n02116450	raccoon dog, Nyctereutes procyonides
+n02116738	African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus
+n02117135	hyena, hyaena
+n02117512	striped hyena, Hyaena hyaena
+n02117646	brown hyena, strand wolf, Hyaena brunnea
+n02117900	spotted hyena, laughing hyena, Crocuta crocuta
+n02118176	aardwolf, Proteles cristata
+n02118333	fox
+n02118643	vixen
+n02118707	Reynard
+n02119022	red fox, Vulpes vulpes
+n02119247	black fox
+n02119359	silver fox
+n02119477	red fox, Vulpes fulva
+n02119634	kit fox, prairie fox, Vulpes velox
+n02119789	kit fox, Vulpes macrotis
+n02120079	Arctic fox, white fox, Alopex lagopus
+n02120278	blue fox
+n02120505	grey fox, gray fox, Urocyon cinereoargenteus
+n02120997	feline, felid
+n02121620	cat, true cat
+n02121808	domestic cat, house cat, Felis domesticus, Felis catus
+n02122298	kitty, kitty-cat, puss, pussy, pussycat
+n02122430	mouser
+n02122510	alley cat
+n02122580	stray
+n02122725	tom, tomcat
+n02122810	gib
+n02122878	tabby, queen
+n02122948	kitten, kitty
+n02123045	tabby, tabby cat
+n02123159	tiger cat
+n02123242	tortoiseshell, tortoiseshell-cat, calico cat
+n02123394	Persian cat
+n02123478	Angora, Angora cat
+n02123597	Siamese cat, Siamese
+n02123785	blue point Siamese
+n02123917	Burmese cat
+n02124075	Egyptian cat
+n02124157	Maltese, Maltese cat
+n02124313	Abyssinian, Abyssinian cat
+n02124484	Manx, Manx cat
+n02124623	wildcat
+n02125010	sand cat
+n02125081	European wildcat, catamountain, Felis silvestris
+n02125311	cougar, puma, catamount, mountain lion, painter, panther, Felis concolor
+n02125494	ocelot, panther cat, Felis pardalis
+n02125689	jaguarundi, jaguarundi cat, jaguarondi, eyra, Felis yagouaroundi
+n02125872	kaffir cat, caffer cat, Felis ocreata
+n02126028	jungle cat, Felis chaus
+n02126139	serval, Felis serval
+n02126317	leopard cat, Felis bengalensis
+n02126640	margay, margay cat, Felis wiedi
+n02126787	manul, Pallas's cat, Felis manul
+n02127052	lynx, catamount
+n02127292	common lynx, Lynx lynx
+n02127381	Canada lynx, Lynx canadensis
+n02127482	bobcat, bay lynx, Lynx rufus
+n02127586	spotted lynx, Lynx pardina
+n02127678	caracal, desert lynx, Lynx caracal
+n02127808	big cat, cat
+n02128385	leopard, Panthera pardus
+n02128598	leopardess
+n02128669	panther
+n02128757	snow leopard, ounce, Panthera uncia
+n02128925	jaguar, panther, Panthera onca, Felis onca
+n02129165	lion, king of beasts, Panthera leo
+n02129463	lioness
+n02129530	lionet
+n02129604	tiger, Panthera tigris
+n02129837	Bengal tiger
+n02129923	tigress
+n02129991	liger
+n02130086	tiglon, tigon
+n02130308	cheetah, chetah, Acinonyx jubatus
+n02130545	saber-toothed tiger, sabertooth
+n02130925	Smiledon californicus
+n02131653	bear
+n02132136	brown bear, bruin, Ursus arctos
+n02132320	bruin
+n02132466	Syrian bear, Ursus arctos syriacus
+n02132580	grizzly, grizzly bear, silvertip, silver-tip, Ursus horribilis, Ursus arctos horribilis
+n02132788	Alaskan brown bear, Kodiak bear, Kodiak, Ursus middendorffi, Ursus arctos middendorffi
+n02133161	American black bear, black bear, Ursus americanus, Euarctos americanus
+n02133400	cinnamon bear
+n02133704	Asiatic black bear, black bear, Ursus thibetanus, Selenarctos thibetanus
+n02134084	ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus
+n02134418	sloth bear, Melursus ursinus, Ursus ursinus
+n02134971	viverrine, viverrine mammal
+n02135220	civet, civet cat
+n02135610	large civet, Viverra zibetha
+n02135844	small civet, Viverricula indica, Viverricula malaccensis
+n02136103	binturong, bearcat, Arctictis bintourong
+n02136285	Cryptoprocta, genus Cryptoprocta
+n02136452	fossa, fossa cat, Cryptoprocta ferox
+n02136794	fanaloka, Fossa fossa
+n02137015	genet, Genetta genetta
+n02137302	banded palm civet, Hemigalus hardwickii
+n02137549	mongoose
+n02137722	Indian mongoose, Herpestes nyula
+n02137888	ichneumon, Herpestes ichneumon
+n02138169	palm cat, palm civet
+n02138441	meerkat, mierkat
+n02138647	slender-tailed meerkat, Suricata suricatta
+n02138777	suricate, Suricata tetradactyla
+n02139199	bat, chiropteran
+n02139671	fruit bat, megabat
+n02140049	flying fox
+n02140179	Pteropus capestratus
+n02140268	Pteropus hypomelanus
+n02140491	harpy, harpy bat, tube-nosed bat, tube-nosed fruit bat
+n02140858	Cynopterus sphinx
+n02141306	carnivorous bat, microbat
+n02141611	mouse-eared bat
+n02141713	leafnose bat, leaf-nosed bat
+n02142407	macrotus, Macrotus californicus
+n02142734	spearnose bat
+n02142898	Phyllostomus hastatus
+n02143142	hognose bat, Choeronycteris mexicana
+n02143439	horseshoe bat
+n02143891	horseshoe bat
+n02144251	orange bat, orange horseshoe bat, Rhinonicteris aurantius
+n02144593	false vampire, false vampire bat
+n02144936	big-eared bat, Megaderma lyra
+n02145424	vespertilian bat, vespertilionid
+n02145910	frosted bat, Vespertilio murinus
+n02146201	red bat, Lasiurus borealis
+n02146371	brown bat
+n02146700	little brown bat, little brown myotis, Myotis leucifugus
+n02146879	cave myotis, Myotis velifer
+n02147173	big brown bat, Eptesicus fuscus
+n02147328	serotine, European brown bat, Eptesicus serotinus
+n02147591	pallid bat, cave bat, Antrozous pallidus
+n02147947	pipistrelle, pipistrel, Pipistrellus pipistrellus
+n02148088	eastern pipistrel, Pipistrellus subflavus
+n02148512	jackass bat, spotted bat, Euderma maculata
+n02148835	long-eared bat
+n02148991	western big-eared bat, Plecotus townsendi
+n02149420	freetail, free-tailed bat, freetailed bat
+n02149653	guano bat, Mexican freetail bat, Tadarida brasiliensis
+n02149861	pocketed bat, pocketed freetail bat, Tadirida femorosacca
+n02150134	mastiff bat
+n02150482	vampire bat, true vampire bat
+n02150885	Desmodus rotundus
+n02151230	hairy-legged vampire bat, Diphylla ecaudata
+n02152740	predator, predatory animal
+n02152881	prey, quarry
+n02152991	game
+n02153109	big game
+n02153203	game bird
+n02153809	fossorial mammal
+n02156732	tetrapod
+n02156871	quadruped
+n02157206	hexapod
+n02157285	biped
+n02159955	insect
+n02160947	social insect
+n02161225	holometabola, metabola
+n02161338	defoliator
+n02161457	pollinator
+n02161588	gallfly
+n02162561	scorpion fly
+n02163008	hanging fly
+n02163297	collembolan, springtail
+n02164464	beetle
+n02165105	tiger beetle
+n02165456	ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle
+n02165877	two-spotted ladybug, Adalia bipunctata
+n02166229	Mexican bean beetle, bean beetle, Epilachna varivestis
+n02166567	Hippodamia convergens
+n02166826	vedalia, Rodolia cardinalis
+n02167151	ground beetle, carabid beetle
+n02167505	bombardier beetle
+n02167820	calosoma
+n02167944	searcher, searcher beetle, Calosoma scrutator
+n02168245	firefly, lightning bug
+n02168427	glowworm
+n02168699	long-horned beetle, longicorn, longicorn beetle
+n02169023	sawyer, sawyer beetle
+n02169218	pine sawyer
+n02169497	leaf beetle, chrysomelid
+n02169705	flea beetle
+n02169974	Colorado potato beetle, Colorado beetle, potato bug, potato beetle, Leptinotarsa decemlineata
+n02170400	carpet beetle, carpet bug
+n02170599	buffalo carpet beetle, Anthrenus scrophulariae
+n02170738	black carpet beetle
+n02170993	clerid beetle, clerid
+n02171164	bee beetle
+n02171453	lamellicorn beetle
+n02171869	scarabaeid beetle, scarabaeid, scarabaean
+n02172182	dung beetle
+n02172518	scarab, scarabaeus, Scarabaeus sacer
+n02172678	tumblebug
+n02172761	dorbeetle
+n02172870	June beetle, June bug, May bug, May beetle
+n02173113	green June beetle, figeater
+n02173373	Japanese beetle, Popillia japonica
+n02173784	Oriental beetle, Asiatic beetle, Anomala orientalis
+n02174001	rhinoceros beetle
+n02174355	melolonthid beetle
+n02174659	cockchafer, May bug, May beetle, Melolontha melolontha
+n02175014	rose chafer, rose bug, Macrodactylus subspinosus
+n02175569	rose chafer, rose beetle, Cetonia aurata
+n02175916	stag beetle
+n02176261	elaterid beetle, elater, elaterid
+n02176439	click beetle, skipjack, snapping beetle
+n02176747	firefly, fire beetle, Pyrophorus noctiluca
+n02176916	wireworm
+n02177196	water beetle
+n02177506	whirligig beetle
+n02177775	deathwatch beetle, deathwatch, Xestobium rufovillosum
+n02177972	weevil
+n02178411	snout beetle
+n02178717	boll weevil, Anthonomus grandis
+n02179012	blister beetle, meloid
+n02179192	oil beetle
+n02179340	Spanish fly
+n02179891	Dutch-elm beetle, Scolytus multistriatus
+n02180233	bark beetle
+n02180427	spruce bark beetle, Dendroctonus rufipennis
+n02180875	rove beetle
+n02181235	darkling beetle, darkling groung beetle, tenebrionid
+n02181477	mealworm
+n02181724	flour beetle, flour weevil
+n02182045	seed beetle, seed weevil
+n02182355	pea weevil, Bruchus pisorum
+n02182642	bean weevil, Acanthoscelides obtectus
+n02182930	rice weevil, black weevil, Sitophylus oryzae
+n02183096	Asian longhorned beetle, Anoplophora glabripennis
+n02183507	web spinner
+n02183857	louse, sucking louse
+n02184473	common louse, Pediculus humanus
+n02184589	head louse, Pediculus capitis
+n02184720	body louse, cootie, Pediculus corporis
+n02185167	crab louse, pubic louse, crab, Phthirius pubis
+n02185481	bird louse, biting louse, louse
+n02186153	flea
+n02186717	Pulex irritans
+n02187150	dog flea, Ctenocephalides canis
+n02187279	cat flea, Ctenocephalides felis
+n02187554	chigoe, chigger, chigoe flea, Tunga penetrans
+n02187900	sticktight, sticktight flea, Echidnophaga gallinacea
+n02188699	dipterous insect, two-winged insects, dipteran, dipteron
+n02189363	gall midge, gallfly, gall gnat
+n02189670	Hessian fly, Mayetiola destructor
+n02190166	fly
+n02190790	housefly, house fly, Musca domestica
+n02191273	tsetse fly, tsetse, tzetze fly, tzetze, glossina
+n02191773	blowfly, blow fly
+n02191979	bluebottle, Calliphora vicina
+n02192252	greenbottle, greenbottle fly
+n02192513	flesh fly, Sarcophaga carnaria
+n02192814	tachina fly
+n02193009	gadfly
+n02193163	botfly
+n02194249	human botfly, Dermatobia hominis
+n02194750	sheep botfly, sheep gadfly, Oestrus ovis
+n02195091	warble fly
+n02195526	horsefly, cleg, clegg, horse fly
+n02195819	bee fly
+n02196119	robber fly, bee killer
+n02196344	fruit fly, pomace fly
+n02196896	apple maggot, railroad worm, Rhagoletis pomonella
+n02197185	Mediterranean fruit fly, medfly, Ceratitis capitata
+n02197689	drosophila, Drosophila melanogaster
+n02197877	vinegar fly
+n02198129	leaf miner, leaf-miner
+n02198532	louse fly, hippoboscid
+n02198859	horse tick, horsefly, Hippobosca equina
+n02199170	sheep ked, sheep-tick, sheep tick, Melophagus Ovinus
+n02199502	horn fly, Haematobia irritans
+n02200198	mosquito
+n02200509	wiggler, wriggler
+n02200630	gnat
+n02200850	yellow-fever mosquito, Aedes aegypti
+n02201000	Asian tiger mosquito, Aedes albopictus
+n02201497	anopheline
+n02201626	malarial mosquito, malaria mosquito
+n02202006	common mosquito, Culex pipiens
+n02202124	Culex quinquefasciatus, Culex fatigans
+n02202287	gnat
+n02202678	punkie, punky, punkey, no-see-um, biting midge
+n02203152	midge
+n02203592	fungus gnat
+n02203978	psychodid
+n02204249	sand fly, sandfly, Phlebotomus papatasii
+n02204722	fungus gnat, sciara, sciarid
+n02204907	armyworm
+n02205219	crane fly, daddy longlegs
+n02205673	blackfly, black fly, buffalo gnat
+n02206270	hymenopterous insect, hymenopteran, hymenopteron, hymenopter
+n02206856	bee
+n02207179	drone
+n02207345	queen bee
+n02207449	worker
+n02207647	soldier
+n02207805	worker bee
+n02208280	honeybee, Apis mellifera
+n02208498	Africanized bee, Africanized honey bee, killer bee, Apis mellifera scutellata, Apis mellifera adansonii
+n02208848	black bee, German bee
+n02208979	Carniolan bee
+n02209111	Italian bee
+n02209354	carpenter bee
+n02209624	bumblebee, humblebee
+n02209964	cuckoo-bumblebee
+n02210427	andrena, andrenid, mining bee
+n02210921	Nomia melanderi, alkali bee
+n02211444	leaf-cutting bee, leaf-cutter, leaf-cutter bee
+n02211627	mason bee
+n02211896	potter bee
+n02212062	wasp
+n02212602	vespid, vespid wasp
+n02212958	paper wasp
+n02213107	hornet
+n02213239	giant hornet, Vespa crabro
+n02213543	common wasp, Vespula vulgaris
+n02213663	bald-faced hornet, white-faced hornet, Vespula maculata
+n02213788	yellow jacket, yellow hornet, Vespula maculifrons
+n02214096	Polistes annularis
+n02214341	mason wasp
+n02214499	potter wasp
+n02214660	Mutillidae, family Mutillidae
+n02214773	velvet ant
+n02215161	sphecoid wasp, sphecoid
+n02215621	mason wasp
+n02215770	digger wasp
+n02216211	cicada killer, Sphecius speciosis
+n02216365	mud dauber
+n02216740	gall wasp, gallfly, cynipid wasp, cynipid gall wasp
+n02217563	chalcid fly, chalcidfly, chalcid, chalcid wasp
+n02217839	strawworm, jointworm
+n02218134	chalcis fly
+n02218371	ichneumon fly
+n02218713	sawfly
+n02219015	birch leaf miner, Fenusa pusilla
+n02219486	ant, emmet, pismire
+n02220055	pharaoh ant, pharaoh's ant, Monomorium pharaonis
+n02220225	little black ant, Monomorium minimum
+n02220518	army ant, driver ant, legionary ant
+n02220804	carpenter ant
+n02221083	fire ant
+n02221414	wood ant, Formica rufa
+n02221571	slave ant
+n02221715	Formica fusca
+n02221820	slave-making ant, slave-maker
+n02222035	sanguinary ant, Formica sanguinea
+n02222321	bulldog ant
+n02222582	Amazon ant, Polyergus rufescens
+n02223266	termite, white ant
+n02223520	dry-wood termite
+n02224023	Reticulitermes lucifugus
+n02224713	Mastotermes darwiniensis
+n02225081	Mastotermes electrodominicus
+n02225798	powder-post termite, Cryptotermes brevis
+n02226183	orthopterous insect, orthopteron, orthopteran
+n02226429	grasshopper, hopper
+n02226821	short-horned grasshopper, acridid
+n02226970	locust
+n02227247	migratory locust, Locusta migratoria
+n02227604	migratory grasshopper
+n02227966	long-horned grasshopper, tettigoniid
+n02228341	katydid
+n02228697	mormon cricket, Anabrus simplex
+n02229156	sand cricket, Jerusalem cricket, Stenopelmatus fuscus
+n02229544	cricket
+n02229765	mole cricket
+n02230023	European house cricket, Acheta domestica
+n02230187	field cricket, Acheta assimilis
+n02230480	tree cricket
+n02230634	snowy tree cricket, Oecanthus fultoni
+n02231052	phasmid, phasmid insect
+n02231487	walking stick, walkingstick, stick insect
+n02231803	diapheromera, Diapheromera femorata
+n02232223	walking leaf, leaf insect
+n02233338	cockroach, roach
+n02233943	oriental cockroach, oriental roach, Asiatic cockroach, blackbeetle, Blatta orientalis
+n02234355	American cockroach, Periplaneta americana
+n02234570	Australian cockroach, Periplaneta australasiae
+n02234848	German cockroach, Croton bug, crotonbug, water bug, Blattella germanica
+n02235205	giant cockroach
+n02236044	mantis, mantid
+n02236241	praying mantis, praying mantid, Mantis religioso
+n02236355	bug
+n02236896	hemipterous insect, bug, hemipteran, hemipteron
+n02237424	leaf bug, plant bug
+n02237581	mirid bug, mirid, capsid
+n02237868	four-lined plant bug, four-lined leaf bug, Poecilocapsus lineatus
+n02238235	lygus bug
+n02238358	tarnished plant bug, Lygus lineolaris
+n02238594	lace bug
+n02238887	lygaeid, lygaeid bug
+n02239192	chinch bug, Blissus leucopterus
+n02239528	coreid bug, coreid
+n02239774	squash bug, Anasa tristis
+n02240068	leaf-footed bug, leaf-foot bug
+n02240517	bedbug, bed bug, chinch, Cimex lectularius
+n02241008	backswimmer, Notonecta undulata
+n02241426	true bug
+n02241569	heteropterous insect
+n02241799	water bug
+n02242137	giant water bug
+n02242455	water scorpion
+n02243209	water boatman, boat bug
+n02243562	water strider, pond-skater, water skater
+n02243878	common pond-skater, Gerris lacustris
+n02244173	assassin bug, reduviid
+n02244515	conenose, cone-nosed bug, conenose bug, big bedbug, kissing bug
+n02244797	wheel bug, Arilus cristatus
+n02245111	firebug
+n02245443	cotton stainer
+n02246011	homopterous insect, homopteran
+n02246628	whitefly
+n02246941	citrus whitefly, Dialeurodes citri
+n02247216	greenhouse whitefly, Trialeurodes vaporariorum
+n02247511	sweet-potato whitefly
+n02247655	superbug, Bemisia tabaci, poinsettia strain
+n02248062	cotton strain
+n02248368	coccid insect
+n02248510	scale insect
+n02248887	soft scale
+n02249134	brown soft scale, Coccus hesperidum
+n02249515	armored scale
+n02249809	San Jose scale, Aspidiotus perniciosus
+n02250280	cochineal insect, cochineal, Dactylopius coccus
+n02250822	mealybug, mealy bug
+n02251067	citrophilous mealybug, citrophilus mealybug, Pseudococcus fragilis
+n02251233	Comstock mealybug, Comstock's mealybug, Pseudococcus comstocki
+n02251593	citrus mealybug, Planococcus citri
+n02251775	plant louse, louse
+n02252226	aphid
+n02252799	apple aphid, green apple aphid, Aphis pomi
+n02252972	blackfly, bean aphid, Aphis fabae
+n02253127	greenfly
+n02253264	green peach aphid
+n02253494	ant cow
+n02253715	woolly aphid, woolly plant louse
+n02253913	woolly apple aphid, American blight, Eriosoma lanigerum
+n02254246	woolly alder aphid, Prociphilus tessellatus
+n02254697	adelgid
+n02254901	balsam woolly aphid, Adelges piceae
+n02255023	spruce gall aphid, Adelges abietis
+n02255391	woolly adelgid
+n02256172	jumping plant louse, psylla, psyllid
+n02256656	cicada, cicala
+n02257003	dog-day cicada, harvest fly
+n02257284	seventeen-year locust, periodical cicada, Magicicada septendecim
+n02257715	spittle insect, spittlebug
+n02257985	froghopper
+n02258198	meadow spittlebug, Philaenus spumarius
+n02258508	pine spittlebug
+n02258629	Saratoga spittlebug, Aphrophora saratogensis
+n02259212	leafhopper
+n02259377	plant hopper, planthopper
+n02259708	treehopper
+n02259987	lantern fly, lantern-fly
+n02260421	psocopterous insect
+n02260863	psocid
+n02261063	bark-louse, bark louse
+n02261419	booklouse, book louse, deathwatch, Liposcelis divinatorius
+n02261757	common booklouse, Trogium pulsatorium
+n02262178	ephemerid, ephemeropteran
+n02262449	mayfly, dayfly, shadfly
+n02262803	stonefly, stone fly, plecopteran
+n02263378	neuropteron, neuropteran, neuropterous insect
+n02264021	ant lion, antlion, antlion fly
+n02264232	doodlebug, ant lion, antlion
+n02264363	lacewing, lacewing fly
+n02264591	aphid lion, aphis lion
+n02264885	green lacewing, chrysopid, stink fly
+n02265330	brown lacewing, hemerobiid, hemerobiid fly
+n02266050	dobson, dobsonfly, dobson fly, Corydalus cornutus
+n02266269	hellgrammiate, dobson
+n02266421	fish fly, fish-fly
+n02266864	alderfly, alder fly, Sialis lutaria
+n02267208	snakefly
+n02267483	mantispid
+n02268148	odonate
+n02268443	dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk
+n02268853	damselfly
+n02269196	trichopterous insect, trichopteran, trichopteron
+n02269340	caddis fly, caddis-fly, caddice fly, caddice-fly
+n02269522	caseworm
+n02269657	caddisworm, strawworm
+n02270011	thysanuran insect, thysanuron
+n02270200	bristletail
+n02270623	silverfish, Lepisma saccharina
+n02270945	firebrat, Thermobia domestica
+n02271222	jumping bristletail, machilid
+n02271570	thysanopter, thysanopteron, thysanopterous insect
+n02271897	thrips, thrip, thripid
+n02272286	tobacco thrips, Frankliniella fusca
+n02272552	onion thrips, onion louse, Thrips tobaci
+n02272871	earwig
+n02273392	common European earwig, Forficula auricularia
+n02274024	lepidopterous insect, lepidopteron, lepidopteran
+n02274259	butterfly
+n02274822	nymphalid, nymphalid butterfly, brush-footed butterfly, four-footed butterfly
+n02275560	mourning cloak, mourning cloak butterfly, Camberwell beauty, Nymphalis antiopa
+n02275773	tortoiseshell, tortoiseshell butterfly
+n02276078	painted beauty, Vanessa virginiensis
+n02276258	admiral
+n02276355	red admiral, Vanessa atalanta
+n02276749	white admiral, Limenitis camilla
+n02276902	banded purple, white admiral, Limenitis arthemis
+n02277094	red-spotted purple, Limenitis astyanax
+n02277268	viceroy, Limenitis archippus
+n02277422	anglewing
+n02277742	ringlet, ringlet butterfly
+n02278024	comma, comma butterfly, Polygonia comma
+n02278210	fritillary
+n02278463	silverspot
+n02278839	emperor butterfly, emperor
+n02278980	purple emperor, Apatura iris
+n02279257	peacock, peacock butterfly, Inachis io
+n02279637	danaid, danaid butterfly
+n02279972	monarch, monarch butterfly, milkweed butterfly, Danaus plexippus
+n02280458	pierid, pierid butterfly
+n02280649	cabbage butterfly
+n02281015	small white, Pieris rapae
+n02281136	large white, Pieris brassicae
+n02281267	southern cabbage butterfly, Pieris protodice
+n02281406	sulphur butterfly, sulfur butterfly
+n02281787	lycaenid, lycaenid butterfly
+n02282257	blue
+n02282385	copper
+n02282553	American copper, Lycaena hypophlaeas
+n02282903	hairstreak, hairstreak butterfly
+n02283077	Strymon melinus
+n02283201	moth
+n02283617	moth miller, miller
+n02283951	tortricid, tortricid moth
+n02284224	leaf roller, leaf-roller
+n02284611	tea tortrix, tortrix, Homona coffearia
+n02284884	orange tortrix, tortrix, Argyrotaenia citrana
+n02285179	codling moth, codlin moth, Carpocapsa pomonella
+n02285548	lymantriid, tussock moth
+n02285801	tussock caterpillar
+n02286089	gypsy moth, gipsy moth, Lymantria dispar
+n02286425	browntail, brown-tail moth, Euproctis phaeorrhoea
+n02286654	gold-tail moth, Euproctis chrysorrhoea
+n02287004	geometrid, geometrid moth
+n02287352	Paleacrita vernata
+n02287622	Alsophila pometaria
+n02287799	cankerworm
+n02287987	spring cankerworm
+n02288122	fall cankerworm
+n02288268	measuring worm, inchworm, looper
+n02288789	pyralid, pyralid moth
+n02289307	bee moth, wax moth, Galleria mellonella
+n02289610	corn borer, European corn borer moth, corn borer moth, Pyrausta nubilalis
+n02289988	Mediterranean flour moth, Anagasta kuehniella
+n02290340	tobacco moth, cacao moth, Ephestia elutella
+n02290664	almond moth, fig moth, Cadra cautella
+n02290870	raisin moth, Cadra figulilella
+n02291220	tineoid, tineoid moth
+n02291572	tineid, tineid moth
+n02291748	clothes moth
+n02292085	casemaking clothes moth, Tinea pellionella
+n02292401	webbing clothes moth, webbing moth, Tineola bisselliella
+n02292692	carpet moth, tapestry moth, Trichophaga tapetzella
+n02293352	gelechiid, gelechiid moth
+n02293868	grain moth
+n02294097	angoumois moth, angoumois grain moth, Sitotroga cerealella
+n02294407	potato moth, potato tuber moth, splitworm, Phthorimaea operculella
+n02294577	potato tuberworm, Phthorimaea operculella
+n02295064	noctuid moth, noctuid, owlet moth
+n02295390	cutworm
+n02295870	underwing
+n02296021	red underwing, Catocala nupta
+n02296276	antler moth, Cerapteryx graminis
+n02296612	heliothis moth, Heliothis zia
+n02296912	army cutworm, Chorizagrotis auxiliaris
+n02297294	armyworm, Pseudaletia unipuncta
+n02297442	armyworm, army worm, Pseudaletia unipuncta
+n02297819	Spodoptera exigua
+n02297938	beet armyworm, Spodoptera exigua
+n02298095	Spodoptera frugiperda
+n02298218	fall armyworm, Spodoptera frugiperda
+n02298541	hawkmoth, hawk moth, sphingid, sphinx moth, hummingbird moth
+n02299039	Manduca sexta
+n02299157	tobacco hornworm, tomato worm, Manduca sexta
+n02299378	Manduca quinquemaculata
+n02299505	tomato hornworm, potato worm, Manduca quinquemaculata
+n02299846	death's-head moth, Acherontia atropos
+n02300173	bombycid, bombycid moth, silkworm moth
+n02300554	domestic silkworm moth, domesticated silkworm moth, Bombyx mori
+n02300797	silkworm
+n02301452	saturniid, saturniid moth
+n02301935	emperor, emperor moth, Saturnia pavonia
+n02302244	imperial moth, Eacles imperialis
+n02302459	giant silkworm moth, silkworm moth
+n02302620	silkworm, giant silkworm, wild wilkworm
+n02302969	luna moth, Actias luna
+n02303284	cecropia, cecropia moth, Hyalophora cecropia
+n02303585	cynthia moth, Samia cynthia, Samia walkeri
+n02303777	ailanthus silkworm, Samia cynthia
+n02304036	io moth, Automeris io
+n02304432	polyphemus moth, Antheraea polyphemus
+n02304657	pernyi moth, Antheraea pernyi
+n02304797	tussah, tusseh, tussur, tussore, tusser, Antheraea mylitta
+n02305085	atlas moth, Atticus atlas
+n02305407	arctiid, arctiid moth
+n02305636	tiger moth
+n02305929	cinnabar, cinnabar moth, Callimorpha jacobeae
+n02306433	lasiocampid, lasiocampid moth
+n02306825	eggar, egger
+n02307176	tent-caterpillar moth, Malacosoma americana
+n02307325	tent caterpillar
+n02307515	tent-caterpillar moth, Malacosoma disstria
+n02307681	forest tent caterpillar, Malacosoma disstria
+n02307910	lappet, lappet moth
+n02308033	lappet caterpillar
+n02308139	webworm
+n02308471	webworm moth
+n02308618	Hyphantria cunea
+n02308735	fall webworm, Hyphantria cunea
+n02309120	garden webworm, Loxostege similalis
+n02309242	instar
+n02309337	caterpillar
+n02309841	corn borer, Pyrausta nubilalis
+n02310000	bollworm
+n02310149	pink bollworm, Gelechia gossypiella
+n02310334	corn earworm, cotton bollworm, tomato fruitworm, tobacco budworm, vetchworm, Heliothis zia
+n02310585	cabbageworm, Pieris rapae
+n02310717	woolly bear, woolly bear caterpillar
+n02310941	woolly bear moth
+n02311060	larva
+n02311617	nymph
+n02311748	leptocephalus
+n02312006	grub
+n02312175	maggot
+n02312325	leatherjacket
+n02312427	pupa
+n02312640	chrysalis
+n02312912	imago
+n02313008	queen
+n02313360	phoronid
+n02313709	bryozoan, polyzoan, sea mat, sea moss, moss animal
+n02315487	brachiopod, lamp shell, lampshell
+n02315821	peanut worm, sipunculid
+n02316707	echinoderm
+n02317335	starfish, sea star
+n02317781	brittle star, brittle-star, serpent star
+n02318167	basket star, basket fish
+n02318687	Astrophyton muricatum
+n02319095	sea urchin
+n02319308	edible sea urchin, Echinus esculentus
+n02319555	sand dollar
+n02319829	heart urchin
+n02320127	crinoid
+n02320465	sea lily
+n02321170	feather star, comatulid
+n02321529	sea cucumber, holothurian
+n02322047	trepang, Holothuria edulis
+n02322992	Duplicidentata
+n02323449	lagomorph, gnawing mammal
+n02323902	leporid, leporid mammal
+n02324045	rabbit, coney, cony
+n02324431	rabbit ears
+n02324514	lapin
+n02324587	bunny, bunny rabbit
+n02324850	European rabbit, Old World rabbit, Oryctolagus cuniculus
+n02325366	wood rabbit, cottontail, cottontail rabbit
+n02325722	eastern cottontail, Sylvilagus floridanus
+n02325884	swamp rabbit, canecutter, swamp hare, Sylvilagus aquaticus
+n02326074	marsh hare, swamp rabbit, Sylvilagus palustris
+n02326432	hare
+n02326763	leveret
+n02326862	European hare, Lepus europaeus
+n02327028	jackrabbit
+n02327175	white-tailed jackrabbit, whitetail jackrabbit, Lepus townsendi
+n02327435	blacktail jackrabbit, Lepus californicus
+n02327656	polar hare, Arctic hare, Lepus arcticus
+n02327842	snowshoe hare, snowshoe rabbit, varying hare, Lepus americanus
+n02328009	Belgian hare, leporide
+n02328150	Angora, Angora rabbit
+n02328429	pika, mouse hare, rock rabbit, coney, cony
+n02328820	little chief hare, Ochotona princeps
+n02328942	collared pika, Ochotona collaris
+n02329401	rodent, gnawer
+n02330245	mouse
+n02331046	rat
+n02331309	pocket rat
+n02331842	murine
+n02332156	house mouse, Mus musculus
+n02332447	harvest mouse, Micromyx minutus
+n02332755	field mouse, fieldmouse
+n02332954	nude mouse
+n02333190	European wood mouse, Apodemus sylvaticus
+n02333546	brown rat, Norway rat, Rattus norvegicus
+n02333733	wharf rat
+n02333819	sewer rat
+n02333909	black rat, roof rat, Rattus rattus
+n02334201	bandicoot rat, mole rat
+n02334460	jerboa rat
+n02334728	kangaroo mouse
+n02335127	water rat
+n02335231	beaver rat
+n02336011	New World mouse
+n02336275	American harvest mouse, harvest mouse
+n02336641	wood mouse
+n02336826	white-footed mouse, vesper mouse, Peromyscus leucopus
+n02337001	deer mouse, Peromyscus maniculatus
+n02337171	cactus mouse, Peromyscus eremicus
+n02337332	cotton mouse, Peromyscus gossypinus
+n02337598	pygmy mouse, Baiomys taylori
+n02337902	grasshopper mouse
+n02338145	muskrat, musquash, Ondatra zibethica
+n02338449	round-tailed muskrat, Florida water rat, Neofiber alleni
+n02338722	cotton rat, Sigmodon hispidus
+n02338901	wood rat, wood-rat
+n02339282	dusky-footed wood rat
+n02339376	vole, field mouse
+n02339922	packrat, pack rat, trade rat, bushytail woodrat, Neotoma cinerea
+n02340186	dusky-footed woodrat, Neotoma fuscipes
+n02340358	eastern woodrat, Neotoma floridana
+n02340640	rice rat, Oryzomys palustris
+n02340930	pine vole, pine mouse, Pitymys pinetorum
+n02341288	meadow vole, meadow mouse, Microtus pennsylvaticus
+n02341475	water vole, Richardson vole, Microtus richardsoni
+n02341616	prairie vole, Microtus ochrogaster
+n02341974	water vole, water rat, Arvicola amphibius
+n02342250	red-backed mouse, redback vole
+n02342534	phenacomys
+n02342885	hamster
+n02343058	Eurasian hamster, Cricetus cricetus
+n02343320	golden hamster, Syrian hamster, Mesocricetus auratus
+n02343772	gerbil, gerbille
+n02344175	jird
+n02344270	tamarisk gerbil, Meriones unguiculatus
+n02344408	sand rat, Meriones longifrons
+n02344528	lemming
+n02344918	European lemming, Lemmus lemmus
+n02345078	brown lemming, Lemmus trimucronatus
+n02345340	grey lemming, gray lemming, red-backed lemming
+n02345600	pied lemming
+n02345774	Hudson bay collared lemming, Dicrostonyx hudsonius
+n02345997	southern bog lemming, Synaptomys cooperi
+n02346170	northern bog lemming, Synaptomys borealis
+n02346627	porcupine, hedgehog
+n02346998	Old World porcupine
+n02347274	brush-tailed porcupine, brush-tail porcupine
+n02347573	long-tailed porcupine, Trichys lipura
+n02347744	New World porcupine
+n02348173	Canada porcupine, Erethizon dorsatum
+n02348788	pocket mouse
+n02349205	silky pocket mouse, Perognathus flavus
+n02349390	plains pocket mouse, Perognathus flavescens
+n02349557	hispid pocket mouse, Perognathus hispidus
+n02349847	Mexican pocket mouse, Liomys irroratus
+n02350105	kangaroo rat, desert rat, Dipodomys phillipsii
+n02350357	Ord kangaroo rat, Dipodomys ordi
+n02350670	kangaroo mouse, dwarf pocket rat
+n02350989	jumping mouse
+n02351343	meadow jumping mouse, Zapus hudsonius
+n02351870	jerboa
+n02352002	typical jerboa
+n02352290	Jaculus jaculus
+n02352591	dormouse
+n02352932	loir, Glis glis
+n02353172	hazel mouse, Muscardinus avellanarius
+n02353411	lerot
+n02353861	gopher, pocket gopher, pouched rat
+n02354162	plains pocket gopher, Geomys bursarius
+n02354320	southeastern pocket gopher, Geomys pinetis
+n02354621	valley pocket gopher, Thomomys bottae
+n02354781	northern pocket gopher, Thomomys talpoides
+n02355227	squirrel
+n02355477	tree squirrel
+n02356381	eastern grey squirrel, eastern gray squirrel, cat squirrel, Sciurus carolinensis
+n02356612	western grey squirrel, western gray squirrel, Sciurus griseus
+n02356798	fox squirrel, eastern fox squirrel, Sciurus niger
+n02356977	black squirrel
+n02357111	red squirrel, cat squirrel, Sciurus vulgaris
+n02357401	American red squirrel, spruce squirrel, red squirrel, Sciurus hudsonicus, Tamiasciurus hudsonicus
+n02357585	chickeree, Douglas squirrel, Tamiasciurus douglasi
+n02357911	antelope squirrel, whitetail antelope squirrel, antelope chipmunk, Citellus leucurus
+n02358091	ground squirrel, gopher, spermophile
+n02358390	mantled ground squirrel, Citellus lateralis
+n02358584	suslik, souslik, Citellus citellus
+n02358712	flickertail, Richardson ground squirrel, Citellus richardsoni
+n02358890	rock squirrel, Citellus variegatus
+n02359047	Arctic ground squirrel, parka squirrel, Citellus parryi
+n02359324	prairie dog, prairie marmot
+n02359556	blacktail prairie dog, Cynomys ludovicianus
+n02359667	whitetail prairie dog, Cynomys gunnisoni
+n02359915	eastern chipmunk, hackee, striped squirrel, ground squirrel, Tamias striatus
+n02360282	chipmunk
+n02360480	baronduki, baranduki, barunduki, burunduki, Eutamius asiaticus, Eutamius sibiricus
+n02360781	American flying squirrel
+n02360933	southern flying squirrel, Glaucomys volans
+n02361090	northern flying squirrel, Glaucomys sabrinus
+n02361337	marmot
+n02361587	groundhog, woodchuck, Marmota monax
+n02361706	hoary marmot, whistler, whistling marmot, Marmota caligata
+n02361850	yellowbelly marmot, rockchuck, Marmota flaviventris
+n02362194	Asiatic flying squirrel
+n02363005	beaver
+n02363245	Old World beaver, Castor fiber
+n02363351	New World beaver, Castor canadensis
+n02363996	mountain beaver, sewellel, Aplodontia rufa
+n02364520	cavy
+n02364673	guinea pig, Cavia cobaya
+n02364840	aperea, wild cavy, Cavia porcellus
+n02365108	mara, Dolichotis patagonum
+n02365480	capybara, capibara, Hydrochoerus hydrochaeris
+n02366002	agouti, Dasyprocta aguti
+n02366301	paca, Cuniculus paca
+n02366579	mountain paca
+n02366959	coypu, nutria, Myocastor coypus
+n02367492	chinchilla, Chinchilla laniger
+n02367812	mountain chinchilla, mountain viscacha
+n02368116	viscacha, chinchillon, Lagostomus maximus
+n02368399	abrocome, chinchilla rat, rat chinchilla
+n02368821	mole rat
+n02369293	mole rat
+n02369555	sand rat
+n02369680	naked mole rat
+n02369935	queen, queen mole rat
+n02370137	Damaraland mole rat
+n02370525	Ungulata
+n02370806	ungulate, hoofed mammal
+n02371344	unguiculate, unguiculate mammal
+n02372140	dinoceras, uintathere
+n02372584	hyrax, coney, cony, dassie, das
+n02372952	rock hyrax, rock rabbit, Procavia capensis
+n02373336	odd-toed ungulate, perissodactyl, perissodactyl mammal
+n02374149	equine, equid
+n02374451	horse, Equus caballus
+n02375302	roan
+n02375438	stablemate, stable companion
+n02375757	gee-gee
+n02375862	eohippus, dawn horse
+n02376542	foal
+n02376679	filly
+n02376791	colt
+n02376918	male horse
+n02377063	ridgeling, ridgling, ridgel, ridgil
+n02377181	stallion, entire
+n02377291	stud, studhorse
+n02377388	gelding
+n02377480	mare, female horse
+n02377603	broodmare, stud mare
+n02377703	saddle horse, riding horse, mount
+n02378149	remount
+n02378299	palfrey
+n02378415	warhorse
+n02378541	cavalry horse
+n02378625	charger, courser
+n02378755	steed
+n02378870	prancer
+n02378969	hack
+n02379081	cow pony
+n02379183	quarter horse
+n02379329	Morgan
+n02379430	Tennessee walker, Tennessee walking horse, Walking horse, Plantation walking horse
+n02379630	American saddle horse
+n02379743	Appaloosa
+n02379908	Arabian, Arab
+n02380052	Lippizan, Lipizzan, Lippizaner
+n02380335	pony
+n02380464	polo pony
+n02380583	mustang
+n02380745	bronco, bronc, broncho
+n02380875	bucking bronco
+n02381004	buckskin
+n02381119	crowbait, crow-bait
+n02381261	dun
+n02381364	grey, gray
+n02381460	wild horse
+n02381609	tarpan, Equus caballus gomelini
+n02381831	Przewalski's horse, Przevalski's horse, Equus caballus przewalskii, Equus caballus przevalskii
+n02382039	cayuse, Indian pony
+n02382132	hack
+n02382204	hack, jade, nag, plug
+n02382338	plow horse, plough horse
+n02382437	pony
+n02382635	Shetland pony
+n02382750	Welsh pony
+n02382850	Exmoor
+n02382948	racehorse, race horse, bangtail
+n02383231	thoroughbred
+n02384741	steeplechaser
+n02384858	racer
+n02385002	finisher
+n02385098	pony
+n02385214	yearling
+n02385580	dark horse
+n02385676	mudder
+n02385776	nonstarter
+n02385898	stalking-horse
+n02386014	harness horse
+n02386141	cob
+n02386224	hackney
+n02386310	workhorse
+n02386496	draft horse, draught horse, dray horse
+n02386746	packhorse
+n02386853	carthorse, cart horse, drayhorse
+n02386968	Clydesdale
+n02387093	Percheron
+n02387254	farm horse, dobbin
+n02387346	shire, shire horse
+n02387452	pole horse, poler
+n02387722	post horse, post-horse, poster
+n02387887	coach horse
+n02387983	pacer
+n02388143	pacer, pacemaker, pacesetter
+n02388276	trotting horse, trotter
+n02388453	pole horse
+n02388588	stepper, high stepper
+n02388735	chestnut
+n02388832	liver chestnut
+n02388917	bay
+n02389026	sorrel
+n02389128	palomino
+n02389261	pinto
+n02389346	ass
+n02389559	domestic ass, donkey, Equus asinus
+n02389779	burro
+n02389865	moke
+n02389943	jack, jackass
+n02390015	jennet, jenny, jenny ass
+n02390101	mule
+n02390258	hinny
+n02390454	wild ass
+n02390640	African wild ass, Equus asinus
+n02390738	kiang, Equus kiang
+n02390834	onager, Equus hemionus
+n02390938	chigetai, dziggetai, Equus hemionus hemionus
+n02391049	zebra
+n02391234	common zebra, Burchell's zebra, Equus Burchelli
+n02391373	mountain zebra, Equus zebra zebra
+n02391508	grevy's zebra, Equus grevyi
+n02391617	quagga, Equus quagga
+n02391994	rhinoceros, rhino
+n02392434	Indian rhinoceros, Rhinoceros unicornis
+n02392555	woolly rhinoceros, Rhinoceros antiquitatis
+n02392824	white rhinoceros, Ceratotherium simum, Diceros simus
+n02393161	black rhinoceros, Diceros bicornis
+n02393580	tapir
+n02393807	New World tapir, Tapirus terrestris
+n02393940	Malayan tapir, Indian tapir, Tapirus indicus
+n02394477	even-toed ungulate, artiodactyl, artiodactyl mammal
+n02395003	swine
+n02395406	hog, pig, grunter, squealer, Sus scrofa
+n02395694	piglet, piggy, shoat, shote
+n02395855	sucking pig
+n02395931	porker
+n02396014	boar
+n02396088	sow
+n02396157	razorback, razorback hog, razorbacked hog
+n02396427	wild boar, boar, Sus scrofa
+n02396796	babirusa, babiroussa, babirussa, Babyrousa Babyrussa
+n02397096	warthog
+n02397529	peccary, musk hog
+n02397744	collared peccary, javelina, Tayassu angulatus, Tayassu tajacu, Peccari angulatus
+n02397987	white-lipped peccary, Tayassu pecari
+n02398521	hippopotamus, hippo, river horse, Hippopotamus amphibius
+n02399000	ruminant
+n02401031	bovid
+n02402010	bovine
+n02402175	ox, wild ox
+n02402425	cattle, cows, kine, oxen, Bos taurus
+n02403003	ox
+n02403153	stirk
+n02403231	bullock, steer
+n02403325	bull
+n02403454	cow, moo-cow
+n02403740	heifer
+n02403820	bullock
+n02403920	dogie, dogy, leppy
+n02404028	maverick
+n02404186	beef, beef cattle
+n02404432	longhorn, Texas longhorn
+n02404573	Brahman, Brahma, Brahmin, Bos indicus
+n02404906	zebu
+n02405101	aurochs, urus, Bos primigenius
+n02405302	yak, Bos grunniens
+n02405440	banteng, banting, tsine, Bos banteng
+n02405577	Welsh, Welsh Black
+n02405692	red poll
+n02405799	Santa Gertrudis
+n02405929	Aberdeen Angus, Angus, black Angus
+n02406046	Africander
+n02406174	dairy cattle, dairy cow, milch cow, milk cow, milcher, milker
+n02406432	Ayrshire
+n02406533	Brown Swiss
+n02406647	Charolais
+n02406749	Jersey
+n02406859	Devon
+n02406952	grade
+n02407071	Durham, shorthorn
+n02407172	milking shorthorn
+n02407276	Galloway
+n02407390	Friesian, Holstein, Holstein-Friesian
+n02407521	Guernsey
+n02407625	Hereford, whiteface
+n02407763	cattalo, beefalo
+n02407959	Old World buffalo, buffalo
+n02408429	water buffalo, water ox, Asiatic buffalo, Bubalus bubalis
+n02408660	Indian buffalo
+n02408817	carabao
+n02409038	anoa, dwarf buffalo, Anoa depressicornis
+n02409202	tamarau, tamarao, Bubalus mindorensis, Anoa mindorensis
+n02409508	Cape buffalo, Synercus caffer
+n02409870	Asian wild ox
+n02410011	gaur, Bibos gaurus
+n02410141	gayal, mithan, Bibos frontalis
+n02410509	bison
+n02410702	American bison, American buffalo, buffalo, Bison bison
+n02410900	wisent, aurochs, Bison bonasus
+n02411206	musk ox, musk sheep, Ovibos moschatus
+n02411705	sheep
+n02411999	ewe
+n02412080	ram, tup
+n02412210	wether
+n02412440	lamb
+n02412629	lambkin
+n02412700	baa-lamb
+n02412787	hog, hogget, hogg
+n02412909	teg
+n02412977	Persian lamb
+n02413050	black sheep
+n02413131	domestic sheep, Ovis aries
+n02413484	Cotswold
+n02413593	Hampshire, Hampshire down
+n02413717	Lincoln
+n02413824	Exmoor
+n02413917	Cheviot
+n02414043	broadtail, caracul, karakul
+n02414209	longwool
+n02414290	merino, merino sheep
+n02414442	Rambouillet
+n02414578	wild sheep
+n02414763	argali, argal, Ovis ammon
+n02414904	Marco Polo sheep, Marco Polo's sheep, Ovis poli
+n02415130	urial, Ovis vignei
+n02415253	Dall sheep, Dall's sheep, white sheep, Ovis montana dalli
+n02415435	mountain sheep
+n02415577	bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis
+n02415829	mouflon, moufflon, Ovis musimon
+n02416104	aoudad, arui, audad, Barbary sheep, maned sheep, Ammotragus lervia
+n02416519	goat, caprine animal
+n02416820	kid
+n02416880	billy, billy goat, he-goat
+n02416964	nanny, nanny-goat, she-goat
+n02417070	domestic goat, Capra hircus
+n02417242	Cashmere goat, Kashmir goat
+n02417387	Angora, Angora goat
+n02417534	wild goat
+n02417663	bezoar goat, pasang, Capra aegagrus
+n02417785	markhor, markhoor, Capra falconeri
+n02417914	ibex, Capra ibex
+n02418064	goat antelope
+n02418465	mountain goat, Rocky Mountain goat, Oreamnos americanus
+n02418770	goral, Naemorhedus goral
+n02419056	serow
+n02419336	chamois, Rupicapra rupicapra
+n02419634	takin, gnu goat, Budorcas taxicolor
+n02419796	antelope
+n02420509	blackbuck, black buck, Antilope cervicapra
+n02420828	gerenuk, Litocranius walleri
+n02421136	addax, Addax nasomaculatus
+n02421449	gnu, wildebeest
+n02421792	dik-dik
+n02422106	hartebeest
+n02422391	sassaby, topi, Damaliscus lunatus
+n02422699	impala, Aepyceros melampus
+n02423022	gazelle
+n02423218	Thomson's gazelle, Gazella thomsoni
+n02423362	Gazella subgutturosa
+n02423589	springbok, springbuck, Antidorcas marsupialis, Antidorcas euchore
+n02424085	bongo, Tragelaphus eurycerus, Boocercus eurycerus
+n02424305	kudu, koodoo, koudou
+n02424486	greater kudu, Tragelaphus strepsiceros
+n02424589	lesser kudu, Tragelaphus imberbis
+n02424695	harnessed antelope
+n02424909	nyala, Tragelaphus angasi
+n02425086	mountain nyala, Tragelaphus buxtoni
+n02425228	bushbuck, guib, Tragelaphus scriptus
+n02425532	nilgai, nylghai, nylghau, blue bull, Boselaphus tragocamelus
+n02425887	sable antelope, Hippotragus niger
+n02426176	saiga, Saiga tatarica
+n02426481	steenbok, steinbok, Raphicerus campestris
+n02426813	eland
+n02427032	common eland, Taurotragus oryx
+n02427183	giant eland, Taurotragus derbianus
+n02427470	kob, Kobus kob
+n02427576	lechwe, Kobus leche
+n02427724	waterbuck
+n02428089	puku, Adenota vardoni
+n02428349	oryx, pasang
+n02428508	gemsbok, gemsbuck, Oryx gazella
+n02428842	forest goat, spindle horn, Pseudoryx nghetinhensis
+n02429456	pronghorn, prongbuck, pronghorn antelope, American antelope, Antilocapra americana
+n02430045	deer, cervid
+n02430559	stag
+n02430643	royal, royal stag
+n02430748	pricket
+n02430830	fawn
+n02431122	red deer, elk, American elk, wapiti, Cervus elaphus
+n02431337	hart, stag
+n02431441	hind
+n02431542	brocket
+n02431628	sambar, sambur, Cervus unicolor
+n02431785	wapiti, elk, American elk, Cervus elaphus canadensis
+n02431976	Japanese deer, sika, Cervus nipon, Cervus sika
+n02432291	Virginia deer, white tail, whitetail, white-tailed deer, whitetail deer, Odocoileus Virginianus
+n02432511	mule deer, burro deer, Odocoileus hemionus
+n02432704	black-tailed deer, blacktail deer, blacktail, Odocoileus hemionus columbianus
+n02432983	elk, European elk, moose, Alces alces
+n02433318	fallow deer, Dama dama
+n02433546	roe deer, Capreolus capreolus
+n02433729	roebuck
+n02433925	caribou, reindeer, Greenland caribou, Rangifer tarandus
+n02434190	woodland caribou, Rangifer caribou
+n02434415	barren ground caribou, Rangifer arcticus
+n02434712	brocket
+n02434954	muntjac, barking deer
+n02435216	musk deer, Moschus moschiferus
+n02435517	pere david's deer, elaphure, Elaphurus davidianus
+n02435853	chevrotain, mouse deer
+n02436224	kanchil, Tragulus kanchil
+n02436353	napu, Tragulus Javanicus
+n02436645	water chevrotain, water deer, Hyemoschus aquaticus
+n02437136	camel
+n02437312	Arabian camel, dromedary, Camelus dromedarius
+n02437482	Bactrian camel, Camelus bactrianus
+n02437616	llama
+n02437971	domestic llama, Lama peruana
+n02438173	guanaco, Lama guanicoe
+n02438272	alpaca, Lama pacos
+n02438580	vicuna, Vicugna vicugna
+n02439033	giraffe, camelopard, Giraffa camelopardalis
+n02439398	okapi, Okapia johnstoni
+n02441326	musteline mammal, mustelid, musteline
+n02441942	weasel
+n02442172	ermine, shorttail weasel, Mustela erminea
+n02442336	stoat
+n02442446	New World least weasel, Mustela rixosa
+n02442572	Old World least weasel, Mustela nivalis
+n02442668	longtail weasel, long-tailed weasel, Mustela frenata
+n02442845	mink
+n02443015	American mink, Mustela vison
+n02443114	polecat, fitch, foulmart, foumart, Mustela putorius
+n02443346	ferret
+n02443484	black-footed ferret, ferret, Mustela nigripes
+n02443808	muishond
+n02443959	snake muishond, Poecilogale albinucha
+n02444251	striped muishond, Ictonyx striata
+n02444819	otter
+n02445004	river otter, Lutra canadensis
+n02445171	Eurasian otter, Lutra lutra
+n02445394	sea otter, Enhydra lutris
+n02445715	skunk, polecat, wood pussy
+n02446206	striped skunk, Mephitis mephitis
+n02446352	hooded skunk, Mephitis macroura
+n02446645	hog-nosed skunk, hognosed skunk, badger skunk, rooter skunk, Conepatus leuconotus
+n02447021	spotted skunk, little spotted skunk, Spilogale putorius
+n02447366	badger
+n02447762	American badger, Taxidea taxus
+n02448060	Eurasian badger, Meles meles
+n02448318	ratel, honey badger, Mellivora capensis
+n02448633	ferret badger
+n02448885	hog badger, hog-nosed badger, sand badger, Arctonyx collaris
+n02449183	wolverine, carcajou, skunk bear, Gulo luscus
+n02449350	glutton, Gulo gulo, wolverine
+n02449699	grison, Grison vittatus, Galictis vittatus
+n02450034	marten, marten cat
+n02450295	pine marten, Martes martes
+n02450426	sable, Martes zibellina
+n02450561	American marten, American sable, Martes americana
+n02450677	stone marten, beech marten, Martes foina
+n02450829	fisher, pekan, fisher cat, black cat, Martes pennanti
+n02451125	yellow-throated marten, Charronia flavigula
+n02451415	tayra, taira, Eira barbara
+n02451575	fictional animal
+n02453108	pachyderm
+n02453611	edentate
+n02454379	armadillo
+n02454794	peba, nine-banded armadillo, Texas armadillo, Dasypus novemcinctus
+n02455135	apar, three-banded armadillo, Tolypeutes tricinctus
+n02455428	tatouay, cabassous, Cabassous unicinctus
+n02455720	peludo, poyou, Euphractus sexcinctus
+n02456008	giant armadillo, tatou, tatu, Priodontes giganteus
+n02456275	pichiciago, pichiciego, fairy armadillo, chlamyphore, Chlamyphorus truncatus
+n02456962	sloth, tree sloth
+n02457408	three-toed sloth, ai, Bradypus tridactylus
+n02457945	two-toed sloth, unau, unai, Choloepus didactylus
+n02458135	two-toed sloth, unau, unai, Choloepus hoffmanni
+n02458517	megatherian, megatheriid, megatherian mammal
+n02459190	mylodontid
+n02460009	anteater, New World anteater
+n02460451	ant bear, giant anteater, great anteater, tamanoir, Myrmecophaga jubata
+n02460817	silky anteater, two-toed anteater, Cyclopes didactylus
+n02461128	tamandua, tamandu, lesser anteater, Tamandua tetradactyla
+n02461830	pangolin, scaly anteater, anteater
+n02462213	coronet
+n02469248	scapular
+n02469472	tadpole, polliwog, pollywog
+n02469914	primate
+n02470238	simian
+n02470325	ape
+n02470709	anthropoid
+n02470899	anthropoid ape
+n02471300	hominoid
+n02471762	hominid
+n02472293	homo, man, human being, human
+n02472987	world, human race, humanity, humankind, human beings, humans, mankind, man
+n02473307	Homo erectus
+n02473554	Pithecanthropus, Pithecanthropus erectus, genus Pithecanthropus
+n02473720	Java man, Trinil man
+n02473857	Peking man
+n02473983	Sinanthropus, genus Sinanthropus
+n02474110	Homo soloensis
+n02474282	Javanthropus, genus Javanthropus
+n02474605	Homo habilis
+n02474777	Homo sapiens
+n02475078	Neandertal man, Neanderthal man, Neandertal, Neanderthal, Homo sapiens neanderthalensis
+n02475358	Cro-magnon
+n02475669	Homo sapiens sapiens, modern man
+n02476219	australopithecine
+n02476567	Australopithecus afarensis
+n02476870	Australopithecus africanus
+n02477028	Australopithecus boisei
+n02477187	Zinjanthropus, genus Zinjanthropus
+n02477329	Australopithecus robustus
+n02477516	Paranthropus, genus Paranthropus
+n02477782	Sivapithecus
+n02478239	rudapithecus, Dryopithecus Rudapithecus hungaricus
+n02478875	proconsul
+n02479332	Aegyptopithecus
+n02480153	great ape, pongid
+n02480495	orangutan, orang, orangutang, Pongo pygmaeus
+n02480855	gorilla, Gorilla gorilla
+n02481103	western lowland gorilla, Gorilla gorilla gorilla
+n02481235	eastern lowland gorilla, Gorilla gorilla grauri
+n02481366	mountain gorilla, Gorilla gorilla beringei
+n02481500	silverback
+n02481823	chimpanzee, chimp, Pan troglodytes
+n02482060	western chimpanzee, Pan troglodytes verus
+n02482286	eastern chimpanzee, Pan troglodytes schweinfurthii
+n02482474	central chimpanzee, Pan troglodytes troglodytes
+n02482650	pygmy chimpanzee, bonobo, Pan paniscus
+n02483092	lesser ape
+n02483362	gibbon, Hylobates lar
+n02483708	siamang, Hylobates syndactylus, Symphalangus syndactylus
+n02484322	monkey
+n02484473	Old World monkey, catarrhine
+n02484975	guenon, guenon monkey
+n02485225	talapoin, Cercopithecus talapoin
+n02485371	grivet, Cercopithecus aethiops
+n02485536	vervet, vervet monkey, Cercopithecus aethiops pygerythrus
+n02485688	green monkey, African green monkey, Cercopithecus aethiops sabaeus
+n02485988	mangabey
+n02486261	patas, hussar monkey, Erythrocebus patas
+n02486410	baboon
+n02486657	chacma, chacma baboon, Papio ursinus
+n02486908	mandrill, Mandrillus sphinx
+n02487079	drill, Mandrillus leucophaeus
+n02487347	macaque
+n02487547	rhesus, rhesus monkey, Macaca mulatta
+n02487675	bonnet macaque, bonnet monkey, capped macaque, crown monkey, Macaca radiata
+n02487847	Barbary ape, Macaca sylvana
+n02488003	crab-eating macaque, croo monkey, Macaca irus
+n02488291	langur
+n02488415	entellus, hanuman, Presbytes entellus, Semnopithecus entellus
+n02488702	colobus, colobus monkey
+n02488894	guereza, Colobus guereza
+n02489166	proboscis monkey, Nasalis larvatus
+n02489589	New World monkey, platyrrhine, platyrrhinian
+n02490219	marmoset
+n02490597	true marmoset
+n02490811	pygmy marmoset, Cebuella pygmaea
+n02491107	tamarin, lion monkey, lion marmoset, leoncita
+n02491329	silky tamarin, Leontocebus rosalia
+n02491474	pinche, Leontocebus oedipus
+n02492035	capuchin, ringtail, Cebus capucinus
+n02492356	douroucouli, Aotus trivirgatus
+n02492660	howler monkey, howler
+n02492948	saki
+n02493224	uakari
+n02493509	titi, titi monkey
+n02493793	spider monkey, Ateles geoffroyi
+n02494079	squirrel monkey, Saimiri sciureus
+n02494383	woolly monkey
+n02495242	tree shrew
+n02496052	prosimian
+n02496913	lemur
+n02497673	Madagascar cat, ring-tailed lemur, Lemur catta
+n02498153	aye-aye, Daubentonia madagascariensis
+n02498743	slender loris, Loris gracilis
+n02499022	slow loris, Nycticebus tardigradua, Nycticebus pygmaeus
+n02499316	potto, kinkajou, Perodicticus potto
+n02499568	angwantibo, golden potto, Arctocebus calabarensis
+n02499808	galago, bushbaby, bush baby
+n02500267	indri, indris, Indri indri, Indri brevicaudatus
+n02500596	woolly indris, Avahi laniger
+n02501583	tarsier
+n02501923	Tarsius syrichta
+n02502006	Tarsius glis
+n02502514	flying lemur, flying cat, colugo
+n02502807	Cynocephalus variegatus
+n02503127	proboscidean, proboscidian
+n02503517	elephant
+n02503756	rogue elephant
+n02504013	Indian elephant, Elephas maximus
+n02504458	African elephant, Loxodonta africana
+n02504770	mammoth
+n02505063	woolly mammoth, northern mammoth, Mammuthus primigenius
+n02505238	columbian mammoth, Mammuthus columbi
+n02505485	imperial mammoth, imperial elephant, Archidiskidon imperator
+n02505998	mastodon, mastodont
+n02506947	plantigrade mammal, plantigrade
+n02507148	digitigrade mammal, digitigrade
+n02507649	procyonid
+n02508021	raccoon, racoon
+n02508213	common raccoon, common racoon, coon, ringtail, Procyon lotor
+n02508346	crab-eating raccoon, Procyon cancrivorus
+n02508742	bassarisk, cacomistle, cacomixle, coon cat, raccoon fox, ringtail, ring-tailed cat, civet cat, miner's cat, Bassariscus astutus
+n02509197	kinkajou, honey bear, potto, Potos flavus, Potos caudivolvulus
+n02509515	coati, coati-mondi, coati-mundi, coon cat, Nasua narica
+n02509815	lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens
+n02510455	giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca
+n02511730	twitterer
+n02512053	fish
+n02512752	fingerling
+n02512830	game fish, sport fish
+n02512938	food fish
+n02513248	rough fish
+n02513355	groundfish, bottom fish
+n02513560	young fish
+n02513727	parr
+n02513805	mouthbreeder
+n02513939	spawner
+n02514041	barracouta, snoek
+n02515214	crossopterygian, lobefin, lobe-finned fish
+n02515713	coelacanth, Latimeria chalumnae
+n02516188	lungfish
+n02516776	ceratodus
+n02517442	catfish, siluriform fish
+n02517938	silurid, silurid fish
+n02518324	European catfish, sheatfish, Silurus glanis
+n02518622	electric catfish, Malopterurus electricus
+n02519148	bullhead, bullhead catfish
+n02519340	horned pout, hornpout, pout, Ameiurus Melas
+n02519472	brown bullhead
+n02519686	channel catfish, channel cat, Ictalurus punctatus
+n02519862	blue catfish, blue cat, blue channel catfish, blue channel cat
+n02520147	flathead catfish, mudcat, goujon, shovelnose catfish, spoonbill catfish, Pylodictus olivaris
+n02520525	armored catfish
+n02520810	sea catfish
+n02521646	gadoid, gadoid fish
+n02522399	cod, codfish
+n02522637	codling
+n02522722	Atlantic cod, Gadus morhua
+n02522866	Pacific cod, Alaska cod, Gadus macrocephalus
+n02523110	whiting, Merlangus merlangus, Gadus merlangus
+n02523427	burbot, eelpout, ling, cusk, Lota lota
+n02523877	haddock, Melanogrammus aeglefinus
+n02524202	pollack, pollock, Pollachius pollachius
+n02524524	hake
+n02524659	silver hake, Merluccius bilinearis, whiting
+n02524928	ling
+n02525382	cusk, torsk, Brosme brosme
+n02525703	grenadier, rattail, rattail fish
+n02526121	eel
+n02526425	elver
+n02526818	common eel, freshwater eel
+n02527057	tuna, Anguilla sucklandii
+n02527271	moray, moray eel
+n02527622	conger, conger eel
+n02528163	teleost fish, teleost, teleostan
+n02529293	beaked salmon, sandfish, Gonorhynchus gonorhynchus
+n02529772	clupeid fish, clupeid
+n02530052	whitebait
+n02530188	brit, britt
+n02530421	shad
+n02530637	common American shad, Alosa sapidissima
+n02530831	river shad, Alosa chrysocloris
+n02530999	allice shad, allis shad, allice, allis, Alosa alosa
+n02531114	alewife, Alosa pseudoharengus, Pomolobus pseudoharengus
+n02531625	menhaden, Brevoortia tyrannis
+n02532028	herring, Clupea harangus
+n02532272	Atlantic herring, Clupea harengus harengus
+n02532451	Pacific herring, Clupea harengus pallasii
+n02532602	sardine
+n02532786	sild
+n02532918	brisling, sprat, Clupea sprattus
+n02533209	pilchard, sardine, Sardina pilchardus
+n02533545	Pacific sardine, Sardinops caerulea
+n02533834	anchovy
+n02534165	mediterranean anchovy, Engraulis encrasicholus
+n02534559	salmonid
+n02534734	salmon
+n02535080	parr
+n02535163	blackfish
+n02535258	redfish
+n02535537	Atlantic salmon, Salmo salar
+n02535759	landlocked salmon, lake salmon
+n02536165	sockeye, sockeye salmon, red salmon, blueback salmon, Oncorhynchus nerka
+n02536456	chinook, chinook salmon, king salmon, quinnat salmon, Oncorhynchus tshawytscha
+n02536864	coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch
+n02537085	trout
+n02537319	brown trout, salmon trout, Salmo trutta
+n02537525	rainbow trout, Salmo gairdneri
+n02537716	sea trout
+n02538010	lake trout, salmon trout, Salvelinus namaycush
+n02538216	brook trout, speckled trout, Salvelinus fontinalis
+n02538406	char, charr
+n02538562	Arctic char, Salvelinus alpinus
+n02538985	whitefish
+n02539424	lake whitefish, Coregonus clupeaformis
+n02539573	cisco, lake herring, Coregonus artedi
+n02539894	round whitefish, Menominee whitefish, Prosopium cylindraceum
+n02540412	smelt
+n02540983	sparling, European smelt, Osmerus eperlanus
+n02541257	capelin, capelan, caplin
+n02541687	tarpon, Tarpon atlanticus
+n02542017	ladyfish, tenpounder, Elops saurus
+n02542432	bonefish, Albula vulpes
+n02542958	argentine
+n02543255	lanternfish
+n02543565	lizardfish, snakefish, snake-fish
+n02544274	lancetfish, lancet fish, wolffish
+n02545841	opah, moonfish, Lampris regius
+n02546028	New World opah, Lampris guttatus
+n02546331	ribbonfish
+n02546627	dealfish, Trachipterus arcticus
+n02547014	oarfish, king of the herring, ribbonfish, Regalecus glesne
+n02547733	batfish
+n02548247	goosefish, angler, anglerfish, angler fish, monkfish, lotte, allmouth, Lophius Americanus
+n02548689	toadfish, Opsanus tau
+n02548884	oyster fish, oyster-fish, oysterfish
+n02549248	frogfish
+n02549376	sargassum fish
+n02549989	needlefish, gar, billfish
+n02550203	timucu
+n02550460	flying fish
+n02550655	monoplane flying fish, two-wing flying fish
+n02551134	halfbeak
+n02551668	saury, billfish, Scomberesox saurus
+n02552171	spiny-finned fish, acanthopterygian
+n02553028	lingcod, Ophiodon elongatus
+n02554730	percoid fish, percoid, percoidean
+n02555863	perch
+n02556373	climbing perch, Anabas testudineus, A. testudineus
+n02556846	perch
+n02557182	yellow perch, Perca flavescens
+n02557318	European perch, Perca fluviatilis
+n02557591	pike-perch, pike perch
+n02557749	walleye, walleyed pike, jack salmon, dory, Stizostedion vitreum
+n02557909	blue pike, blue pickerel, blue pikeperch, blue walleye, Strizostedion vitreum glaucum
+n02558206	snail darter, Percina tanasi
+n02558860	cusk-eel
+n02559144	brotula
+n02559383	pearlfish, pearl-fish
+n02559862	robalo
+n02560110	snook
+n02561108	pike
+n02561381	northern pike, Esox lucius
+n02561514	muskellunge, Esox masquinongy
+n02561661	pickerel
+n02561803	chain pickerel, chain pike, Esox niger
+n02561937	redfin pickerel, barred pickerel, Esox americanus
+n02562315	sunfish, centrarchid
+n02562796	crappie
+n02562971	black crappie, Pomoxis nigromaculatus
+n02563079	white crappie, Pomoxis annularis
+n02563182	freshwater bream, bream
+n02563648	pumpkinseed, Lepomis gibbosus
+n02563792	bluegill, Lepomis macrochirus
+n02563949	spotted sunfish, stumpknocker, Lepomis punctatus
+n02564270	freshwater bass
+n02564403	rock bass, rock sunfish, Ambloplites rupestris
+n02564720	black bass
+n02564935	Kentucky black bass, spotted black bass, Micropterus pseudoplites
+n02565072	smallmouth, smallmouth bass, smallmouthed bass, smallmouth black bass, smallmouthed black bass, Micropterus dolomieu
+n02565324	largemouth, largemouth bass, largemouthed bass, largemouth black bass, largemouthed black bass, Micropterus salmoides
+n02565573	bass
+n02566109	serranid fish, serranid
+n02566489	white perch, silver perch, Morone americana
+n02566665	yellow bass, Morone interrupta
+n02567334	blackmouth bass, Synagrops bellus
+n02567633	rock sea bass, rock bass, Centropristis philadelphica
+n02568087	striped bass, striper, Roccus saxatilis, rockfish
+n02568447	stone bass, wreckfish, Polyprion americanus
+n02568959	grouper
+n02569484	hind
+n02569631	rock hind, Epinephelus adscensionis
+n02569905	creole-fish, Paranthias furcifer
+n02570164	jewfish, Mycteroperca bonaci
+n02570484	soapfish
+n02570838	surfperch, surffish, surf fish
+n02571167	rainbow seaperch, rainbow perch, Hipsurus caryi
+n02571652	bigeye
+n02571810	catalufa, Priacanthus arenatus
+n02572196	cardinalfish
+n02572484	flame fish, flamefish, Apogon maculatus
+n02573249	tilefish, Lopholatilus chamaeleonticeps
+n02573704	bluefish, Pomatomus saltatrix
+n02574271	cobia, Rachycentron canadum, sergeant fish
+n02574910	remora, suckerfish, sucking fish
+n02575325	sharksucker, Echeneis naucrates
+n02575590	whale sucker, whalesucker, Remilegia australis
+n02576223	carangid fish, carangid
+n02576575	jack
+n02576906	crevalle jack, jack crevalle, Caranx hippos
+n02577041	yellow jack, Caranx bartholomaei
+n02577164	runner, blue runner, Caranx crysos
+n02577403	rainbow runner, Elagatis bipinnulata
+n02577662	leatherjacket, leatherjack
+n02577952	threadfish, thread-fish, Alectis ciliaris
+n02578233	moonfish, Atlantic moonfish, horsefish, horsehead, horse-head, dollarfish, Selene setapinnis
+n02578454	lookdown, lookdown fish, Selene vomer
+n02578771	amberjack, amberfish
+n02578928	yellowtail, Seriola dorsalis
+n02579303	kingfish, Seriola grandis
+n02579557	pompano
+n02579762	Florida pompano, Trachinotus carolinus
+n02579928	permit, Trachinotus falcatus
+n02580336	scad
+n02580679	horse mackerel, jack mackerel, Spanish mackerel, saurel, Trachurus symmetricus
+n02580830	horse mackerel, saurel, Trachurus trachurus
+n02581108	bigeye scad, big-eyed scad, goggle-eye, Selar crumenophthalmus
+n02581482	mackerel scad, mackerel shad, Decapterus macarellus
+n02581642	round scad, cigarfish, quiaquia, Decapterus punctatus
+n02581957	dolphinfish, dolphin, mahimahi
+n02582220	Coryphaena hippurus
+n02582349	Coryphaena equisetis
+n02582721	pomfret, Brama raii
+n02583567	characin, characin fish, characid
+n02583890	tetra
+n02584145	cardinal tetra, Paracheirodon axelrodi
+n02584449	piranha, pirana, caribe
+n02585872	cichlid, cichlid fish
+n02586238	bolti, Tilapia nilotica
+n02586543	snapper
+n02587051	red snapper, Lutjanus blackfordi
+n02587300	grey snapper, gray snapper, mangrove snapper, Lutjanus griseus
+n02587479	mutton snapper, muttonfish, Lutjanus analis
+n02587618	schoolmaster, Lutjanus apodus
+n02587877	yellowtail, yellowtail snapper, Ocyurus chrysurus
+n02588286	grunt
+n02588794	margate, Haemulon album
+n02588945	Spanish grunt, Haemulon macrostomum
+n02589062	tomtate, Haemulon aurolineatum
+n02589196	cottonwick, Haemulon malanurum
+n02589316	sailor's-choice, sailors choice, Haemulon parra
+n02589623	porkfish, pork-fish, Anisotremus virginicus
+n02589796	pompon, black margate, Anisotremus surinamensis
+n02590094	pigfish, hogfish, Orthopristis chrysopterus
+n02590495	sparid, sparid fish
+n02590702	sea bream, bream
+n02590987	porgy
+n02591330	red porgy, Pagrus pagrus
+n02591613	European sea bream, Pagellus centrodontus
+n02591911	Atlantic sea bream, Archosargus rhomboidalis
+n02592055	sheepshead, Archosargus probatocephalus
+n02592371	pinfish, sailor's-choice, squirrelfish, Lagodon rhomboides
+n02592734	sheepshead porgy, Calamus penna
+n02593019	snapper, Chrysophrys auratus
+n02593191	black bream, Chrysophrys australis
+n02593453	scup, northern porgy, northern scup, Stenotomus chrysops
+n02593679	scup, southern porgy, southern scup, Stenotomus aculeatus
+n02594250	sciaenid fish, sciaenid
+n02594942	striped drum, Equetus pulcher
+n02595056	jackknife-fish, Equetus lanceolatus
+n02595339	silver perch, mademoiselle, Bairdiella chrysoura
+n02595702	red drum, channel bass, redfish, Sciaenops ocellatus
+n02596067	mulloway, jewfish, Sciaena antarctica
+n02596252	maigre, maiger, Sciaena aquila
+n02596381	croaker
+n02596720	Atlantic croaker, Micropogonias undulatus
+n02597004	yellowfin croaker, surffish, surf fish, Umbrina roncador
+n02597367	whiting
+n02597608	kingfish
+n02597818	king whiting, Menticirrhus americanus
+n02597972	northern whiting, Menticirrhus saxatilis
+n02598134	corbina, Menticirrhus undulatus
+n02598573	white croaker, chenfish, kingfish, Genyonemus lineatus
+n02598878	white croaker, queenfish, Seriphus politus
+n02599052	sea trout
+n02599347	weakfish, Cynoscion regalis
+n02599557	spotted weakfish, spotted sea trout, spotted squeateague, Cynoscion nebulosus
+n02599958	mullet
+n02600298	goatfish, red mullet, surmullet, Mullus surmuletus
+n02600503	red goatfish, Mullus auratus
+n02600798	yellow goatfish, Mulloidichthys martinicus
+n02601344	mullet, grey mullet, gray mullet
+n02601767	striped mullet, Mugil cephalus
+n02601921	white mullet, Mugil curema
+n02602059	liza, Mugil liza
+n02602405	silversides, silverside
+n02602760	jacksmelt, Atherinopsis californiensis
+n02603317	barracuda
+n02603540	great barracuda, Sphyraena barracuda
+n02603862	sweeper
+n02604157	sea chub
+n02604480	Bermuda chub, rudderfish, Kyphosus sectatrix
+n02604954	spadefish, angelfish, Chaetodipterus faber
+n02605316	butterfly fish
+n02605703	chaetodon
+n02605936	angelfish
+n02606052	rock beauty, Holocanthus tricolor
+n02606384	damselfish, demoiselle
+n02606751	beaugregory, Pomacentrus leucostictus
+n02607072	anemone fish
+n02607201	clown anemone fish, Amphiprion percula
+n02607470	sergeant major, Abudefduf saxatilis
+n02607862	wrasse
+n02608284	pigfish, giant pigfish, Achoerodus gouldii
+n02608547	hogfish, hog snapper, Lachnolaimus maximus
+n02608860	slippery dick, Halicoeres bivittatus
+n02608996	puddingwife, pudding-wife, Halicoeres radiatus
+n02609302	bluehead, Thalassoma bifasciatum
+n02609823	pearly razorfish, Hemipteronatus novacula
+n02610066	tautog, blackfish, Tautoga onitis
+n02610373	cunner, bergall, Tautogolabrus adspersus
+n02610664	parrotfish, polly fish, pollyfish
+n02610980	threadfin
+n02611561	jawfish
+n02611898	stargazer
+n02612167	sand stargazer
+n02613181	blenny, combtooth blenny
+n02613572	shanny, Blennius pholis
+n02613820	Molly Miller, Scartella cristata
+n02614140	clinid, clinid fish
+n02614482	pikeblenny
+n02614653	bluethroat pikeblenny, Chaenopsis ocellata
+n02614978	gunnel, bracketed blenny
+n02615298	rock gunnel, butterfish, Pholis gunnellus
+n02616128	eelblenny
+n02616397	wrymouth, ghostfish, Cryptacanthodes maculatus
+n02616851	wolffish, wolf fish, catfish
+n02617537	viviparous eelpout, Zoarces viviparus
+n02618094	ocean pout, Macrozoarces americanus
+n02618513	sand lance, sand launce, sand eel, launce
+n02618827	dragonet
+n02619165	goby, gudgeon
+n02619550	mudskipper, mudspringer
+n02619861	sleeper, sleeper goby
+n02620167	flathead
+n02620578	archerfish, Toxotes jaculatrix
+n02621258	surgeonfish
+n02621908	gempylid
+n02622249	snake mackerel, Gempylus serpens
+n02622547	escolar, Lepidocybium flavobrunneum
+n02622712	oilfish, Ruvettus pretiosus
+n02622955	cutlassfish, frost fish, hairtail
+n02623445	scombroid, scombroid fish
+n02624167	mackerel
+n02624551	common mackerel, shiner, Scomber scombrus
+n02624807	Spanish mackerel, Scomber colias
+n02624987	chub mackerel, tinker, Scomber japonicus
+n02625258	wahoo, Acanthocybium solandri
+n02625612	Spanish mackerel
+n02625851	king mackerel, cavalla, cero, Scomberomorus cavalla
+n02626089	Scomberomorus maculatus
+n02626265	cero, pintado, kingfish, Scomberomorus regalis
+n02626471	sierra, Scomberomorus sierra
+n02626762	tuna, tunny
+n02627037	albacore, long-fin tunny, Thunnus alalunga
+n02627292	bluefin, bluefin tuna, horse mackerel, Thunnus thynnus
+n02627532	yellowfin, yellowfin tuna, Thunnus albacares
+n02627835	bonito
+n02628062	skipjack, Atlantic bonito, Sarda sarda
+n02628259	Chile bonito, Chilean bonito, Pacific bonito, Sarda chiliensis
+n02628600	skipjack, skipjack tuna, Euthynnus pelamis
+n02629230	bonito, oceanic bonito, Katsuwonus pelamis
+n02629716	swordfish, Xiphias gladius
+n02630281	sailfish
+n02630615	Atlantic sailfish, Istiophorus albicans
+n02630739	billfish
+n02631041	marlin
+n02631330	blue marlin, Makaira nigricans
+n02631475	black marlin, Makaira mazara, Makaira marlina
+n02631628	striped marlin, Makaira mitsukurii
+n02631775	white marlin, Makaira albida
+n02632039	spearfish
+n02632494	louvar, Luvarus imperialis
+n02633422	dollarfish, Poronotus triacanthus
+n02633677	palometa, California pompano, Palometa simillima
+n02633977	harvestfish, Paprilus alepidotus
+n02634545	driftfish
+n02635154	barrelfish, black rudderfish, Hyperglyphe perciformis
+n02635580	clingfish
+n02636170	tripletail
+n02636405	Atlantic tripletail, Lobotes surinamensis
+n02636550	Pacific tripletail, Lobotes pacificus
+n02636854	mojarra
+n02637179	yellowfin mojarra, Gerres cinereus
+n02637475	silver jenny, Eucinostomus gula
+n02637977	whiting
+n02638596	ganoid, ganoid fish
+n02639087	bowfin, grindle, dogfish, Amia calva
+n02639605	paddlefish, duckbill, Polyodon spathula
+n02639922	Chinese paddlefish, Psephurus gladis
+n02640242	sturgeon
+n02640626	Pacific sturgeon, white sturgeon, Sacramento sturgeon, Acipenser transmontanus
+n02640857	beluga, hausen, white sturgeon, Acipenser huso
+n02641379	gar, garfish, garpike, billfish, Lepisosteus osseus
+n02642107	scorpaenoid, scorpaenoid fish
+n02642644	scorpaenid, scorpaenid fish
+n02643112	scorpionfish, scorpion fish, sea scorpion
+n02643316	plumed scorpionfish, Scorpaena grandicornis
+n02643566	lionfish
+n02643836	stonefish, Synanceja verrucosa
+n02644113	rockfish
+n02644360	copper rockfish, Sebastodes caurinus
+n02644501	vermillion rockfish, rasher, Sebastodes miniatus
+n02644665	red rockfish, Sebastodes ruberrimus
+n02644817	rosefish, ocean perch, Sebastodes marinus
+n02645538	bullhead
+n02645691	miller's-thumb
+n02645953	sea raven, Hemitripterus americanus
+n02646667	lumpfish, Cyclopterus lumpus
+n02646892	lumpsucker
+n02648035	pogge, armed bullhead, Agonus cataphractus
+n02648625	greenling
+n02648916	kelp greenling, Hexagrammos decagrammus
+n02649218	painted greenling, convict fish, convictfish, Oxylebius pictus
+n02649546	flathead
+n02650050	gurnard
+n02650413	tub gurnard, yellow gurnard, Trigla lucerna
+n02650541	sea robin, searobin
+n02651060	northern sea robin, Prionotus carolinus
+n02652132	flying gurnard, flying robin, butterflyfish
+n02652668	plectognath, plectognath fish
+n02653145	triggerfish
+n02653497	queen triggerfish, Bessy cerca, oldwench, oldwife, Balistes vetula
+n02653786	filefish
+n02654112	leatherjacket, leatherfish
+n02654425	boxfish, trunkfish
+n02654745	cowfish, Lactophrys quadricornis
+n02655020	puffer, pufferfish, blowfish, globefish
+n02655523	spiny puffer
+n02655848	porcupinefish, porcupine fish, Diodon hystrix
+n02656032	balloonfish, Diodon holocanthus
+n02656301	burrfish
+n02656670	ocean sunfish, sunfish, mola, headfish
+n02656969	sharptail mola, Mola lanceolata
+n02657368	flatfish
+n02657694	flounder
+n02658079	righteye flounder, righteyed flounder
+n02658531	plaice, Pleuronectes platessa
+n02658811	European flatfish, Platichthys flesus
+n02659176	yellowtail flounder, Limanda ferruginea
+n02659478	winter flounder, blackback flounder, lemon sole, Pseudopleuronectes americanus
+n02659808	lemon sole, Microstomus kitt
+n02660091	American plaice, Hippoglossoides platessoides
+n02660208	halibut, holibut
+n02660519	Atlantic halibut, Hippoglossus hippoglossus
+n02660640	Pacific halibut, Hippoglossus stenolepsis
+n02661017	lefteye flounder, lefteyed flounder
+n02661473	southern flounder, Paralichthys lethostigmus
+n02661618	summer flounder, Paralichthys dentatus
+n02662239	whiff
+n02662397	horned whiff, Citharichthys cornutus
+n02662559	sand dab
+n02662825	windowpane, Scophthalmus aquosus
+n02662993	brill, Scophthalmus rhombus
+n02663211	turbot, Psetta maxima
+n02663485	tonguefish, tongue-fish
+n02663849	sole
+n02664285	European sole, Solea solea
+n02664642	English sole, lemon sole, Parophrys vitulus
+n02665250	hogchoker, Trinectes maculatus
+n02665985	aba
+n02666196	abacus
+n02666501	abandoned ship, derelict
+n02666624	A battery
+n02666943	abattoir, butchery, shambles, slaughterhouse
+n02667093	abaya
+n02667244	Abbe condenser
+n02667379	abbey
+n02667478	abbey
+n02667576	abbey
+n02667693	Abney level
+n02668393	abrader, abradant
+n02668613	abrading stone
+n02669295	abutment
+n02669442	abutment arch
+n02669534	academic costume
+n02669723	academic gown, academic robe, judge's robe
+n02670186	accelerator, throttle, throttle valve
+n02670382	accelerator, particle accelerator, atom smasher
+n02670683	accelerator, accelerator pedal, gas pedal, gas, throttle, gun
+n02670935	accelerometer
+n02671780	accessory, accoutrement, accouterment
+n02672152	accommodating lens implant, accommodating IOL
+n02672371	accommodation
+n02672831	accordion, piano accordion, squeeze box
+n02675077	acetate disk, phonograph recording disk
+n02675219	acetate rayon, acetate
+n02675522	achromatic lens
+n02676097	acoustic delay line, sonic delay line
+n02676261	acoustic device
+n02676566	acoustic guitar
+n02676670	acoustic modem
+n02676938	acropolis
+n02677028	acrylic
+n02677136	acrylic, acrylic paint
+n02677436	actinometer
+n02677718	action, action mechanism
+n02678010	active matrix screen
+n02678384	actuator
+n02678897	adapter, adaptor
+n02679142	adder
+n02679257	adding machine, totalizer, totaliser
+n02679961	addressing machine, Addressograph
+n02680110	adhesive bandage
+n02680512	adit
+n02680638	adjoining room
+n02680754	adjustable wrench, adjustable spanner
+n02681392	adobe, adobe brick
+n02682311	adz, adze
+n02682407	aeolian harp, aeolian lyre, wind harp
+n02682569	aerator
+n02682811	aerial torpedo
+n02682922	aerosol, aerosol container, aerosol can, aerosol bomb, spray can
+n02683183	Aertex
+n02683323	afghan
+n02683454	Afro-wig
+n02683558	afterburner
+n02683791	after-shave, after-shave lotion
+n02684248	agateware
+n02684356	agglomerator
+n02684515	aglet, aiglet, aiguilette
+n02684649	aglet, aiglet
+n02684962	agora, public square
+n02685082	aigrette, aigret
+n02685253	aileron
+n02685365	air bag
+n02685701	airbrake
+n02685995	airbrush
+n02686121	airbus
+n02686227	air compressor
+n02686379	air conditioner, air conditioning
+n02686568	aircraft
+n02687172	aircraft carrier, carrier, flattop, attack aircraft carrier
+n02687423	aircraft engine
+n02687682	air cushion, air spring
+n02687821	airdock, hangar, repair shed
+n02687992	airfield, landing field, flying field, field
+n02688273	air filter, air cleaner
+n02688443	airfoil, aerofoil, control surface, surface
+n02689144	airframe
+n02689274	air gun, airgun, air rifle
+n02689434	air hammer, jackhammer, pneumatic hammer
+n02689748	air horn
+n02689819	airing cupboard
+n02690373	airliner
+n02690715	airmailer
+n02691156	airplane, aeroplane, plane
+n02692086	airplane propeller, airscrew, prop
+n02692232	airport, airdrome, aerodrome, drome
+n02692513	air pump, vacuum pump
+n02692680	air search radar
+n02692877	airship, dirigible
+n02693246	air terminal, airport terminal
+n02693413	air-to-air missile
+n02693540	air-to-ground missile, air-to-surface missile
+n02694045	aisle
+n02694279	Aladdin's lamp
+n02694426	alarm, warning device, alarm system
+n02694662	alarm clock, alarm
+n02694966	alb
+n02695627	alcazar
+n02695762	alcohol thermometer, alcohol-in-glass thermometer
+n02696165	alehouse
+n02696246	alembic
+n02696569	algometer
+n02696843	alidade, alidad
+n02697022	alidade, alidad
+n02697221	A-line
+n02697576	Allen screw
+n02697675	Allen wrench
+n02697876	alligator wrench
+n02698244	alms dish, alms tray
+n02698473	alpaca
+n02698634	alpenstock
+n02699494	altar
+n02699629	altar, communion table, Lord's table
+n02699770	altarpiece, reredos
+n02699915	altazimuth
+n02700064	alternator
+n02700258	altimeter
+n02700895	Amati
+n02701002	ambulance
+n02701260	amen corner
+n02701730	American organ
+n02702989	ammeter
+n02703124	ammonia clock
+n02703275	ammunition, ammo
+n02704645	amphibian, amphibious aircraft
+n02704792	amphibian, amphibious vehicle
+n02704949	amphitheater, amphitheatre, coliseum
+n02705201	amphitheater, amphitheatre
+n02705429	amphora
+n02705944	amplifier
+n02706221	ampulla
+n02706806	amusement arcade
+n02708093	analog clock
+n02708224	analog computer, analogue computer
+n02708433	analog watch
+n02708555	analytical balance, chemical balance
+n02708711	analyzer, analyser
+n02708885	anamorphosis, anamorphism
+n02709101	anastigmat
+n02709367	anchor, ground tackle
+n02709637	anchor chain, anchor rope
+n02709763	anchor light, riding light, riding lamp
+n02709908	AND circuit, AND gate
+n02710044	andiron, firedog, dog, dog-iron
+n02710201	android, humanoid, mechanical man
+n02710324	anechoic chamber
+n02710429	anemometer, wind gauge, wind gage
+n02710600	aneroid barometer, aneroid
+n02711237	angiocardiogram
+n02711780	angioscope
+n02712545	angle bracket, angle iron
+n02712643	angledozer
+n02713003	ankle brace
+n02713218	anklet, anklets, bobbysock, bobbysocks
+n02713364	anklet
+n02713496	ankus
+n02714315	anode
+n02714535	anode
+n02714751	answering machine
+n02715229	antenna, aerial, transmitting aerial
+n02715513	anteroom, antechamber, entrance hall, hall, foyer, lobby, vestibule
+n02715712	antiaircraft, antiaircraft gun, flak, flack, pom-pom, ack-ack, ack-ack gun
+n02716626	antiballistic missile, ABM
+n02720048	antifouling paint
+n02720576	anti-G suit, G suit
+n02721813	antimacassar
+n02723165	antiperspirant
+n02724722	anti-submarine rocket
+n02725872	anvil
+n02726017	ao dai
+n02726210	apadana
+n02726305	apartment, flat
+n02726681	apartment building, apartment house
+n02727016	aperture
+n02727141	aperture
+n02727426	apiary, bee house
+n02727825	apparatus, setup
+n02728440	apparel, wearing apparel, dress, clothes
+n02729222	applecart
+n02729837	appliance
+n02729965	appliance, contraption, contrivance, convenience, gadget, gizmo, gismo, widget
+n02730265	applicator, applier
+n02730568	appointment, fitting
+n02730930	apron
+n02731251	apron string
+n02731398	apse, apsis
+n02731629	aqualung, Aqua-Lung, scuba
+n02731900	aquaplane
+n02732072	aquarium, fish tank, marine museum
+n02732572	arabesque
+n02732827	arbor, arbour, bower, pergola
+n02733213	arcade, colonnade
+n02733524	arch
+n02734725	architecture
+n02734835	architrave
+n02735268	arch support
+n02735361	arc lamp, arc light
+n02735538	arctic, galosh, golosh, rubber, gumshoe
+n02735688	area
+n02736396	areaway
+n02736798	argyle, argyll
+n02737351	ark
+n02737660	arm
+n02738031	armament
+n02738271	armature
+n02738449	armband
+n02738535	armchair
+n02738741	armet
+n02738859	arm guard, arm pad
+n02738978	armhole
+n02739123	armilla
+n02739427	armlet, arm band
+n02739550	armoire
+n02739668	armor, armour
+n02739889	armored car, armoured car
+n02740061	armored car, armoured car
+n02740300	armored personnel carrier, armoured personnel carrier, APC
+n02740533	armored vehicle, armoured vehicle
+n02740764	armor plate, armour plate, armor plating, plate armor, plate armour
+n02741367	armory, armoury, arsenal
+n02741475	armrest
+n02742070	arquebus, harquebus, hackbut, hagbut
+n02742194	array
+n02742322	array, raiment, regalia
+n02742468	arrester, arrester hook
+n02742753	arrow
+n02743426	arsenal, armory, armoury
+n02744323	arterial road
+n02744844	arthrogram
+n02744961	arthroscope
+n02745492	artificial heart
+n02745611	artificial horizon, gyro horizon, flight indicator
+n02745816	artificial joint
+n02746008	artificial kidney, hemodialyzer
+n02746225	artificial skin
+n02746365	artillery, heavy weapon, gun, ordnance
+n02746595	artillery shell
+n02746683	artist's loft
+n02746978	art school
+n02747063	ascot
+n02747177	ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin
+n02747672	ash-pan
+n02747802	ashtray
+n02748183	aspergill, aspersorium
+n02748359	aspersorium
+n02748491	aspirator
+n02749169	aspirin powder, headache powder
+n02749292	assault gun
+n02749479	assault rifle, assault gun
+n02749670	assegai, assagai
+n02749790	assembly
+n02749953	assembly
+n02750070	assembly hall
+n02750169	assembly plant
+n02750320	astatic coils
+n02750652	astatic galvanometer
+n02751067	astrodome
+n02751215	astrolabe
+n02751295	astronomical telescope
+n02751490	astronomy satellite
+n02752199	athenaeum, atheneum
+n02752496	athletic sock, sweat sock, varsity sock
+n02752615	athletic supporter, supporter, suspensor, jockstrap, jock
+n02752810	atlas, telamon
+n02752917	atmometer, evaporometer
+n02753044	atom bomb, atomic bomb, A-bomb, fission bomb, plutonium bomb
+n02753394	atomic clock
+n02753710	atomic pile, atomic reactor, pile, chain reactor
+n02754103	atomizer, atomiser, spray, sprayer, nebulizer, nebuliser
+n02754656	atrium
+n02755140	attache case, attache
+n02755352	attachment, bond
+n02755529	attack submarine
+n02755675	attenuator
+n02755823	attic
+n02755984	attic fan
+n02756098	attire, garb, dress
+n02756854	audio amplifier
+n02756977	audiocassette
+n02757061	audio CD, audio compact disc
+n02757337	audiometer, sonometer
+n02757462	audio system, sound system
+n02757714	audiotape
+n02757810	audiotape
+n02757927	audiovisual, audiovisual aid
+n02758134	auditorium
+n02758490	auger, gimlet, screw auger, wimble
+n02758863	autobahn
+n02758960	autoclave, sterilizer, steriliser
+n02759257	autofocus
+n02759387	autogiro, autogyro, gyroplane
+n02759700	autoinjector
+n02759963	autoloader, self-loader
+n02760099	automat
+n02760199	automat
+n02760298	automatic choke
+n02760429	automatic firearm, automatic gun, automatic weapon
+n02760658	automatic pistol, automatic
+n02760855	automatic rifle, automatic, machine rifle
+n02761034	automatic transmission, automatic drive
+n02761206	automation
+n02761392	automaton, robot, golem
+n02761557	automobile engine
+n02761696	automobile factory, auto factory, car factory
+n02761834	automobile horn, car horn, motor horn, horn, hooter
+n02762169	autopilot, automatic pilot, robot pilot
+n02762371	autoradiograph
+n02762508	autostrada
+n02762725	auxiliary boiler, donkey boiler
+n02762909	auxiliary engine, donkey engine
+n02763083	auxiliary pump, donkey pump
+n02763198	auxiliary research submarine
+n02763306	auxiliary storage, external storage, secondary storage
+n02763604	aviary, bird sanctuary, volary
+n02763714	awl
+n02763901	awning, sunshade, sunblind
+n02764044	ax, axe
+n02764398	ax handle, axe handle
+n02764505	ax head, axe head
+n02764614	axis, axis of rotation
+n02764779	axle
+n02764935	axle bar
+n02765028	axletree
+n02766168	babushka
+n02766320	baby bed, baby's bed
+n02766534	baby buggy, baby carriage, carriage, perambulator, pram, stroller, go-cart, pushchair, pusher
+n02766792	baby grand, baby grand piano, parlor grand, parlor grand piano, parlour grand, parlour grand piano
+n02767038	baby powder
+n02767147	baby shoe
+n02767433	back, backrest
+n02767665	back
+n02767956	backbench
+n02768114	backboard
+n02768226	backboard, basketball backboard
+n02768433	backbone
+n02768655	back brace
+n02768973	backgammon board
+n02769075	background, desktop, screen background
+n02769290	backhoe
+n02769669	backlighting
+n02769748	backpack, back pack, knapsack, packsack, rucksack, haversack
+n02769963	backpacking tent, pack tent
+n02770078	backplate
+n02770211	back porch
+n02770585	backsaw, back saw
+n02770721	backscratcher
+n02770830	backseat
+n02771004	backspace key, backspace, backspacer
+n02771166	backstairs
+n02771286	backstay
+n02771547	backstop
+n02771750	backsword
+n02772101	backup system
+n02772435	badminton court
+n02772554	badminton equipment
+n02772700	badminton racket, badminton racquet, battledore
+n02773037	bag
+n02773838	bag, traveling bag, travelling bag, grip, suitcase
+n02774152	bag, handbag, pocketbook, purse
+n02774630	baggage, luggage
+n02774921	baggage
+n02775039	baggage car, luggage van
+n02775178	baggage claim
+n02775483	bagpipe
+n02775689	bailey
+n02775813	bailey
+n02775897	Bailey bridge
+n02776007	bain-marie
+n02776205	bait, decoy, lure
+n02776505	baize
+n02776631	bakery, bakeshop, bakehouse
+n02776825	balaclava, balaclava helmet
+n02776978	balalaika
+n02777100	balance
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+n02777402	balance wheel, balance
+n02777638	balbriggan
+n02777734	balcony
+n02777927	balcony
+n02778131	baldachin
+n02778294	baldric, baldrick
+n02778456	bale
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+n02779435	ball
+n02779609	ball and chain
+n02779719	ball-and-socket joint
+n02779971	ballast, light ballast
+n02780315	ball bearing, needle bearing, roller bearing
+n02780445	ball cartridge
+n02780588	ballcock, ball cock
+n02780704	balldress
+n02780815	ballet skirt, tutu
+n02781121	ball gown
+n02781213	ballistic galvanometer
+n02781338	ballistic missile
+n02781517	ballistic pendulum
+n02781764	ballistocardiograph, cardiograph
+n02782093	balloon
+n02782432	balloon bomb, Fugo
+n02782602	balloon sail
+n02782681	ballot box
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+n02783035	ball-peen hammer
+n02783161	ballpoint, ballpoint pen, ballpen, Biro
+n02783324	ballroom, dance hall, dance palace
+n02783459	ball valve
+n02783900	balsa raft, Kon Tiki
+n02783994	baluster
+n02784124	banana boat
+n02784998	band
+n02785648	bandage, patch
+n02786058	Band Aid
+n02786198	bandanna, bandana
+n02786331	bandbox
+n02786463	banderilla
+n02786611	bandoleer, bandolier
+n02786736	bandoneon
+n02786837	bandsaw, band saw
+n02787120	bandwagon
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+n02787622	banjo
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+n02788148	bannister, banister, balustrade, balusters, handrail
+n02788386	banquette
+n02788462	banyan, banian
+n02788572	baptismal font, baptistry, baptistery, font
+n02788689	bar
+n02789487	bar
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+n02790996	barbell
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+n02791795	bar bit
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+n02792948	barge pole
+n02793089	baritone, baritone horn
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+n02793684	barndoor
+n02793842	barn door
+n02793930	barnyard
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+n02794368	barong
+n02794474	barouche
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+n02795169	barrel, cask
+n02795528	barrel, gun barrel
+n02795670	barrelhouse, honky-tonk
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+n02795978	barrel organ, grind organ, hand organ, hurdy gurdy, hurdy-gurdy, street organ
+n02796207	barrel vault
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+n02796995	barroom, bar, saloon, ginmill, taproom
+n02797295	barrow, garden cart, lawn cart, wheelbarrow
+n02797535	bascule
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+n02797881	base, bag
+n02799071	baseball
+n02799175	baseball bat, lumber
+n02799323	baseball cap, jockey cap, golf cap
+n02799897	baseball equipment
+n02800213	baseball glove, glove, baseball mitt, mitt
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+n02800675	basement
+n02800940	basic point defense missile system
+n02801047	basilica, Roman basilica
+n02801184	basilica
+n02801450	basilisk
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+n02801823	basinet
+n02801938	basket, handbasket
+n02802215	basket, basketball hoop, hoop
+n02802426	basketball
+n02802544	basketball court
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+n02802990	basket weave
+n02803349	bass
+n02803539	bass clarinet
+n02803666	bass drum, gran casa
+n02803809	basset horn
+n02803934	bass fiddle, bass viol, bull fiddle, double bass, contrabass, string bass
+n02804123	bass guitar
+n02804252	bass horn, sousaphone, tuba
+n02804414	bassinet
+n02804515	bassinet
+n02804610	bassoon
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+n02805983	bastion
+n02806088	bastion, citadel
+n02806379	bat
+n02806530	bath
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+n02806875	bathhouse, bagnio
+n02806992	bathhouse, bathing machine
+n02807133	bathing cap, swimming cap
+n02807523	bath oil
+n02807616	bathrobe
+n02807731	bathroom, bath
+n02808185	bath salts
+n02808304	bath towel
+n02808440	bathtub, bathing tub, bath, tub
+n02808829	bathyscaphe, bathyscaph, bathyscape
+n02808968	bathysphere
+n02809105	batik
+n02809241	batiste
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+n02809491	baton
+n02809605	baton
+n02809736	baton
+n02810139	battering ram
+n02810270	batter's box
+n02810471	battery, electric battery
+n02810782	battery, stamp battery
+n02811059	batting cage, cage
+n02811204	batting glove
+n02811350	batting helmet
+n02811468	battle-ax, battle-axe
+n02811618	battle cruiser
+n02811719	battle dress
+n02811936	battlement, crenelation, crenellation
+n02812201	battleship, battlewagon
+n02812342	battle sight, battlesight
+n02812631	bay
+n02812785	bay
+n02812949	bayonet
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+n02813544	bazaar, bazar
+n02813645	bazaar, bazar
+n02813752	bazooka
+n02813981	B battery
+n02814116	BB gun
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+n02814533	beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
+n02814774	beachwear
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+n02815478	beading plane
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+n02816656	beanbag
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+n02817251	bearing rein, checkrein
+n02817386	bearing wall
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+n02817650	beater
+n02817799	beating-reed instrument, reed instrument, reed
+n02818135	beaver, castor
+n02818254	beaver
+n02818687	Beckman thermometer
+n02818832	bed
+n02819697	bed
+n02820085	bed and breakfast, bed-and-breakfast
+n02820210	bedclothes, bed clothing, bedding
+n02820556	Bedford cord
+n02820675	bed jacket
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+n02821415	bedpost
+n02821543	bedroll
+n02821627	bedroom, sleeping room, sleeping accommodation, chamber, bedchamber
+n02821943	bedroom furniture
+n02822064	bedsitting room, bedsitter, bedsit
+n02822220	bedspread, bedcover, bed cover, bed covering, counterpane, spread
+n02822399	bedspring
+n02822579	bedstead, bedframe
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+n02823124	beeper, pager
+n02823335	beer barrel, beer keg
+n02823428	beer bottle
+n02823510	beer can
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+n02823848	beer hall
+n02823964	beer mat
+n02824058	beer mug, stein
+n02824152	belaying pin
+n02824319	belfry
+n02824448	bell
+n02825153	bell arch
+n02825240	bellarmine, longbeard, long-beard, greybeard
+n02825442	bellbottom trousers, bell-bottoms, bellbottom pants
+n02825657	bell cote, bell cot
+n02825872	bell foundry
+n02825961	bell gable
+n02826068	bell jar, bell glass
+n02826259	bellows
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+n02826589	bell push
+n02826683	bell seat, balloon seat
+n02826812	bell tent
+n02826886	bell tower
+n02827148	bellyband
+n02827606	belt
+n02828115	belt, belt ammunition, belted ammunition
+n02828299	belt buckle
+n02828427	belting
+n02828884	bench
+n02829246	bench clamp
+n02829353	bench hook
+n02829510	bench lathe
+n02829596	bench press
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+n02831237	beret
+n02831335	berlin
+n02831595	Bermuda shorts, Jamaica shorts
+n02831724	berth, bunk, built in bed
+n02831894	besom
+n02831998	Bessemer converter
+n02833040	bethel
+n02833140	betting shop
+n02833275	bevatron
+n02833403	bevel, bevel square
+n02833793	bevel gear, pinion and crown wheel, pinion and ring gear
+n02834027	B-flat clarinet, licorice stick
+n02834397	bib
+n02834506	bib-and-tucker
+n02834642	bicorn, bicorne
+n02834778	bicycle, bike, wheel, cycle
+n02835271	bicycle-built-for-two, tandem bicycle, tandem
+n02835412	bicycle chain
+n02835551	bicycle clip, trouser clip
+n02835724	bicycle pump
+n02835829	bicycle rack
+n02835915	bicycle seat, saddle
+n02836035	bicycle wheel
+n02836174	bidet
+n02836268	bier
+n02836392	bier
+n02836513	bi-fold door
+n02836607	bifocals
+n02836900	Big Blue, BLU-82
+n02837134	big board
+n02837567	bight
+n02837789	bikini, two-piece
+n02837887	bikini pants
+n02838014	bilge
+n02838178	bilge keel
+n02838345	bilge pump
+n02838577	bilge well
+n02838728	bill, peak, eyeshade, visor, vizor
+n02838958	bill, billhook
+n02839110	billboard, hoarding
+n02839351	billiard ball
+n02839592	billiard room, billiard saloon, billiard parlor, billiard parlour, billiard hall
+n02839910	bin
+n02840134	binder, ligature
+n02840245	binder, ring-binder
+n02840515	bindery
+n02840619	binding, book binding, cover, back
+n02841063	bin liner
+n02841187	binnacle
+n02841315	binoculars, field glasses, opera glasses
+n02841506	binocular microscope
+n02841641	biochip
+n02841847	biohazard suit
+n02842133	bioscope
+n02842573	biplane
+n02842809	birch, birch rod
+n02843029	birchbark canoe, birchbark, birch bark
+n02843158	birdbath
+n02843276	birdcage
+n02843465	birdcall
+n02843553	bird feeder, birdfeeder, feeder
+n02843684	birdhouse
+n02843777	bird shot, buckshot, duck shot
+n02843909	biretta, berretta, birretta
+n02844056	bishop
+n02844214	bistro
+n02844307	bit
+n02844714	bit
+n02845130	bite plate, biteplate
+n02845293	bitewing
+n02845985	bitumastic
+n02846141	black
+n02846260	black
+n02846511	blackboard, chalkboard
+n02846619	blackboard eraser
+n02846733	black box
+n02846874	blackface
+n02847461	blackjack, cosh, sap
+n02847631	black tie
+n02847852	blackwash
+n02848118	bladder
+n02848216	blade
+n02848523	blade, vane
+n02848806	blade
+n02848921	blank, dummy, blank shell
+n02849154	blanket, cover
+n02849885	blast furnace
+n02850060	blasting cap
+n02850358	blazer, sport jacket, sport coat, sports jacket, sports coat
+n02850732	blender, liquidizer, liquidiser
+n02850950	blimp, sausage balloon, sausage
+n02851099	blind, screen
+n02851795	blind curve, blind bend
+n02851939	blindfold
+n02852043	bling, bling bling
+n02852173	blinker, flasher
+n02852360	blister pack, bubble pack
+n02853016	block
+n02853218	blockade
+n02853336	blockade-runner
+n02853745	block and tackle
+n02853870	blockbuster
+n02854378	blockhouse
+n02854532	block plane
+n02854630	bloodmobile
+n02854739	bloomers, pants, drawers, knickers
+n02854926	blouse
+n02855089	blower
+n02855390	blowtorch, torch, blowlamp
+n02855701	blucher
+n02855793	bludgeon
+n02855925	blue
+n02856013	blue chip
+n02856237	blunderbuss
+n02856362	blunt file
+n02857365	boarding
+n02857477	boarding house, boardinghouse
+n02857644	boardroom, council chamber
+n02857907	boards
+n02858304	boat
+n02859184	boater, leghorn, Panama, Panama hat, sailor, skimmer, straw hat
+n02859343	boat hook
+n02859443	boathouse
+n02859557	boatswain's chair, bosun's chair
+n02859729	boat train
+n02859955	boatyard
+n02860415	bobbin, spool, reel
+n02860640	bobby pin, hairgrip, grip
+n02860847	bobsled, bobsleigh, bob
+n02861022	bobsled, bobsleigh
+n02861147	bocce ball, bocci ball, boccie ball
+n02861286	bodega
+n02861387	bodice
+n02861509	bodkin, threader
+n02861658	bodkin
+n02861777	bodkin
+n02861886	body
+n02862048	body armor, body armour, suit of armor, suit of armour, coat of mail, cataphract
+n02862916	body lotion
+n02863014	body stocking
+n02863176	body plethysmograph
+n02863340	body pad
+n02863426	bodywork
+n02863536	Bofors gun
+n02863638	bogy, bogie, bogey
+n02863750	boiler, steam boiler
+n02864122	boiling water reactor, BWR
+n02864504	bolero
+n02864593	bollard, bitt
+n02864987	bolo, bolo knife
+n02865351	bolo tie, bolo, bola tie, bola
+n02865665	bolt
+n02865931	bolt, deadbolt
+n02866106	bolt
+n02866386	bolt cutter
+n02866578	bomb
+n02867401	bombazine
+n02867592	bomb calorimeter, bomb
+n02867715	bomber
+n02867966	bomber jacket
+n02868240	bomblet, cluster bomblet
+n02868429	bomb rack
+n02868546	bombshell
+n02868638	bomb shelter, air-raid shelter, bombproof
+n02868975	bone-ash cup, cupel, refractory pot
+n02869155	bone china
+n02869249	bones, castanets, clappers, finger cymbals
+n02869563	boneshaker
+n02869737	bongo, bongo drum
+n02869837	bonnet, poke bonnet
+n02870526	book
+n02870676	book bag
+n02870772	bookbindery
+n02870880	bookcase
+n02871005	bookend
+n02871147	bookmark, bookmarker
+n02871314	bookmobile
+n02871439	bookshelf
+n02871525	bookshop, bookstore, bookstall
+n02871631	boom
+n02871824	boom, microphone boom
+n02871963	boomerang, throwing stick, throw stick
+n02872333	booster, booster rocket, booster unit, takeoff booster, takeoff rocket
+n02872529	booster, booster amplifier, booster station, relay link, relay station, relay transmitter
+n02872752	boot
+n02873520	boot
+n02873623	boot camp
+n02873733	bootee, bootie
+n02873839	booth, cubicle, stall, kiosk
+n02874086	booth
+n02874214	booth
+n02874336	boothose
+n02874442	bootjack
+n02874537	bootlace
+n02874642	bootleg
+n02874750	bootstrap
+n02875436	bore bit, borer, rock drill, stone drill
+n02875626	boron chamber
+n02875948	borstal
+n02876084	bosom
+n02876326	Boston rocker
+n02876457	bota
+n02876657	bottle
+n02877266	bottle, feeding bottle, nursing bottle
+n02877513	bottle bank
+n02877642	bottlebrush
+n02877765	bottlecap
+n02877962	bottle opener
+n02878107	bottling plant
+n02878222	bottom, freighter, merchantman, merchant ship
+n02878425	boucle
+n02878534	boudoir
+n02878628	boulle, boule, buhl
+n02878796	bouncing betty
+n02879087	bouquet, corsage, posy, nosegay
+n02879309	boutique, dress shop
+n02879422	boutonniere
+n02879517	bow
+n02879718	bow
+n02880189	bow, bowknot
+n02880393	bow and arrow
+n02880546	bowed stringed instrument, string
+n02880842	Bowie knife
+n02880940	bowl
+n02881193	bowl
+n02881546	bowl
+n02881757	bowler hat, bowler, derby hat, derby, plug hat
+n02881906	bowline, bowline knot
+n02882190	bowling alley
+n02882301	bowling ball, bowl
+n02882483	bowling equipment
+n02882647	bowling pin, pin
+n02882894	bowling shoe
+n02883004	bowsprit
+n02883101	bowstring
+n02883205	bow tie, bow-tie, bowtie
+n02883344	box
+n02884225	box, loge
+n02884450	box, box seat
+n02884859	box beam, box girder
+n02884994	box camera, box Kodak
+n02885108	boxcar
+n02885233	box coat
+n02885338	boxing equipment
+n02885462	boxing glove, glove
+n02885882	box office, ticket office, ticket booth
+n02886321	box spring
+n02886434	box wrench, box end wrench
+n02886599	brace, bracing
+n02887079	brace, braces, orthodontic braces
+n02887209	brace
+n02887489	brace, suspender, gallus
+n02887832	brace and bit
+n02887970	bracelet, bangle
+n02888270	bracer, armguard
+n02888429	brace wrench
+n02888569	bracket, wall bracket
+n02888898	bradawl, pricker
+n02889425	brake
+n02889646	brake
+n02889856	brake band
+n02889996	brake cylinder, hydraulic brake cylinder, master cylinder
+n02890188	brake disk
+n02890351	brake drum, drum
+n02890513	brake lining
+n02890662	brake pad
+n02890804	brake pedal
+n02890940	brake shoe, shoe, skid
+n02891188	brake system, brakes
+n02891788	brass, brass instrument
+n02892201	brass, memorial tablet, plaque
+n02892304	brass
+n02892392	brassard
+n02892499	brasserie
+n02892626	brassie
+n02892767	brassiere, bra, bandeau
+n02892948	brass knucks, knucks, brass knuckles, knuckles, knuckle duster
+n02893269	brattice
+n02893418	brazier, brasier
+n02893608	breadbasket
+n02893692	bread-bin, breadbox
+n02893941	bread knife
+n02894024	breakable
+n02894158	breakfast area, breakfast nook
+n02894337	breakfast table
+n02894605	breakwater, groin, groyne, mole, bulwark, seawall, jetty
+n02894847	breast drill
+n02895008	breast implant
+n02895154	breastplate, aegis, egis
+n02895328	breast pocket
+n02895438	breathalyzer, breathalyser
+n02896074	breechblock, breech closer
+n02896294	breechcloth, breechclout, loincloth
+n02896442	breeches, knee breeches, knee pants, knickerbockers, knickers
+n02896694	breeches buoy
+n02896856	breechloader
+n02896949	breeder reactor
+n02897097	Bren, Bren gun
+n02897389	brewpub
+n02897820	brick
+n02898093	brickkiln
+n02898173	bricklayer's hammer
+n02898269	brick trowel, mason's trowel
+n02898369	brickwork
+n02898585	bridal gown, wedding gown, wedding dress
+n02898711	bridge, span
+n02899439	bridge, nosepiece
+n02900160	bridle
+n02900459	bridle path, bridle road
+n02900594	bridoon
+n02900705	briefcase
+n02900857	briefcase bomb
+n02900987	briefcase computer
+n02901114	briefs, Jockey shorts
+n02901259	brig
+n02901377	brig
+n02901481	brigandine
+n02901620	brigantine, hermaphrodite brig
+n02901793	brilliantine
+n02901901	brilliant pebble
+n02902079	brim
+n02902687	bristle brush
+n02902816	britches
+n02902916	broad arrow
+n02903006	broadax, broadaxe
+n02903126	brochette
+n02903204	broadcaster, spreader
+n02903727	broadcloth
+n02903852	broadcloth
+n02904109	broad hatchet
+n02904233	broadloom
+n02904505	broadside
+n02904640	broadsword
+n02904803	brocade
+n02904927	brogan, brogue, clodhopper, work shoe
+n02905036	broiler
+n02905152	broken arch
+n02905886	bronchoscope
+n02906734	broom
+n02906963	broom closet
+n02907082	broomstick, broom handle
+n02907296	brougham
+n02907391	Browning automatic rifle, BAR
+n02907656	Browning machine gun, Peacemaker
+n02907873	brownstone
+n02908123	brunch coat
+n02908217	brush
+n02908773	Brussels carpet
+n02908951	Brussels lace
+n02909053	bubble
+n02909165	bubble chamber
+n02909285	bubble jet printer, bubble-jet printer, bubblejet
+n02909706	buckboard
+n02909870	bucket, pail
+n02910145	bucket seat
+n02910241	bucket shop
+n02910353	buckle
+n02910542	buckram
+n02910701	bucksaw
+n02910864	buckskins
+n02910964	buff, buffer
+n02911332	buffer, polisher
+n02911485	buffer, buffer storage, buffer store
+n02912065	buffet, counter, sideboard
+n02912319	buffing wheel
+n02912557	buggy, roadster
+n02912894	bugle
+n02913152	building, edifice
+n02914991	building complex, complex
+n02915904	bulldog clip, alligator clip
+n02916065	bulldog wrench
+n02916179	bulldozer, dozer
+n02916350	bullet, slug
+n02916936	bulletproof vest
+n02917067	bullet train, bullet
+n02917377	bullhorn, loud hailer, loud-hailer
+n02917521	bullion
+n02917607	bullnose, bullnosed plane
+n02917742	bullpen, detention cell, detention centre
+n02917964	bullpen
+n02918112	bullring
+n02918330	bulwark
+n02918455	bumboat
+n02918595	bumper
+n02918831	bumper
+n02918964	bumper car, Dodgem
+n02919148	bumper guard
+n02919308	bumper jack
+n02919414	bundle, sheaf
+n02919648	bung, spile
+n02919792	bungalow, cottage
+n02919890	bungee, bungee cord
+n02919976	bunghole
+n02920083	bunk
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+n02920259	bunk bed, bunk
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+n02920503	bunker, dugout
+n02920658	bunker
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+n02921195	bunting
+n02921292	bur, burr
+n02921406	Burberry
+n02921592	burette, buret
+n02921756	burglar alarm
+n02921884	burial chamber, sepulcher, sepulchre, sepulture
+n02922159	burial garment
+n02922292	burial mound, grave mound, barrow, tumulus
+n02922461	burin
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+n02922798	burlap, gunny
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+n02923129	burner
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+n02923682	burp gun, machine pistol
+n02923915	burr
+n02924116	bus, autobus, coach, charabanc, double-decker, jitney, motorbus, motorcoach, omnibus, passenger vehicle
+n02925009	bushel basket
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+n02925666	buskin, combat boot, desert boot, half boot, top boot
+n02926426	bustier
+n02926591	bustle
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+n02927161	butcher shop, meat market
+n02927764	butter dish
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+n02928049	butter knife
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+n02928413	butt joint, butt
+n02928608	button
+n02929184	buttonhook
+n02929289	buttress, buttressing
+n02929462	butt shaft
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+n02929923	buzz bomb, robot bomb, flying bomb, doodlebug, V-1
+n02930080	buzzer
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+n02930339	bypass condenser, bypass capacitor
+n02930645	byway, bypath, byroad
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+n02931013	cab, cabriolet
+n02931148	cab
+n02931294	cabana
+n02931417	cabaret, nightclub, night club, club, nightspot
+n02931836	caber
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+n02932400	cabin
+n02932523	cabin car, caboose
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+n02933112	cabinet
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+n02933462	cabinet, locker, storage locker
+n02933649	cabinetwork
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+n02934168	cable, line, transmission line
+n02934451	cable car, car
+n02935017	cache, memory cache
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+n02935490	caesium clock
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+n02935891	cafeteria
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+n02936281	caff
+n02936402	caftan, kaftan
+n02936570	caftan, kaftan
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+n02936921	cage
+n02937010	cagoule
+n02937336	caisson
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+n02939763	calico
+n02939866	caliper, calliper
+n02940289	call-board
+n02940385	call center, call centre
+n02940570	caller ID
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+n02942460	camel's hair, camelhair
+n02942699	camera, photographic camera
+n02943241	camera lens, optical lens
+n02943465	camera lucida
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+n02943871	camera tripod
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+n02944256	camlet
+n02944459	camouflage
+n02944579	camouflage, camo
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+n02946270	camp chair
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+n02946509	camper trailer
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+n02948557	candlestick, candle holder
+n02948834	candlewick
+n02948942	candy thermometer
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+n02949202	cane
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+n02950018	cannery
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+n02950186	cannikin
+n02950256	cannon
+n02950482	cannon
+n02950632	cannon
+n02950826	cannon
+n02950943	cannonball, cannon ball, round shot
+n02951358	canoe
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+n02951703	canopic jar, canopic vase
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+n02952109	canopy
+n02952237	canopy
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+n02952485	canteen
+n02952585	canteen
+n02952674	canteen, mobile canteen
+n02952798	canteen
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+n02953197	cantilever bridge
+n02953455	cantle
+n02953552	Canton crepe
+n02953673	canvas, canvass
+n02953850	canvas, canvass
+n02954163	canvas tent, canvas, canvass
+n02954340	cap
+n02954938	cap
+n02955065	cap
+n02955247	capacitor, capacitance, condenser, electrical condenser
+n02955540	caparison, trapping, housing
+n02955767	cape, mantle
+n02956393	capital ship
+n02956699	capitol
+n02956795	cap opener
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+n02957008	capote, hooded coat
+n02957135	cap screw
+n02957252	capstan
+n02957427	capstone, copestone, coping stone, stretcher
+n02957755	capsule
+n02957862	captain's chair
+n02958343	car, auto, automobile, machine, motorcar
+n02959942	car, railcar, railway car, railroad car
+n02960352	car, elevator car
+n02960690	carabiner, karabiner, snap ring
+n02960903	carafe, decanter
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+n02961225	car battery, automobile battery
+n02961451	carbine
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+n02962061	carboy
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+n02962414	car carrier
+n02962843	cardcase
+n02962938	cardiac monitor, heart monitor
+n02963159	cardigan
+n02963302	card index, card catalog, card catalogue
+n02963503	cardiograph, electrocardiograph
+n02963692	cardioid microphone
+n02963821	car door
+n02963987	cardroom
+n02964075	card table
+n02964196	card table
+n02964295	car-ferry
+n02964634	cargo area, cargo deck, cargo hold, hold, storage area
+n02964843	cargo container
+n02964934	cargo door
+n02965024	cargo hatch
+n02965122	cargo helicopter
+n02965216	cargo liner
+n02965300	cargo ship, cargo vessel
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+n02965783	car mirror
+n02966068	caroche
+n02966193	carousel, carrousel, merry-go-round, roundabout, whirligig
+n02966545	carpenter's hammer, claw hammer, clawhammer
+n02966687	carpenter's kit, tool kit
+n02966786	carpenter's level
+n02966942	carpenter's mallet
+n02967081	carpenter's rule
+n02967170	carpenter's square
+n02967294	carpetbag
+n02967407	carpet beater, rug beater
+n02967540	carpet loom
+n02967626	carpet pad, rug pad, underlay, underlayment
+n02967782	carpet sweeper, sweeper
+n02967991	carpet tack
+n02968074	carport, car port
+n02968210	carrack, carack
+n02968333	carrel, carrell, cubicle, stall
+n02968473	carriage, equipage, rig
+n02969010	carriage
+n02969163	carriage bolt
+n02969323	carriageway
+n02969527	carriage wrench
+n02969634	carrick bend
+n02969886	carrier
+n02970408	carryall, holdall, tote, tote bag
+n02970534	carrycot
+n02970685	car seat
+n02970849	cart
+n02971167	car tire, automobile tire, auto tire, rubber tire
+n02971356	carton
+n02971473	cartouche, cartouch
+n02971579	car train
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+n02971940	cartridge, pickup
+n02972397	cartridge belt
+n02972714	cartridge extractor, cartridge remover, extractor
+n02972934	cartridge fuse
+n02973017	cartridge holder, cartridge clip, clip, magazine
+n02973236	cartwheel
+n02973805	carving fork
+n02973904	carving knife
+n02974003	car wheel
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+n02974454	cascade liquefier
+n02974565	cascade transformer
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+n02975212	case, display case, showcase, vitrine
+n02975589	case, compositor's case, typesetter's case
+n02975994	casein paint, casein
+n02976123	case knife, sheath knife
+n02976249	case knife
+n02976350	casement
+n02976455	casement window
+n02976552	casern
+n02976641	case shot, canister, canister shot
+n02976815	cash bar
+n02976939	cashbox, money box, till
+n02977058	cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM
+n02977330	cashmere
+n02977438	cash register, register
+n02977619	casing, case
+n02977936	casino, gambling casino
+n02978055	casket, jewel casket
+n02978205	casque
+n02978367	casquet, casquetel
+n02978478	Cassegrainian telescope, Gregorian telescope
+n02978753	casserole
+n02978881	cassette
+n02979074	cassette deck
+n02979186	cassette player
+n02979290	cassette recorder
+n02979399	cassette tape
+n02979516	cassock
+n02979836	cast, plaster cast, plaster bandage
+n02980036	caster, castor
+n02980203	caster, castor
+n02980441	castle
+n02980625	castle, rook
+n02981024	catacomb
+n02981198	catafalque
+n02981321	catalytic converter
+n02981565	catalytic cracker, cat cracker
+n02981792	catamaran
+n02981911	catapult, arbalest, arbalist, ballista, bricole, mangonel, onager, trebuchet, trebucket
+n02982232	catapult, launcher
+n02982416	catboat
+n02982515	cat box
+n02982599	catch
+n02983072	catchall
+n02983189	catcher's mask
+n02983357	catchment
+n02983507	Caterpillar, cat
+n02983904	cathedra, bishop's throne
+n02984061	cathedral
+n02984203	cathedral, duomo
+n02984469	catheter
+n02984699	cathode
+n02985137	cathode-ray tube, CRT
+n02985606	cat-o'-nine-tails, cat
+n02985828	cat's-paw
+n02985963	catsup bottle, ketchup bottle
+n02986066	cattle car
+n02986160	cattle guard, cattle grid
+n02986348	cattleship, cattle boat
+n02987047	cautery, cauterant
+n02987379	cavalier hat, slouch hat
+n02987492	cavalry sword, saber, sabre
+n02987706	cavetto
+n02987823	cavity wall
+n02987950	C battery
+n02988066	C-clamp
+n02988156	CD drive
+n02988304	CD player
+n02988486	CD-R, compact disc recordable, CD-WO, compact disc write-once
+n02988679	CD-ROM, compact disc read-only memory
+n02988963	CD-ROM drive
+n02989099	cedar chest
+n02990373	ceiling
+n02990758	celesta
+n02991048	cell, electric cell
+n02991302	cell, jail cell, prison cell
+n02991847	cellar, wine cellar
+n02992032	cellblock, ward
+n02992211	cello, violoncello
+n02992368	cellophane
+n02992529	cellular telephone, cellular phone, cellphone, cell, mobile phone
+n02992795	cellulose tape, Scotch tape, Sellotape
+n02993194	cenotaph, empty tomb
+n02993368	censer, thurible
+n02993546	center, centre
+n02994573	center punch
+n02994743	Centigrade thermometer
+n02995345	central processing unit, CPU, C.P.U., central processor, processor, mainframe
+n02995871	centrifugal pump
+n02995998	centrifuge, extractor, separator
+n02997391	ceramic
+n02997607	ceramic ware
+n02997910	cereal bowl
+n02998003	cereal box
+n02998107	cerecloth
+n02998563	cesspool, cesspit, sink, sump
+n02998696	chachka, tsatske, tshatshke, tchotchke
+n02998841	chador, chadar, chaddar, chuddar
+n02999138	chafing dish
+n02999410	chain
+n02999936	chain
+n03000134	chainlink fence
+n03000247	chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour
+n03000530	chain printer
+n03000684	chain saw, chainsaw
+n03001115	chain store
+n03001282	chain tongs
+n03001540	chain wrench
+n03001627	chair
+n03002096	chair
+n03002210	chair of state
+n03002341	chairlift, chair lift
+n03002555	chaise, shay
+n03002711	chaise longue, chaise, daybed
+n03002816	chalet
+n03002948	chalice, goblet
+n03003091	chalk
+n03003633	challis
+n03004275	chamberpot, potty, thunder mug
+n03004409	chambray
+n03004531	chamfer bit
+n03004620	chamfer plane
+n03004713	chamois cloth
+n03004824	chancel, sanctuary, bema
+n03005033	chancellery
+n03005147	chancery
+n03005285	chandelier, pendant, pendent
+n03005515	chandlery
+n03005619	chanfron, chamfron, testiere, frontstall, front-stall
+n03006626	chanter, melody pipe
+n03006788	chantry
+n03006903	chap
+n03007130	chapel
+n03007297	chapterhouse, fraternity house, frat house
+n03007444	chapterhouse
+n03007591	character printer, character-at-a-time printer, serial printer
+n03008177	charcuterie
+n03008817	charge-exchange accelerator
+n03008976	charger, battery charger
+n03009111	chariot
+n03009269	chariot
+n03009794	charnel house, charnel
+n03010473	chassis
+n03010656	chassis
+n03010795	chasuble
+n03010915	chateau
+n03011018	chatelaine
+n03011355	checker, chequer
+n03011741	checkout, checkout counter
+n03012013	cheekpiece
+n03012159	cheeseboard, cheese tray
+n03012373	cheesecloth
+n03012499	cheese cutter
+n03012644	cheese press
+n03012734	chemical bomb, gas bomb
+n03012897	chemical plant
+n03013006	chemical reactor
+n03013438	chemise, sack, shift
+n03013580	chemise, shimmy, shift, slip, teddy
+n03013850	chenille
+n03014440	chessman, chess piece
+n03014705	chest
+n03015149	chesterfield
+n03015254	chest of drawers, chest, bureau, dresser
+n03015478	chest protector
+n03015631	cheval-de-frise, chevaux-de-frise
+n03015851	cheval glass
+n03016209	chicane
+n03016389	chicken coop, coop, hencoop, henhouse
+n03016609	chicken wire
+n03016737	chicken yard, hen yard, chicken run, fowl run
+n03016868	chiffon
+n03016953	chiffonier, commode
+n03017070	child's room
+n03017168	chime, bell, gong
+n03017698	chimney breast
+n03017835	chimney corner, inglenook
+n03018209	china
+n03018349	china cabinet, china closet
+n03018614	chinchilla
+n03018712	Chinese lantern
+n03018848	Chinese puzzle
+n03019198	chinning bar
+n03019304	chino
+n03019434	chino
+n03019685	chin rest
+n03019806	chin strap
+n03019938	chintz
+n03020034	chip, microchip, micro chip, silicon chip, microprocessor chip
+n03020416	chip, poker chip
+n03020692	chisel
+n03021228	chlamys
+n03024064	choir
+n03024233	choir loft
+n03024333	choke
+n03024518	choke, choke coil, choking coil
+n03025070	chokey, choky
+n03025165	choo-choo
+n03025250	chopine, platform
+n03025886	chordophone
+n03026506	Christmas stocking
+n03026907	chronograph
+n03027001	chronometer
+n03027108	chronoscope
+n03027250	chuck
+n03027505	chuck wagon
+n03027625	chukka, chukka boot
+n03028079	church, church building
+n03028596	church bell
+n03028785	church hat
+n03029066	church key
+n03029197	church tower
+n03029296	churidars
+n03029445	churn, butter churn
+n03029925	ciderpress
+n03030262	cigar band
+n03030353	cigar box
+n03030557	cigar cutter
+n03030880	cigarette butt
+n03031012	cigarette case
+n03031152	cigarette holder
+n03031422	cigar lighter, cigarette lighter, pocket lighter
+n03031756	cinch, girth
+n03032252	cinema, movie theater, movie theatre, movie house, picture palace
+n03032453	cinquefoil
+n03032811	circle, round
+n03033267	circlet
+n03033362	circuit, electrical circuit, electric circuit
+n03033986	circuit board, circuit card, board, card, plug-in, add-in
+n03034244	circuit breaker, breaker
+n03034405	circuitry
+n03034516	circular plane, compass plane
+n03034663	circular saw, buzz saw
+n03035252	circus tent, big top, round top, top
+n03035510	cistern
+n03035715	cistern, water tank
+n03035832	cittern, cithern, cither, citole, gittern
+n03036022	city hall
+n03036149	cityscape
+n03036244	city university
+n03036341	civies, civvies
+n03036469	civilian clothing, civilian dress, civilian garb, plain clothes
+n03036701	clack valve, clack, clapper valve
+n03036866	clamp, clinch
+n03037108	clamshell, grapple
+n03037228	clapper, tongue
+n03037404	clapperboard
+n03037590	clarence
+n03037709	clarinet
+n03038041	Clark cell, Clark standard cell
+n03038281	clasp
+n03038480	clasp knife, jackknife
+n03038685	classroom, schoolroom
+n03038870	clavichord
+n03039015	clavier, Klavier
+n03039259	clay pigeon
+n03039353	claymore mine, claymore
+n03039493	claymore
+n03039827	cleaners, dry cleaners
+n03039947	cleaning implement, cleaning device, cleaning equipment
+n03040229	cleaning pad
+n03040376	clean room, white room
+n03040836	clearway
+n03041114	cleat
+n03041265	cleat
+n03041449	cleats
+n03041632	cleaver, meat cleaver, chopper
+n03041810	clerestory, clearstory
+n03042139	clevis
+n03042384	clews
+n03042490	cliff dwelling
+n03042697	climbing frame
+n03042829	clinch
+n03042984	clinch, clench
+n03043173	clincher
+n03043274	clinic
+n03043423	clinical thermometer, mercury-in-glass clinical thermometer
+n03043693	clinker, clinker brick
+n03043798	clinometer, inclinometer
+n03043958	clip
+n03044671	clip lead
+n03044801	clip-on
+n03044934	clipper
+n03045074	clipper
+n03045228	clipper, clipper ship
+n03045337	cloak
+n03045698	cloak
+n03045800	cloakroom, coatroom
+n03046029	cloche
+n03046133	cloche
+n03046257	clock
+n03046802	clock pendulum
+n03046921	clock radio
+n03047052	clock tower
+n03047171	clockwork
+n03047690	clog, geta, patten, sabot
+n03047799	cloisonne
+n03047941	cloister
+n03048883	closed circuit, loop
+n03049066	closed-circuit television
+n03049326	closed loop, closed-loop system
+n03049457	closet
+n03049782	closeup lens
+n03049924	cloth cap, flat cap
+n03050026	cloth covering
+n03050453	clothesbrush
+n03050546	clothes closet, clothespress
+n03050655	clothes dryer, clothes drier
+n03050864	clothes hamper, laundry basket, clothes basket, voider
+n03051041	clotheshorse
+n03051249	clothespin, clothes pin, clothes peg
+n03051396	clothes tree, coat tree, coat stand
+n03051540	clothing, article of clothing, vesture, wear, wearable, habiliment
+n03052464	clothing store, haberdashery, haberdashery store, mens store
+n03052917	clout nail, clout
+n03053047	clove hitch
+n03053976	club car, lounge car
+n03054491	clubroom
+n03054605	cluster bomb
+n03054901	clutch
+n03055159	clutch, clutch pedal
+n03055418	clutch bag, clutch
+n03055670	coach, four-in-hand, coach-and-four
+n03055857	coach house, carriage house, remise
+n03056097	coal car
+n03056215	coal chute
+n03056288	coal house
+n03056493	coal shovel
+n03056583	coaming
+n03056873	coaster brake
+n03057021	coat
+n03057541	coat button
+n03057636	coat closet
+n03057724	coatdress
+n03057841	coatee
+n03057920	coat hanger, clothes hanger, dress hanger
+n03058107	coating, coat
+n03058603	coating
+n03058949	coat of paint
+n03059103	coatrack, coat rack, hatrack
+n03059236	coattail
+n03059366	coaxial cable, coax, coax cable
+n03059685	cobweb
+n03059934	cobweb
+n03060728	Cockcroft and Walton accelerator, Cockcroft-Walton accelerator, Cockcroft and Walton voltage multiplier, Cockcroft-Walton voltage multiplier
+n03061050	cocked hat
+n03061211	cockhorse
+n03061345	cockleshell
+n03061505	cockpit
+n03061674	cockpit
+n03061819	cockpit
+n03061893	cockscomb, coxcomb
+n03062015	cocktail dress, sheath
+n03062122	cocktail lounge
+n03062245	cocktail shaker
+n03062336	cocotte
+n03062651	codpiece
+n03062798	coelostat
+n03062985	coffee can
+n03063073	coffee cup
+n03063199	coffee filter
+n03063338	coffee maker
+n03063485	coffee mill, coffee grinder
+n03063599	coffee mug
+n03063689	coffeepot
+n03063834	coffee stall
+n03063968	coffee table, cocktail table
+n03064250	coffee urn
+n03064350	coffer
+n03064562	Coffey still
+n03064758	coffin, casket
+n03064935	cog, sprocket
+n03065243	coif
+n03065424	coil, spiral, volute, whorl, helix
+n03065708	coil
+n03066232	coil
+n03066359	coil spring, volute spring
+n03066464	coin box
+n03066849	colander, cullender
+n03067093	cold cathode
+n03067212	cold chisel, set chisel
+n03067339	cold cream, coldcream, face cream, vanishing cream
+n03067518	cold frame
+n03068181	collar, neckband
+n03068998	collar
+n03069752	college
+n03070059	collet, collet chuck
+n03070193	collider
+n03070396	colliery, pit
+n03070587	collimator
+n03070854	collimator
+n03071021	cologne, cologne water, eau de cologne
+n03071160	colonnade
+n03071288	colonoscope
+n03071552	colorimeter, tintometer
+n03072056	colors, colours
+n03072201	color television, colour television, color television system, colour television system, color TV, colour TV
+n03072440	color tube, colour tube, color television tube, colour television tube, color TV tube, colour TV tube
+n03072682	color wash, colour wash
+n03073296	Colt
+n03073384	colter, coulter
+n03073545	columbarium
+n03073694	columbarium, cinerarium
+n03073977	column, pillar
+n03074380	column, pillar
+n03074855	comb
+n03075097	comb
+n03075248	comber
+n03075370	combination lock
+n03075500	combination plane
+n03075634	combine
+n03075768	comforter, pacifier, baby's dummy, teething ring
+n03075946	command module
+n03076411	commissary
+n03076623	commissary
+n03076708	commodity, trade good, good
+n03077442	common ax, common axe, Dayton ax, Dayton axe
+n03077616	common room
+n03077741	communications satellite
+n03078287	communication system
+n03078506	community center, civic center
+n03078670	commutator
+n03078802	commuter, commuter train
+n03078995	compact, powder compact
+n03079136	compact, compact car
+n03079230	compact disk, compact disc, CD
+n03079494	compact-disk burner, CD burner
+n03079616	companionway
+n03079741	compartment
+n03080309	compartment
+n03080497	compass
+n03080633	compass
+n03080731	compass card, mariner's compass
+n03080904	compass saw
+n03081859	compound
+n03081986	compound lens
+n03082127	compound lever
+n03082280	compound microscope
+n03082450	compress
+n03082656	compression bandage, tourniquet
+n03082807	compressor
+n03082979	computer, computing machine, computing device, data processor, electronic computer, information processing system
+n03084420	computer circuit
+n03084834	computerized axial tomography scanner, CAT scanner
+n03085013	computer keyboard, keypad
+n03085219	computer monitor
+n03085333	computer network
+n03085602	computer screen, computer display
+n03085781	computer store
+n03085915	computer system, computing system, automatic data processing system, ADP system, ADPS
+n03086183	concentration camp, stockade
+n03086457	concert grand, concert piano
+n03086580	concert hall
+n03086670	concertina
+n03086868	concertina
+n03087069	concrete mixer, cement mixer
+n03087245	condensation pump, diffusion pump
+n03087366	condenser, optical condenser
+n03087521	condenser
+n03087643	condenser
+n03087816	condenser microphone, capacitor microphone
+n03088389	condominium
+n03088580	condominium, condo
+n03088707	conductor
+n03089477	cone clutch, cone friction clutch
+n03089624	confectionery, confectionary, candy store
+n03089753	conference center, conference house
+n03089879	conference room
+n03090000	conference table, council table, council board
+n03090172	confessional
+n03090437	conformal projection, orthomorphic projection
+n03090710	congress boot, congress shoe, congress gaiter
+n03090856	conic projection, conical projection
+n03091044	connecting rod
+n03091223	connecting room
+n03091374	connection, connexion, connector, connecter, connective
+n03091907	conning tower
+n03092053	conning tower
+n03092166	conservatory, hothouse, indoor garden
+n03092314	conservatory, conservatoire
+n03092476	console
+n03092656	console
+n03092883	console table, console
+n03093427	consulate
+n03093792	contact, tangency
+n03094159	contact, contact lens
+n03094503	container
+n03095699	container ship, containership, container vessel
+n03095965	containment
+n03096439	contrabassoon, contrafagotto, double bassoon
+n03096960	control, controller
+n03097362	control center
+n03097535	control circuit, negative feedback circuit
+n03097673	control key, command key
+n03098140	control panel, instrument panel, control board, board, panel
+n03098515	control rod
+n03098688	control room
+n03098806	control system
+n03098959	control tower
+n03099147	convector
+n03099274	convenience store
+n03099454	convent
+n03099622	conventicle, meetinghouse
+n03099771	converging lens, convex lens
+n03099945	converter, convertor
+n03100240	convertible
+n03100346	convertible, sofa bed
+n03100490	conveyance, transport
+n03100897	conveyer belt, conveyor belt, conveyer, conveyor, transporter
+n03101156	cooker
+n03101302	cookfire
+n03101375	cookhouse
+n03101517	cookie cutter
+n03101664	cookie jar, cooky jar
+n03101796	cookie sheet, baking tray
+n03101986	cooking utensil, cookware
+n03102371	cookstove
+n03102516	coolant system
+n03102654	cooler, ice chest
+n03102859	cooling system, cooling
+n03103128	cooling system, engine cooling system
+n03103396	cooling tower
+n03103563	coonskin cap, coonskin
+n03103904	cope
+n03104019	coping saw
+n03104512	copperware
+n03105088	copyholder
+n03105214	coquille
+n03105306	coracle
+n03105467	corbel, truss
+n03105645	corbel arch
+n03105810	corbel step, corbie-step, corbiestep, crow step
+n03105974	corbie gable
+n03106722	cord, corduroy
+n03106898	cord, electric cord
+n03107046	cordage
+n03107488	cords, corduroys
+n03107716	core
+n03108455	core bit
+n03108624	core drill
+n03108759	corer
+n03108853	cork, bottle cork
+n03109033	corker
+n03109150	corkscrew, bottle screw
+n03109253	corncrib
+n03109693	corner, quoin
+n03109881	corner, nook
+n03110202	corner post
+n03110669	cornet, horn, trumpet, trump
+n03111041	cornice
+n03111177	cornice
+n03111296	cornice, valance, valance board, pelmet
+n03111690	correctional institution
+n03112240	corrugated fastener, wiggle nail
+n03112719	corselet, corslet
+n03112869	corset, girdle, stays
+n03113152	cosmetic
+n03113505	cosmotron
+n03113657	costume
+n03113835	costume
+n03114041	costume
+n03114236	costume
+n03114379	cosy, tea cosy, cozy, tea cozy
+n03114504	cot, camp bed
+n03114743	cottage tent
+n03114839	cotter, cottar
+n03115014	cotter pin
+n03115180	cotton
+n03115400	cotton flannel, Canton flannel
+n03115663	cotton mill
+n03115762	couch
+n03115897	couch
+n03116008	couchette
+n03116163	coude telescope, coude system
+n03116530	counter
+n03116767	counter, tabulator
+n03117199	counter
+n03117642	counterbore, countersink, countersink bit
+n03118346	counter tube
+n03118969	country house
+n03119203	country store, general store, trading post
+n03119396	coupe
+n03119510	coupling, coupler
+n03120198	court, courtyard
+n03120491	court
+n03120778	court, courtroom
+n03121040	court
+n03121190	Courtelle
+n03121298	courthouse
+n03121431	courthouse
+n03121897	coverall
+n03122073	covered bridge
+n03122202	covered couch
+n03122295	covered wagon, Conestoga wagon, Conestoga, prairie wagon, prairie schooner
+n03122748	covering
+n03123553	coverlet
+n03123666	cover plate
+n03123809	cowbarn, cowshed, cow barn, cowhouse, byre
+n03123917	cowbell
+n03124043	cowboy boot
+n03124170	cowboy hat, ten-gallon hat
+n03124313	cowhide
+n03124474	cowl
+n03124590	cow pen, cattle pen, corral
+n03125057	CPU board, mother board
+n03125588	crackle, crackleware, crackle china
+n03125729	cradle
+n03125870	craft
+n03126090	cramp, cramp iron
+n03126385	crampon, crampoon, climbing iron, climber
+n03126580	crampon, crampoon
+n03126707	crane
+n03126927	craniometer
+n03127024	crank, starter
+n03127203	crankcase
+n03127408	crankshaft
+n03127531	crash barrier
+n03127747	crash helmet
+n03127925	crate
+n03128085	cravat
+n03128248	crayon, wax crayon
+n03128427	crazy quilt
+n03128519	cream, ointment, emollient
+n03129001	cream pitcher, creamer
+n03129471	creche, foundling hospital
+n03129636	creche
+n03129753	credenza, credence
+n03129848	creel
+n03130066	crematory, crematorium, cremation chamber
+n03130233	crematory, crematorium
+n03130563	crepe, crape
+n03130761	crepe de Chine
+n03130866	crescent wrench
+n03131193	cretonne
+n03131574	crib, cot
+n03131669	crib
+n03131967	cricket ball
+n03132076	cricket bat, bat
+n03132261	cricket equipment
+n03132438	cringle, eyelet, loop, grommet, grummet
+n03132666	crinoline
+n03132776	crinoline
+n03133050	crochet needle, crochet hook
+n03133415	crock, earthenware jar
+n03133878	Crock Pot
+n03134118	crook, shepherd's crook
+n03134232	Crookes radiometer
+n03134394	Crookes tube
+n03134739	croquet ball
+n03134853	croquet equipment
+n03135030	croquet mallet
+n03135532	cross
+n03135656	crossbar
+n03135788	crossbar
+n03135917	crossbar
+n03136051	crossbench
+n03136254	cross bit
+n03136369	crossbow
+n03136504	crosscut saw, crosscut handsaw, cutoff saw
+n03137473	crossjack, mizzen course
+n03137579	crosspiece
+n03138128	crotchet
+n03138217	croupier's rake
+n03138344	crowbar, wrecking bar, pry, pry bar
+n03138669	crown, diadem
+n03139089	crown, crownwork, jacket, jacket crown, cap
+n03139464	crown jewels
+n03139640	crown lens
+n03139998	crow's nest
+n03140126	crucible, melting pot
+n03140292	crucifix, rood, rood-tree
+n03140431	cruet, crewet
+n03140546	cruet-stand
+n03140652	cruise control
+n03140771	cruise missile
+n03140900	cruiser
+n03141065	cruiser, police cruiser, patrol car, police car, prowl car, squad car
+n03141327	cruise ship, cruise liner
+n03141455	crupper
+n03141612	cruse
+n03141702	crusher
+n03141823	crutch
+n03142099	cryometer
+n03142205	cryoscope
+n03142325	cryostat
+n03142431	crypt
+n03142679	crystal, watch crystal, watch glass
+n03143400	crystal detector
+n03143572	crystal microphone
+n03143754	crystal oscillator, quartz oscillator
+n03144156	crystal set
+n03144873	cubitiere
+n03144982	cucking stool, ducking stool
+n03145147	cuckoo clock
+n03145277	cuddy
+n03145384	cudgel
+n03145522	cue, cue stick, pool cue, pool stick
+n03145719	cue ball
+n03145843	cuff, turnup
+n03146219	cuirass
+n03146342	cuisse
+n03146449	cul, cul de sac, dead end
+n03146560	culdoscope
+n03146687	cullis
+n03146777	culotte
+n03146846	cultivator, tiller
+n03147084	culverin
+n03147156	culverin
+n03147280	culvert
+n03147509	cup
+n03148324	cupboard, closet
+n03148518	cup hook
+n03148727	cupola
+n03148808	cupola
+n03149135	curb, curb bit
+n03149401	curb roof
+n03149686	curbstone, kerbstone
+n03149810	curette, curet
+n03150232	curler, hair curler, roller, crimper
+n03150511	curling iron
+n03150661	currycomb
+n03150795	cursor, pointer
+n03151077	curtain, drape, drapery, mantle, pall
+n03152303	customhouse, customshouse
+n03152951	cutaway, cutaway drawing, cutaway model
+n03153246	cutlas, cutlass
+n03153585	cutoff
+n03153948	cutout
+n03154073	cutter, cutlery, cutting tool
+n03154316	cutter
+n03154446	cutting implement
+n03154616	cutting room
+n03154745	cutty stool
+n03154895	cutwork
+n03155178	cybercafe
+n03155502	cyclopean masonry
+n03155915	cyclostyle
+n03156071	cyclotron
+n03156279	cylinder
+n03156405	cylinder, piston chamber
+n03156767	cylinder lock
+n03157348	cymbal
+n03158186	dacha
+n03158414	Dacron, Terylene
+n03158668	dado
+n03158796	dado plane
+n03158885	dagger, sticker
+n03159535	dairy, dairy farm
+n03159640	dais, podium, pulpit, rostrum, ambo, stump, soapbox
+n03160001	daisy print wheel, daisy wheel
+n03160186	daisywheel printer
+n03160309	dam, dike, dyke
+n03160740	damask
+n03161016	dampener, moistener
+n03161450	damper, muffler
+n03161893	damper block, piano damper
+n03162297	dark lantern, bull's-eye
+n03162460	darkroom
+n03162556	darning needle, embroidery needle
+n03162714	dart
+n03162818	dart
+n03163222	dashboard, fascia
+n03163381	dashiki, daishiki
+n03163488	dash-pot
+n03163798	data converter
+n03163973	data input device, input device
+n03164192	data multiplexer
+n03164344	data system, information system
+n03164605	davenport
+n03164722	davenport
+n03164929	davit
+n03165096	daybed, divan bed
+n03165211	daybook, ledger
+n03165466	day nursery, day care center
+n03165616	day school
+n03165823	dead axle
+n03165955	deadeye
+n03166120	deadhead
+n03166514	deanery
+n03166600	deathbed
+n03166685	death camp
+n03166809	death house, death row
+n03166951	death knell, death bell
+n03167153	death seat
+n03167978	deck
+n03168107	deck
+n03168217	deck chair, beach chair
+n03168543	deck-house
+n03168663	deckle
+n03168774	deckle edge, deckle
+n03168933	declinometer, transit declinometer
+n03169063	decoder
+n03169176	decolletage
+n03170292	decoupage
+n03170459	dedicated file server
+n03170635	deep-freeze, Deepfreeze, deep freezer, freezer
+n03170872	deerstalker
+n03171228	defense system, defence system
+n03171356	defensive structure, defense, defence
+n03171635	defibrillator
+n03171910	defilade
+n03172038	deflector
+n03172738	delayed action
+n03172965	delay line
+n03173270	delft
+n03173387	delicatessen, deli, food shop
+n03173929	delivery truck, delivery van, panel truck
+n03174079	delta wing
+n03174450	demijohn
+n03174731	demitasse
+n03175081	den
+n03175189	denim, dungaree, jean
+n03175301	densimeter, densitometer
+n03175457	densitometer
+n03175604	dental appliance
+n03175843	dental floss, floss
+n03175983	dental implant
+n03176238	dentist's drill, burr drill
+n03176386	denture, dental plate, plate
+n03176594	deodorant, deodourant
+n03176763	department store, emporium
+n03177059	departure lounge
+n03177165	depilatory, depilator, epilator
+n03177708	depressor
+n03178000	depth finder
+n03178173	depth gauge, depth gage
+n03178430	derrick
+n03178538	derrick
+n03178674	derringer
+n03179701	desk
+n03179910	desk phone
+n03180011	desktop computer
+n03180384	dessert spoon
+n03180504	destroyer, guided missile destroyer
+n03180732	destroyer escort
+n03180865	detached house, single dwelling
+n03180969	detector, sensor, sensing element
+n03181293	detector
+n03181667	detention home, detention house, house of detention, detention camp
+n03182140	detonating fuse
+n03182232	detonator, detonating device, cap
+n03182912	developer
+n03183080	device
+n03185868	Dewar flask, Dewar
+n03186199	dhoti
+n03186285	dhow
+n03186818	dial, telephone dial
+n03187037	dial
+n03187153	dial
+n03187268	dialog box, panel
+n03187595	dial telephone, dial phone
+n03187751	dialyzer, dialysis machine
+n03188290	diamante
+n03188531	diaper, nappy, napkin
+n03188725	diaper
+n03188871	diaphone
+n03189083	diaphragm, stop
+n03189311	diaphragm
+n03189818	diathermy machine
+n03190458	dibble, dibber
+n03191286	dice cup, dice box
+n03191451	dicer
+n03191561	dickey, dickie, dicky, shirtfront
+n03191776	dickey, dickie, dicky, dickey-seat, dickie-seat, dicky-seat
+n03192543	Dictaphone
+n03192907	die
+n03193107	diesel, diesel engine, diesel motor
+n03193260	diesel-electric locomotive, diesel-electric
+n03193423	diesel-hydraulic locomotive, diesel-hydraulic
+n03193597	diesel locomotive
+n03193754	diestock
+n03194170	differential analyzer
+n03194297	differential gear, differential
+n03194812	diffuser, diffusor
+n03194992	diffuser, diffusor
+n03195332	digester
+n03195485	diggings, digs, domiciliation, lodgings, pad
+n03195799	digital-analog converter, digital-to-analog converter
+n03195959	digital audiotape, DAT
+n03196062	digital camera
+n03196217	digital clock
+n03196324	digital computer
+n03196598	digital display, alphanumeric display
+n03196990	digital subscriber line, DSL
+n03197201	digital voltmeter
+n03197337	digital watch
+n03197446	digitizer, digitiser, analog-digital converter, analog-to-digital converter
+n03198223	dilator, dilater
+n03198500	dildo
+n03199358	dimity
+n03199488	dimmer
+n03199647	diner
+n03199775	dinette
+n03199901	dinghy, dory, rowboat
+n03200231	dining area
+n03200357	dining car, diner, dining compartment, buffet car
+n03200539	dining-hall
+n03200701	dining room, dining-room
+n03200906	dining-room furniture
+n03201035	dining-room table
+n03201208	dining table, board
+n03201529	dinner bell
+n03201638	dinner dress, dinner gown, formal, evening gown
+n03201776	dinner jacket, tux, tuxedo, black tie
+n03201895	dinner napkin
+n03201996	dinner pail, dinner bucket
+n03202354	dinner table
+n03202481	dinner theater, dinner theatre
+n03202760	diode, semiconductor diode, junction rectifier, crystal rectifier
+n03202940	diode, rectifying tube, rectifying valve
+n03203089	dip
+n03203806	diplomatic building
+n03204134	dipole, dipole antenna
+n03204306	dipper
+n03204436	dipstick
+n03204558	DIP switch, dual inline package switch
+n03204955	directional antenna
+n03205143	directional microphone
+n03205304	direction finder
+n03205458	dirk
+n03205574	dirndl
+n03205669	dirndl
+n03205903	dirty bomb
+n03206023	discharge lamp
+n03206158	discharge pipe
+n03206282	disco, discotheque
+n03206405	discount house, discount store, discounter, wholesale house
+n03206602	discus, saucer
+n03206718	disguise
+n03206908	dish
+n03207305	dish, dish aerial, dish antenna, saucer
+n03207548	dishpan
+n03207630	dish rack
+n03207743	dishrag, dishcloth
+n03207835	dishtowel, dish towel, tea towel
+n03207941	dishwasher, dish washer, dishwashing machine
+n03208556	disk, disc
+n03208938	disk brake, disc brake
+n03209359	disk clutch
+n03209477	disk controller
+n03209666	disk drive, disc drive, hard drive, Winchester drive
+n03209910	diskette, floppy, floppy disk
+n03210245	disk harrow, disc harrow
+n03210372	dispatch case, dispatch box
+n03210552	dispensary
+n03210683	dispenser
+n03211117	display, video display
+n03211413	display adapter, display adaptor
+n03211616	display panel, display board, board
+n03211789	display window, shop window, shopwindow, show window
+n03212114	disposal, electric pig, garbage disposal
+n03212247	disrupting explosive, bursting explosive
+n03212406	distaff
+n03212811	distillery, still
+n03213014	distributor, distributer, electrical distributor
+n03213361	distributor cam
+n03213538	distributor cap
+n03213715	distributor housing
+n03213826	distributor point, breaker point, point
+n03214253	ditch
+n03214450	ditch spade, long-handled spade
+n03214582	ditty bag
+n03214966	divan
+n03215076	divan, diwan
+n03215191	dive bomber
+n03215337	diverging lens, concave lens
+n03215508	divided highway, dual carriageway
+n03215749	divider
+n03215930	diving bell
+n03216199	divining rod, dowser, dowsing rod, waterfinder, water finder
+n03216402	diving suit, diving dress
+n03216562	dixie
+n03216710	Dixie cup, paper cup
+n03216828	dock, dockage, docking facility
+n03217653	doeskin
+n03217739	dogcart
+n03217889	doggie bag, doggy bag
+n03218198	dogsled, dog sled, dog sleigh
+n03218446	dog wrench
+n03219010	doily, doyley, doyly
+n03219135	doll, dolly
+n03219483	dollhouse, doll's house
+n03219612	dolly
+n03219859	dolman
+n03219966	dolman, dolman jacket
+n03220095	dolman sleeve
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+n03220513	dome
+n03220692	dome, domed stadium, covered stadium
+n03221059	domino, half mask, eye mask
+n03221351	dongle
+n03221540	donkey jacket
+n03221720	door
+n03222176	door
+n03222318	door
+n03222516	doorbell, bell, buzzer
+n03222722	doorframe, doorcase
+n03222857	doorjamb, doorpost
+n03223162	doorlock
+n03223299	doormat, welcome mat
+n03223441	doornail
+n03223553	doorplate
+n03223686	doorsill, doorstep, threshold
+n03223923	doorstop, doorstopper
+n03224490	Doppler radar
+n03224603	dormer, dormer window
+n03224753	dormer window
+n03224893	dormitory, dorm, residence hall, hall, student residence
+n03225108	dormitory, dormitory room, dorm room
+n03225458	dosemeter, dosimeter
+n03225616	dossal, dossel
+n03225777	dot matrix printer, matrix printer, dot printer
+n03225988	double bed
+n03226090	double-bitted ax, double-bitted axe, Western ax, Western axe
+n03226254	double boiler, double saucepan
+n03226375	double-breasted jacket
+n03226538	double-breasted suit
+n03226880	double door
+n03227010	double glazing
+n03227184	double-hung window
+n03227317	double knit
+n03227721	doubler
+n03227856	double reed
+n03228016	double-reed instrument, double reed
+n03228254	doublet
+n03228365	doubletree
+n03228533	douche, douche bag
+n03228692	dovecote, columbarium, columbary
+n03228796	Dover's powder
+n03228967	dovetail, dovetail joint
+n03229115	dovetail plane
+n03229244	dowel, dowel pin, joggle
+n03229526	downstage
+n03231160	drafting instrument
+n03231368	drafting table, drawing table
+n03231819	Dragunov
+n03232309	drainage ditch
+n03232417	drainage system
+n03232543	drain basket
+n03232815	drainplug
+n03232923	drape
+n03233123	drapery
+n03233624	drawbar
+n03233744	drawbridge, lift bridge
+n03233905	drawer
+n03234164	drawers, underdrawers, shorts, boxers, boxershorts
+n03234952	drawing chalk
+n03235042	drawing room, withdrawing room
+n03235180	drawing room
+n03235327	drawknife, drawshave
+n03235796	drawstring bag
+n03235979	dray, camion
+n03236093	dreadnought, dreadnaught
+n03236217	dredge
+n03236423	dredger
+n03236580	dredging bucket
+n03236735	dress, frock
+n03237212	dress blues, dress whites
+n03237340	dresser
+n03237416	dress hat, high hat, opera hat, silk hat, stovepipe, top hat, topper, beaver
+n03237639	dressing, medical dressing
+n03237839	dressing case
+n03237992	dressing gown, robe-de-chambre, lounging robe
+n03238131	dressing room
+n03238286	dressing sack, dressing sacque
+n03238586	dressing table, dresser, vanity, toilet table
+n03238762	dress rack
+n03238879	dress shirt, evening shirt
+n03239054	dress suit, full dress, tailcoat, tail coat, tails, white tie, white tie and tails
+n03239259	dress uniform
+n03239607	drift net
+n03239726	drill
+n03240140	electric drill
+n03240683	drilling platform, offshore rig
+n03240892	drill press
+n03241093	drill rig, drilling rig, oilrig, oil rig
+n03241335	drinking fountain, water fountain, bubbler
+n03241496	drinking vessel
+n03241903	drip loop
+n03242120	drip mat
+n03242264	drip pan
+n03242390	dripping pan, drip pan
+n03242506	drip pot
+n03242995	drive
+n03243218	drive
+n03243625	drive line, drive line system
+n03244047	driver, number one wood
+n03244231	driveshaft
+n03244388	driveway, drive, private road
+n03244775	driving iron, one iron
+n03244919	driving wheel
+n03245271	drogue, drogue chute, drogue parachute
+n03245421	drogue parachute
+n03245724	drone, drone pipe, bourdon
+n03245889	drone, pilotless aircraft, radio-controlled aircraft
+n03246197	drop arch
+n03246312	drop cloth
+n03246454	drop curtain, drop cloth, drop
+n03246653	drop forge, drop hammer, drop press
+n03246933	drop-leaf table
+n03247083	dropper, eye dropper
+n03247351	droshky, drosky
+n03247495	drove, drove chisel
+n03248835	drugget
+n03249342	drugstore, apothecary's shop, chemist's, chemist's shop, pharmacy
+n03249569	drum, membranophone, tympan
+n03249956	drum, metal drum
+n03250089	drum brake
+n03250279	drumhead, head
+n03250405	drum printer
+n03250588	drum sander, electric sander, sander, smoother
+n03250847	drumstick
+n03250952	dry battery
+n03251100	dry-bulb thermometer
+n03251280	dry cell
+n03251533	dry dock, drydock, graving dock
+n03251766	dryer, drier
+n03251932	dry fly
+n03252231	dry kiln
+n03252324	dry masonry
+n03252422	dry point
+n03252637	dry wall, dry-stone wall
+n03252787	dual scan display
+n03253071	duck
+n03253187	duckboard
+n03253279	duckpin
+n03253714	dudeen
+n03253796	duffel, duffle
+n03253886	duffel bag, duffle bag, duffel, duffle
+n03254046	duffel coat, duffle coat
+n03254189	dugout
+n03254374	dugout canoe, dugout, pirogue
+n03254625	dulciana
+n03254737	dulcimer
+n03254862	dulcimer
+n03255030	dumbbell
+n03255167	dumb bomb, gravity bomb
+n03255322	dumbwaiter, food elevator
+n03255488	dumdum, dumdum bullet
+n03255899	dumpcart
+n03256032	Dumpster
+n03256166	dump truck, dumper, tipper truck, tipper lorry, tip truck, tipper
+n03256472	Dumpy level
+n03256631	dunce cap, dunce's cap, fool's cap
+n03256788	dune buggy, beach buggy
+n03256928	dungeon
+n03257065	duplex apartment, duplex
+n03257210	duplex house, duplex, semidetached house
+n03257586	duplicator, copier
+n03258192	dust bag, vacuum bag
+n03258330	dustcloth, dustrag, duster
+n03258456	dust cover
+n03258577	dust cover, dust sheet
+n03258905	dustmop, dust mop, dry mop
+n03259009	dustpan
+n03259280	Dutch oven
+n03259401	Dutch oven
+n03259505	dwelling, home, domicile, abode, habitation, dwelling house
+n03260206	dye-works
+n03260504	dynamo
+n03260733	dynamometer, ergometer
+n03260849	Eames chair
+n03261019	earflap, earlap
+n03261263	early warning radar
+n03261395	early warning system
+n03261603	earmuff
+n03261776	earphone, earpiece, headphone, phone
+n03262072	earplug
+n03262248	earplug
+n03262519	earthenware
+n03262717	earthwork
+n03262809	easel
+n03262932	easy chair, lounge chair, overstuffed chair
+n03263076	eaves
+n03263338	ecclesiastical attire, ecclesiastical robe
+n03263640	echinus
+n03263758	echocardiograph
+n03264906	edger
+n03265032	edge tool
+n03265754	efficiency apartment
+n03266195	egg-and-dart, egg-and-anchor, egg-and-tongue
+n03266371	eggbeater, eggwhisk
+n03266620	egg timer
+n03266749	eiderdown, duvet, continental quilt
+n03267113	eight ball
+n03267468	ejection seat, ejector seat, capsule
+n03267696	elastic
+n03267821	elastic bandage
+n03268142	Elastoplast
+n03268311	elbow
+n03268645	elbow pad
+n03268790	electric, electric automobile, electric car
+n03268918	electrical cable
+n03269073	electrical contact
+n03269203	electrical converter
+n03269401	electrical device
+n03270165	electrical system
+n03270695	electric bell
+n03270854	electric blanket
+n03271030	electric chair, chair, death chair, hot seat
+n03271260	electric clock
+n03271376	electric-discharge lamp, gas-discharge lamp
+n03271574	electric fan, blower
+n03271765	electric frying pan
+n03271865	electric furnace
+n03272010	electric guitar
+n03272125	electric hammer
+n03272239	electric heater, electric fire
+n03272383	electric lamp
+n03272562	electric locomotive
+n03272810	electric meter, power meter
+n03272940	electric mixer
+n03273061	electric motor
+n03273551	electric organ, electronic organ, Hammond organ, organ
+n03273740	electric range
+n03273913	electric refrigerator, fridge
+n03274265	electric toothbrush
+n03274435	electric typewriter
+n03274561	electro-acoustic transducer
+n03274796	electrode
+n03275125	electrodynamometer
+n03275311	electroencephalograph
+n03275566	electrograph
+n03275681	electrolytic, electrolytic capacitor, electrolytic condenser
+n03275864	electrolytic cell
+n03276179	electromagnet
+n03276696	electrometer
+n03276839	electromyograph
+n03277004	electron accelerator
+n03277149	electron gun
+n03277459	electronic balance
+n03277602	electronic converter
+n03277771	electronic device
+n03278248	electronic equipment
+n03278914	electronic fetal monitor, electronic foetal monitor, fetal monitor, foetal monitor
+n03279153	electronic instrument, electronic musical instrument
+n03279364	electronic voltmeter
+n03279508	electron microscope
+n03279804	electron multiplier
+n03279918	electrophorus
+n03280216	electroscope
+n03280394	electrostatic generator, electrostatic machine, Wimshurst machine, Van de Graaff generator
+n03280644	electrostatic printer
+n03281145	elevator, lift
+n03281524	elevator
+n03281673	elevator shaft
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+n03282295	embassy
+n03282401	embellishment
+n03283221	emergency room, ER
+n03283413	emesis basin
+n03283827	emitter
+n03284308	empty
+n03284482	emulsion, photographic emulsion
+n03284743	enamel
+n03284886	enamel
+n03284981	enamelware
+n03285578	encaustic
+n03285730	encephalogram, pneumoencephalogram
+n03285912	enclosure
+n03286572	endoscope
+n03287351	energizer, energiser
+n03287733	engine
+n03288003	engine
+n03288500	engineering, engine room
+n03288643	enginery
+n03288742	English horn, cor anglais
+n03288886	English saddle, English cavalry saddle
+n03289660	enlarger
+n03289985	ensemble
+n03290096	ensign
+n03290195	entablature
+n03290653	entertainment center
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+n03291551	entrenchment, intrenchment
+n03291741	envelope
+n03291819	envelope
+n03291963	envelope, gasbag
+n03292085	eolith
+n03292362	epauliere
+n03292475	epee
+n03292603	epergne
+n03292736	epicyclic train, epicyclic gear train
+n03292960	epidiascope
+n03293095	epilating wax
+n03293741	equalizer, equaliser
+n03293863	equatorial
+n03294048	equipment
+n03294604	erasable programmable read-only memory, EPROM
+n03294833	eraser
+n03295012	erecting prism
+n03295140	erection
+n03295246	Erlenmeyer flask
+n03295928	escape hatch
+n03296081	escapement
+n03296217	escape wheel
+n03296328	escarpment, escarp, scarp, protective embankment
+n03296478	escutcheon, scutcheon
+n03296963	esophagoscope, oesophagoscope
+n03297103	espadrille
+n03297226	espalier
+n03297495	espresso maker
+n03297644	espresso shop
+n03297735	establishment
+n03298089	estaminet
+n03298352	estradiol patch
+n03298716	etagere
+n03298858	etamine, etamin
+n03299406	etching
+n03300216	ethernet
+n03300443	ethernet cable
+n03301175	Eton jacket
+n03301291	etui
+n03301389	eudiometer
+n03301568	euphonium
+n03301833	evaporative cooler
+n03301940	evening bag
+n03302671	exercise bike, exercycle
+n03302790	exercise device
+n03302938	exhaust, exhaust system
+n03303217	exhaust fan
+n03303669	exhaust valve
+n03303831	exhibition hall, exhibition area
+n03304197	Exocet
+n03304323	expansion bit, expansive bit
+n03304465	expansion bolt
+n03305300	explosive detection system, EDS
+n03305522	explosive device
+n03305953	explosive trace detection, ETD
+n03306385	express, limited
+n03306869	extension, telephone extension, extension phone
+n03307037	extension cord
+n03307573	external-combustion engine
+n03307792	external drive
+n03308152	extractor
+n03308481	eyebrow pencil
+n03308614	eyecup, eyebath, eye cup
+n03309110	eyeliner
+n03309356	eyepatch, patch
+n03309465	eyepiece, ocular
+n03309687	eyeshadow
+n03309808	fabric, cloth, material, textile
+n03313333	facade, frontage, frontal
+n03314227	face guard
+n03314378	face mask
+n03314608	faceplate
+n03314780	face powder
+n03314884	face veil
+n03315644	facing, cladding
+n03315805	facing
+n03315990	facing, veneer
+n03316105	facsimile, facsimile machine, fax
+n03316406	factory, mill, manufacturing plant, manufactory
+n03316873	factory ship
+n03317233	fagot, faggot
+n03317510	fagot stitch, faggot stitch
+n03317673	Fahrenheit thermometer
+n03317788	faience
+n03317889	faille
+n03318136	fairlead
+n03318294	fairy light
+n03318865	falchion
+n03318983	fallboard, fall-board
+n03319167	fallout shelter
+n03319457	false face
+n03319576	false teeth
+n03319745	family room
+n03320046	fan
+n03320262	fan belt
+n03320421	fan blade
+n03320519	fancy dress, masquerade, masquerade costume
+n03320845	fanion
+n03320959	fanlight
+n03321103	fanjet, fan-jet, fanjet engine, turbojet, turbojet engine, turbofan, turbofan engine
+n03321419	fanjet, fan-jet, turbofan, turbojet
+n03321563	fanny pack, butt pack
+n03321843	fan tracery
+n03321954	fan vaulting
+n03322570	farm building
+n03322704	farmer's market, green market, greenmarket
+n03322836	farmhouse
+n03322940	farm machine
+n03323096	farmplace, farm-place, farmstead
+n03323211	farmyard
+n03323319	farthingale
+n03323703	fastener, fastening, holdfast, fixing
+n03324629	fast reactor
+n03324814	fat farm
+n03324928	fatigues
+n03325088	faucet, spigot
+n03325288	fauld
+n03325403	fauteuil
+n03325584	feather boa, boa
+n03325691	featheredge
+n03325941	fedora, felt hat, homburg, Stetson, trilby
+n03326073	feedback circuit, feedback loop
+n03326371	feedlot
+n03326475	fell, felled seam
+n03326660	felloe, felly
+n03326795	felt
+n03326948	felt-tip pen, felt-tipped pen, felt tip, Magic Marker
+n03327133	felucca
+n03327234	fence, fencing
+n03327553	fencing mask, fencer's mask
+n03327691	fencing sword
+n03327841	fender, wing
+n03328201	fender, buffer, cowcatcher, pilot
+n03329302	Ferris wheel
+n03329536	ferrule, collet
+n03329663	ferry, ferryboat
+n03330002	ferule
+n03330665	festoon
+n03330792	fetoscope, foetoscope
+n03330947	fetter, hobble
+n03331077	fez, tarboosh
+n03331244	fiber, fibre, vulcanized fiber
+n03331599	fiber optic cable, fibre optic cable
+n03332005	fiberscope
+n03332173	fichu
+n03332271	fiddlestick, violin bow
+n03332393	field artillery, field gun
+n03332591	field coil, field winding
+n03332784	field-effect transistor, FET
+n03332989	field-emission microscope
+n03333129	field glass, glass, spyglass
+n03333252	field hockey ball
+n03333349	field hospital
+n03333610	field house, sports arena
+n03333711	field lens
+n03333851	field magnet
+n03334017	field-sequential color television, field-sequential color TV, field-sequential color television system, field-sequential color TV system
+n03334291	field tent
+n03334382	fieldwork
+n03334492	fife
+n03334912	fifth wheel, spare
+n03335030	fighter, fighter aircraft, attack aircraft
+n03335333	fighting chair
+n03335461	fig leaf
+n03335846	figure eight, figure of eight
+n03336168	figure loom, figured-fabric loom
+n03336282	figure skate
+n03336575	filament
+n03336742	filature
+n03336839	file
+n03337140	file, file cabinet, filing cabinet
+n03337383	file folder
+n03337494	file server
+n03337822	filigree, filagree, fillagree
+n03338287	filling
+n03338821	film, photographic film
+n03339296	film, plastic film
+n03339529	film advance
+n03339643	filter
+n03340009	filter
+n03340723	finder, viewfinder, view finder
+n03340923	finery
+n03341035	fine-tooth comb, fine-toothed comb
+n03341153	finger
+n03341297	fingerboard
+n03341606	finger bowl
+n03342015	finger paint, fingerpaint
+n03342127	finger-painting
+n03342262	finger plate, escutcheon, scutcheon
+n03342432	fingerstall, cot
+n03342657	finish coat, finishing coat
+n03342863	finish coat, finishing coat
+n03342961	finisher
+n03343047	fin keel
+n03343234	fipple
+n03343354	fipple flute, fipple pipe, recorder, vertical flute
+n03343560	fire
+n03343737	fire alarm, smoke alarm
+n03343853	firearm, piece, small-arm
+n03344305	fire bell
+n03344393	fireboat
+n03344509	firebox
+n03344642	firebrick
+n03344784	fire control radar
+n03344935	fire control system
+n03345487	fire engine, fire truck
+n03345837	fire extinguisher, extinguisher, asphyxiator
+n03346135	fire iron
+n03346289	fireman's ax, fireman's axe
+n03346455	fireplace, hearth, open fireplace
+n03347037	fire screen, fireguard
+n03347472	fire tongs, coal tongs
+n03347617	fire tower
+n03348142	firewall
+n03348868	firing chamber, gun chamber
+n03349020	firing pin
+n03349296	firkin
+n03349367	firmer chisel
+n03349469	first-aid kit
+n03349599	first-aid station
+n03349771	first base
+n03349892	first class
+n03350204	fishbowl, fish bowl, goldfish bowl
+n03350352	fisherman's bend
+n03350456	fisherman's knot, true lover's knot, truelove knot
+n03350602	fisherman's lure, fish lure
+n03351151	fishhook
+n03351262	fishing boat, fishing smack, fishing vessel
+n03351434	fishing gear, tackle, fishing tackle, fishing rig, rig
+n03351979	fishing rod, fishing pole
+n03352232	fish joint
+n03352366	fish knife
+n03352628	fishnet, fishing net
+n03352961	fish slice
+n03353281	fitment
+n03353951	fixative
+n03354207	fixer-upper
+n03354903	flag
+n03355468	flageolet, treble recorder, shepherd's pipe
+n03355768	flagon
+n03355925	flagpole, flagstaff
+n03356038	flagship
+n03356279	flail
+n03356446	flambeau
+n03356559	flamethrower
+n03356858	flange, rim
+n03356982	flannel
+n03357081	flannel, gabardine, tweed, white
+n03357267	flannelette
+n03357716	flap, flaps
+n03358172	flash, photoflash, flash lamp, flashgun, flashbulb, flash bulb
+n03358380	flash
+n03358726	flash camera
+n03358841	flasher
+n03359137	flashlight, torch
+n03359285	flashlight battery
+n03359436	flash memory
+n03359566	flask
+n03360133	flat arch, straight arch
+n03360300	flatbed
+n03360431	flatbed press, cylinder press
+n03360622	flat bench
+n03360731	flatcar, flatbed, flat
+n03361109	flat file
+n03361297	flatlet
+n03361380	flat panel display, FPD
+n03361550	flats
+n03361683	flat tip screwdriver
+n03362639	fleece
+n03362771	fleet ballistic missile submarine
+n03362890	fleur-de-lis, fleur-de-lys
+n03363363	flight simulator, trainer
+n03363549	flintlock
+n03363749	flintlock, firelock
+n03364008	flip-flop, thong
+n03364156	flipper, fin
+n03364599	float, plasterer's float
+n03364937	floating dock, floating dry dock
+n03365231	floatplane, pontoon plane
+n03365374	flood, floodlight, flood lamp, photoflood
+n03365592	floor, flooring
+n03365991	floor, level, storey, story
+n03366464	floor
+n03366721	floorboard
+n03366823	floor cover, floor covering
+n03366974	floor joist
+n03367059	floor lamp
+n03367321	flophouse, dosshouse
+n03367410	florist, florist shop, flower store
+n03367545	floss
+n03367875	flotsam, jetsam
+n03367969	flour bin
+n03368048	flour mill
+n03368352	flowerbed, flower bed, bed of flowers
+n03369276	flugelhorn, fluegelhorn
+n03369407	fluid drive
+n03369512	fluid flywheel
+n03369866	flume
+n03370387	fluorescent lamp
+n03370646	fluoroscope, roentgenoscope
+n03371875	flush toilet, lavatory
+n03372029	flute, transverse flute
+n03372549	flute, flute glass, champagne flute
+n03372822	flux applicator
+n03372933	fluxmeter
+n03373237	fly
+n03373611	flying boat
+n03373943	flying buttress, arc-boutant
+n03374102	flying carpet
+n03374282	flying jib
+n03374372	fly rod
+n03374473	fly tent
+n03374570	flytrap
+n03374649	flywheel
+n03374838	fob, watch chain, watch guard
+n03375171	foghorn
+n03375329	foglamp
+n03375575	foil
+n03376159	fold, sheepfold, sheep pen, sheepcote
+n03376279	folder
+n03376595	folding chair
+n03376771	folding door, accordion door
+n03376938	folding saw
+n03378005	food court
+n03378174	food processor
+n03378342	food hamper
+n03378442	foot
+n03378593	footage
+n03378765	football
+n03379051	football helmet
+n03379204	football stadium
+n03379343	footbath
+n03379719	foot brake
+n03379828	footbridge, overcrossing, pedestrian bridge
+n03379989	foothold, footing
+n03380301	footlocker, locker
+n03380647	foot rule
+n03380724	footstool, footrest, ottoman, tuffet
+n03380867	footwear, footgear
+n03381126	footwear
+n03381231	forceps
+n03381450	force pump
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+n03382533	fore edge, foredge
+n03382708	foreground
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+n03382969	fore plane
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+n03384352	forklift
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+n03385295	Formica
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+n03386011	fortress, fort
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+n03388183	fountain pen
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+n03388990	four-stroke engine, four-stroke internal-combustion engine
+n03389611	four-wheel drive, 4WD
+n03389761	four-wheel drive, 4WD
+n03389889	four-wheeler
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+n03394272	freight liner, liner train
+n03394480	freight train, rattler
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+n03394916	French horn, horn
+n03395256	French polish, French polish shellac
+n03395401	French roof
+n03395514	French window
+n03395859	Fresnel lens
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+n03396654	friction clutch
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+n03397087	frieze
+n03397266	frigate
+n03397412	frigate
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+n03397947	Frisbee
+n03398153	frock
+n03398228	frock coat
+n03399579	frontlet, frontal
+n03399677	front porch
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+n03400972	fuel filter
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+n03401279	fuel injection, fuel injection system
+n03401721	fuel system
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+n03402369	full metal jacket
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+n03404251	fur coat
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+n03405725	furniture, piece of furniture, article of furniture
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+n03406966	furrow
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+n03411079	gaiter
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+n03412058	gallery, art gallery, picture gallery
+n03412220	galley, ship's galley, caboose, cookhouse
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+n03416489	garage
+n03416640	garage, service department
+n03416775	Garand rifle, Garand, M-1, M-1 rifle
+n03416900	garbage
+n03417042	garbage truck, dustcart
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+n03418402	garden trowel
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+n03420345	garment bag
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+n03421117	garter, supporter
+n03421324	garter belt, suspender belt
+n03421485	garter stitch
+n03421669	gas guzzler
+n03421768	gas shell
+n03421960	gas bracket
+n03422072	gas burner, gas jet
+n03422484	gas-cooled reactor
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+n03423568	gas holder, gasometer
+n03423719	gasket
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+n03424204	gas maser
+n03424325	gasmask, respirator, gas helmet
+n03424489	gas meter, gasometer
+n03424630	gasoline engine, petrol engine
+n03424862	gasoline gauge, gasoline gage, gas gauge, gas gage, petrol gauge, petrol gage
+n03425241	gas oven
+n03425325	gas oven
+n03425413	gas pump, gasoline pump, petrol pump, island dispenser
+n03425595	gas range, gas stove, gas cooker
+n03425769	gas ring
+n03426134	gas tank, gasoline tank, petrol tank
+n03426285	gas thermometer, air thermometer
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+n03429771	gauntlet, gantlet, metal glove
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+n03431745	gearing, gear, geartrain, power train, train
+n03432061	gearset
+n03432129	gearshift, gearstick, shifter, gear lever
+n03432360	Geiger counter, Geiger-Muller counter
+n03432509	Geiger tube, Geiger-Muller tube
+n03433247	gene chip, DNA chip
+n03433637	general-purpose bomb, GP bomb
+n03433877	generator
+n03434188	generator
+n03434285	generator
+n03434830	Geneva gown
+n03435593	geodesic dome
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+n03436182	ghetto blaster, boom box
+n03436417	gift shop, novelty shop
+n03436549	gift wrapping
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+n03436772	gig
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+n03436990	gig
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+n03437941	girder
+n03438071	girdle, cincture, sash, waistband, waistcloth
+n03438257	glass, drinking glass
+n03438661	glass
+n03438780	glass cutter
+n03438863	glasses case
+n03439348	glebe house
+n03439631	Glengarry
+n03439814	glider, sailplane
+n03440216	Global Positioning System, GPS
+n03440682	glockenspiel, orchestral bells
+n03440876	glory hole, lazaretto
+n03441112	glove
+n03441345	glove compartment
+n03441465	glow lamp
+n03441582	glow tube
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+n03442487	glyptics, lithoglyptics
+n03442597	gnomon
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+n03443005	goalmouth
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+n03443371	goblet
+n03443543	godown
+n03443912	goggles
+n03444034	go-kart
+n03445326	gold plate
+n03445617	golf bag
+n03445777	golf ball
+n03445924	golfcart, golf cart
+n03446070	golf club, golf-club, club
+n03446268	golf-club head, club head, club-head, clubhead
+n03446832	golf equipment
+n03447075	golf glove
+n03447358	golliwog, golliwogg
+n03447447	gondola
+n03447721	gong, tam-tam
+n03447894	goniometer
+n03448031	Gordian knot
+n03448590	gorget
+n03448696	gossamer
+n03448956	Gothic arch
+n03449217	gouache
+n03449309	gouge
+n03449451	gourd, calabash
+n03449564	government building
+n03449858	government office
+n03450230	gown
+n03450516	gown, robe
+n03450734	gown, surgical gown, scrubs
+n03450881	grab
+n03450974	grab bag
+n03451120	grab bar
+n03451253	grace cup
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+n03451711	graduated cylinder
+n03451798	graffito, graffiti
+n03452267	gramophone, acoustic gramophone
+n03452449	granary, garner
+n03452594	grandfather clock, longcase clock
+n03452741	grand piano, grand
+n03453231	graniteware
+n03453320	granny knot, granny
+n03453443	grape arbor, grape arbour
+n03454110	grapnel, grapnel anchor
+n03454211	grapnel, grapple, grappler, grappling hook, grappling iron
+n03454442	grass skirt
+n03454536	grate, grating
+n03454707	grate, grating
+n03454885	grater
+n03455355	graver, graving tool, pointel, pointrel
+n03455488	gravestone, headstone, tombstone
+n03455642	gravimeter, gravity meter
+n03455802	gravure, photogravure, heliogravure
+n03456024	gravy boat, gravy holder, sauceboat, boat
+n03456186	grey, gray
+n03456299	grease-gun, gun
+n03456447	greasepaint
+n03456548	greasy spoon
+n03456665	greatcoat, overcoat, topcoat
+n03457008	great hall
+n03457451	greave, jambeau
+n03457686	greengrocery
+n03457902	greenhouse, nursery, glasshouse
+n03458271	grenade
+n03458422	grid, gridiron
+n03459328	griddle
+n03459591	grill, grille, grillwork
+n03459775	grille, radiator grille
+n03459914	grillroom, grill
+n03460040	grinder
+n03460147	grinding wheel, emery wheel
+n03460297	grindstone
+n03460455	gripsack
+n03460899	gristmill
+n03461288	grocery bag
+n03461385	grocery store, grocery, food market, market
+n03461651	grogram
+n03461882	groined vault
+n03461988	groover
+n03462110	grosgrain
+n03462315	gros point
+n03462747	ground, earth
+n03462972	ground bait
+n03463185	ground control
+n03463381	ground floor, first floor, ground level
+n03463666	groundsheet, ground cloth
+n03464053	G-string, thong
+n03464467	guard, safety, safety device
+n03464628	guard boat
+n03464952	guardroom
+n03465040	guardroom
+n03465151	guard ship
+n03465320	guard's van
+n03465426	gueridon
+n03465500	Guarnerius
+n03465605	guesthouse
+n03465718	guestroom
+n03465818	guidance system, guidance device
+n03466162	guided missile
+n03466493	guided missile cruiser
+n03466600	guided missile frigate
+n03466839	guildhall
+n03466947	guilloche
+n03467068	guillotine
+n03467254	guimpe
+n03467380	guimpe
+n03467517	guitar
+n03467796	guitar pick
+n03467887	gulag
+n03467984	gun
+n03468570	gunboat
+n03468696	gun carriage
+n03468821	gun case
+n03469031	gun emplacement, weapons emplacement
+n03469175	gun enclosure, gun turret, turret
+n03469493	gunlock, firing mechanism
+n03469832	gunnery
+n03469903	gunnysack, gunny sack, burlap bag
+n03470005	gun pendulum
+n03470222	gun room
+n03470387	gunsight, gun-sight
+n03470629	gun trigger, trigger
+n03470948	gurney
+n03471030	gusher
+n03471190	gusset, inset
+n03471347	gusset, gusset plate
+n03471779	guy, guy cable, guy wire, guy rope
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+n03472535	gym shoe, sneaker, tennis shoe
+n03472672	gym suit
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+n03472937	gypsy cab
+n03473078	gyrocompass
+n03473227	gyroscope, gyro
+n03473465	gyrostabilizer, gyrostabiliser
+n03473817	habergeon
+n03473966	habit
+n03474167	habit, riding habit
+n03474352	hacienda
+n03474779	hacksaw, hack saw, metal saw
+n03474896	haft, helve
+n03475581	hairbrush
+n03475674	haircloth, hair
+n03475823	hairdressing, hair tonic, hair oil, hair grease
+n03475961	hairnet
+n03476083	hairpiece, false hair, postiche
+n03476313	hairpin
+n03476542	hair shirt
+n03476684	hair slide
+n03476991	hair spray
+n03477143	hairspring
+n03477303	hair trigger
+n03477410	halberd
+n03477512	half binding
+n03477773	half hatchet
+n03477902	half hitch
+n03478589	half track
+n03478756	hall
+n03478907	hall
+n03479121	hall
+n03479266	Hall of Fame
+n03479397	hall of residence
+n03479502	hallstand
+n03480579	halter
+n03480719	halter, hackamore
+n03480973	hame
+n03481172	hammer
+n03481521	hammer, power hammer
+n03482001	hammer
+n03482128	hammerhead
+n03482252	hammock, sack
+n03482405	hamper
+n03482523	hand
+n03482877	handball
+n03483086	handbarrow
+n03483230	handbell
+n03483316	hand blower, blow dryer, blow drier, hair dryer, hair drier
+n03483531	handbow
+n03483637	hand brake, emergency, emergency brake, parking brake
+n03483823	hand calculator, pocket calculator
+n03483971	handcar
+n03484083	handcart, pushcart, cart, go-cart
+n03484487	hand cream
+n03484576	handcuff, cuff, handlock, manacle
+n03484809	hand drill, handheld drill
+n03484931	hand glass, simple microscope, magnifying glass
+n03485198	hand glass, hand mirror
+n03485309	hand grenade
+n03485407	hand-held computer, hand-held microcomputer
+n03485575	handhold
+n03485794	handkerchief, hankie, hanky, hankey
+n03487090	handlebar
+n03487331	handloom
+n03487444	hand lotion
+n03487533	hand luggage
+n03487642	hand-me-down
+n03487774	hand mower
+n03487886	hand pump
+n03488111	handrest
+n03488188	handsaw, hand saw, carpenter's saw
+n03488438	handset, French telephone
+n03488603	hand shovel
+n03488784	handspike
+n03488887	handstamp, rubber stamp
+n03489048	hand throttle
+n03489162	hand tool
+n03490006	hand towel, face towel
+n03490119	hand truck, truck
+n03490324	handwear, hand wear
+n03490449	handwheel
+n03490649	handwheel
+n03490784	hangar queen
+n03490884	hanger
+n03491032	hang glider
+n03491724	hangman's rope, hangman's halter, halter, hemp, hempen necktie
+n03491988	hank
+n03492087	hansom, hansom cab
+n03492250	harbor, harbour
+n03492542	hard disc, hard disk, fixed disk
+n03492922	hard hat, tin hat, safety hat
+n03493219	hardtop
+n03493792	hardware, ironware
+n03493911	hardware store, ironmonger, ironmonger's shop
+n03494278	harmonica, mouth organ, harp, mouth harp
+n03494537	harmonium, organ, reed organ
+n03494706	harness
+n03495039	harness
+n03495258	harp
+n03495570	harp
+n03495671	harpoon
+n03495941	harpoon gun
+n03496183	harpoon log
+n03496296	harpsichord, cembalo
+n03496486	Harris Tweed
+n03496612	harrow
+n03496892	harvester, reaper
+n03497100	hash house
+n03497352	hasp
+n03497657	hat, chapeau, lid
+n03498441	hatbox
+n03498536	hatch
+n03498662	hatchback, hatchback door
+n03498781	hatchback
+n03498866	hatchel, heckle
+n03498962	hatchet
+n03499354	hatpin
+n03499468	hauberk, byrnie
+n03499907	Hawaiian guitar, steel guitar
+n03500090	hawse, hawsehole, hawsepipe
+n03500209	hawser
+n03500295	hawser bend
+n03500389	hay bale
+n03500457	hayfork
+n03500557	hayloft, haymow, mow
+n03500699	haymaker, hay conditioner
+n03500838	hayrack, hayrig
+n03500971	hayrack
+n03501152	hazard
+n03501288	head
+n03501520	head
+n03501614	head
+n03502200	headboard
+n03502331	head covering, veil
+n03502509	headdress, headgear
+n03502777	header
+n03502897	header
+n03503097	header, coping, cope
+n03503233	header, lintel
+n03503358	headfast
+n03503477	head gasket
+n03503567	head gate
+n03503718	headgear
+n03503997	headlight, headlamp
+n03504205	headpiece
+n03504293	headpin, kingpin
+n03504723	headquarters, central office, main office, home office, home base
+n03505015	headrace
+n03505133	headrest
+n03505383	headsail
+n03505504	headscarf
+n03505667	headset
+n03505764	head shop
+n03506028	headstall, headpiece
+n03506184	headstock
+n03506370	health spa, spa, health club
+n03506560	hearing aid, ear trumpet
+n03506727	hearing aid, deaf-aid
+n03506880	hearse
+n03507241	hearth, fireside
+n03507458	hearthrug
+n03507658	heart-lung machine
+n03507963	heat engine
+n03508101	heater, warmer
+n03508485	heat exchanger
+n03508881	heating pad, hot pad
+n03509394	heat lamp, infrared lamp
+n03509608	heat pump
+n03509843	heat-seeking missile
+n03510072	heat shield
+n03510244	heat sink
+n03510384	heaume
+n03510487	heaver
+n03510583	heavier-than-air craft
+n03510866	heckelphone, basset oboe
+n03510987	hectograph, heliotype
+n03511175	hedge, hedgerow
+n03511333	hedge trimmer
+n03512030	helicon, bombardon
+n03512147	helicopter, chopper, whirlybird, eggbeater
+n03512452	heliograph
+n03512624	heliometer
+n03512911	helm
+n03513137	helmet
+n03513376	helmet
+n03514129	hematocrit, haematocrit
+n03514340	hemming-stitch
+n03514451	hemostat, haemostat
+n03514693	hemstitch, hemstitching
+n03514894	henroost
+n03515338	heraldry
+n03515934	hermitage
+n03516266	herringbone
+n03516367	herringbone, herringbone pattern
+n03516647	Herschelian telescope, off-axis reflector
+n03516844	Hessian boot, hessian, jackboot, Wellington, Wellington boot
+n03516996	heterodyne receiver, superheterodyne receiver, superhet
+n03517509	hibachi
+n03517647	hideaway, retreat
+n03517760	hi-fi, high fidelity sound system
+n03517899	high altar
+n03517982	high-angle gun
+n03518135	highball glass
+n03518230	highboard
+n03518305	highboy, tallboy
+n03518445	highchair, feeding chair
+n03518631	high gear, high
+n03518829	high-hat cymbal, high hat
+n03518943	highlighter
+n03519081	highlighter
+n03519226	high-pass filter
+n03519387	high-rise, tower block
+n03519674	high table
+n03519848	high-warp loom
+n03520493	hijab
+n03521076	hinge, flexible joint
+n03521431	hinging post, swinging post
+n03521544	hip boot, thigh boot
+n03521675	hipflask, pocket flask
+n03521771	hip pad
+n03521899	hip pocket
+n03522003	hippodrome
+n03522100	hip roof, hipped roof
+n03522634	hitch
+n03522863	hitch
+n03522990	hitching post
+n03523134	hitchrack, hitching bar
+n03523398	hob
+n03523506	hobble skirt
+n03523987	hockey skate
+n03524150	hockey stick
+n03524287	hod
+n03524425	hodoscope
+n03524574	hoe
+n03524745	hoe handle
+n03524976	hogshead
+n03525074	hoist
+n03525252	hold, keep
+n03525454	holder
+n03525693	holding cell
+n03525827	holding device
+n03526062	holding pen, holding paddock, holding yard
+n03527149	hollowware, holloware
+n03527444	holster
+n03527565	holster
+n03527675	holy of holies, sanctum sanctorum
+n03528100	home, nursing home, rest home
+n03528263	home appliance, household appliance
+n03528523	home computer
+n03528901	home plate, home base, home, plate
+n03529175	home room, homeroom
+n03529444	homespun
+n03529629	homestead
+n03529860	home theater, home theatre
+n03530189	homing torpedo
+n03530511	hone
+n03530642	honeycomb
+n03530910	hood, bonnet, cowl, cowling
+n03531281	hood
+n03531447	hood
+n03531546	hood, exhaust hood
+n03531691	hood
+n03531982	hood latch
+n03532342	hook
+n03532672	hook, claw
+n03532919	hook
+n03533014	hookah, narghile, nargileh, sheesha, shisha, chicha, calean, kalian, water pipe, hubble-bubble, hubbly-bubbly
+n03533392	hook and eye
+n03533486	hookup, assemblage
+n03533654	hookup
+n03533845	hook wrench, hook spanner
+n03534580	hoopskirt, crinoline
+n03534695	hoosegow, hoosgow
+n03534776	Hoover
+n03535024	hope chest, wedding chest
+n03535284	hopper
+n03535647	hopsacking, hopsack
+n03535780	horizontal bar, high bar
+n03536122	horizontal stabilizer, horizontal stabiliser, tailplane
+n03536568	horizontal tail
+n03536761	horn
+n03537085	horn
+n03537241	horn
+n03537412	horn button
+n03537550	hornpipe, pibgorn, stockhorn
+n03538037	horse, gymnastic horse
+n03538179	horsebox
+n03538300	horsecar
+n03538406	horse cart, horse-cart
+n03538542	horsecloth
+n03538634	horse-drawn vehicle
+n03538817	horsehair
+n03538957	horsehair wig
+n03539103	horseless carriage
+n03539293	horse pistol, horse-pistol
+n03539433	horseshoe, shoe
+n03539546	horseshoe
+n03539678	horse-trail
+n03539754	horsewhip
+n03540090	hose
+n03540267	hosiery, hose
+n03540476	hospice
+n03540595	hospital, infirmary
+n03540914	hospital bed
+n03541091	hospital room
+n03541269	hospital ship
+n03541393	hospital train
+n03541537	hostel, youth hostel, student lodging
+n03541696	hostel, hostelry, inn, lodge, auberge
+n03541923	hot-air balloon
+n03542333	hotel
+n03542605	hotel-casino, casino-hotel
+n03542727	hotel-casino, casino-hotel
+n03542860	hotel room
+n03543012	hot line
+n03543112	hot pants
+n03543254	hot plate, hotplate
+n03543394	hot rod, hot-rod
+n03543511	hot spot, hotspot
+n03543603	hot tub
+n03543735	hot-water bottle, hot-water bag
+n03543945	houndstooth check, hound's-tooth check, dogstooth check, dogs-tooth check, dog's-tooth check
+n03544143	hourglass
+n03544238	hour hand, little hand
+n03544360	house
+n03545150	house
+n03545470	houseboat
+n03545585	houselights
+n03545756	house of cards, cardhouse, card-house, cardcastle
+n03545961	house of correction
+n03546112	house paint, housepaint
+n03546235	housetop
+n03546340	housing, lodging, living accommodations
+n03547054	hovel, hut, hutch, shack, shanty
+n03547229	hovercraft, ground-effect machine
+n03547397	howdah, houdah
+n03547530	huarache, huaraches
+n03547861	hub-and-spoke, hub-and-spoke system
+n03548086	hubcap
+n03548195	huck, huckaback
+n03548320	hug-me-tight
+n03548402	hula-hoop
+n03548533	hulk
+n03548626	hull
+n03548930	humeral veil, veil
+n03549199	Humvee, Hum-Vee
+n03549350	hunter, hunting watch
+n03549473	hunting knife
+n03549589	hurdle
+n03549732	hurricane deck, hurricane roof, promenade deck, awning deck
+n03549897	hurricane lamp, hurricane lantern, tornado lantern, storm lantern, storm lamp
+n03550153	hut, army hut, field hut
+n03550289	hutch
+n03550420	hutment
+n03551084	hydraulic brake, hydraulic brakes
+n03551395	hydraulic press
+n03551582	hydraulic pump, hydraulic ram
+n03551790	hydraulic system
+n03552001	hydraulic transmission, hydraulic transmission system
+n03552449	hydroelectric turbine
+n03552749	hydrofoil, hydroplane
+n03553019	hydrofoil, foil
+n03553248	hydrogen bomb, H-bomb, fusion bomb, thermonuclear bomb
+n03553486	hydrometer, gravimeter
+n03554375	hygrodeik
+n03554460	hygrometer
+n03554645	hygroscope
+n03555006	hyperbaric chamber
+n03555217	hypercoaster
+n03555426	hypermarket
+n03555564	hypodermic needle
+n03555662	hypodermic syringe, hypodermic, hypo
+n03555862	hypsometer
+n03555996	hysterosalpingogram
+n03556173	I-beam
+n03556679	ice ax, ice axe, piolet
+n03556811	iceboat, ice yacht, scooter
+n03556992	icebreaker, iceboat
+n03557270	iced-tea spoon
+n03557360	ice hockey rink, ice-hockey rink
+n03557590	ice machine
+n03557692	ice maker
+n03557840	ice pack, ice bag
+n03558007	icepick, ice pick
+n03558176	ice rink, ice-skating rink, ice
+n03558404	ice skate
+n03558633	ice tongs
+n03558739	icetray
+n03559373	iconoscope
+n03559531	Identikit, Identikit picture
+n03559999	idle pulley, idler pulley, idle wheel
+n03560430	igloo, iglu
+n03560860	ignition coil
+n03561047	ignition key
+n03561169	ignition switch
+n03561573	imaret
+n03562565	immovable bandage
+n03563200	impact printer
+n03563460	impeller
+n03563710	implant
+n03563967	implement
+n03564849	impression
+n03565288	imprint
+n03565565	improvised explosive device, I.E.D., IED
+n03565710	impulse turbine
+n03565830	in-basket, in-tray
+n03565991	incendiary bomb, incendiary, firebomb
+n03566193	incinerator
+n03566329	inclined plane
+n03566555	inclinometer, dip circle
+n03566730	inclinometer
+n03566860	incrustation, encrustation
+n03567066	incubator, brooder
+n03567635	index register
+n03567788	Indiaman
+n03567912	Indian club
+n03568117	indicator
+n03568818	induction coil
+n03569014	inductor, inductance
+n03569174	industrial watercourse
+n03569293	inertial guidance system, inertial navigation system
+n03569494	inflater, inflator
+n03571280	inhaler, inhalator
+n03571439	injector
+n03571625	ink bottle, inkpot
+n03571853	ink eraser
+n03571942	ink-jet printer
+n03572107	inkle
+n03572205	inkstand
+n03572321	inkwell, inkstand
+n03572631	inlay
+n03573574	inside caliper
+n03573848	insole, innersole
+n03574243	instep
+n03574416	instillator
+n03574555	institution
+n03574816	instrument
+n03575958	instrument of punishment
+n03576215	instrument of torture
+n03576443	intaglio, diaglyph
+n03576955	intake valve
+n03577090	integrated circuit, microcircuit
+n03577312	integrator, planimeter
+n03577474	Intelnet
+n03577672	interceptor
+n03577818	interchange
+n03578055	intercommunication system, intercom
+n03578251	intercontinental ballistic missile, ICBM
+n03578656	interface, port
+n03578981	interferometer
+n03579538	interior door
+n03579982	internal-combustion engine, ICE
+n03580518	internal drive
+n03580615	internet, net, cyberspace
+n03580845	interphone
+n03580990	interrupter
+n03581125	intersection, crossroad, crossway, crossing, carrefour
+n03581531	interstice
+n03581897	intraocular lens
+n03582508	intravenous pyelogram, IVP
+n03582959	inverter
+n03583419	ion engine
+n03583621	ionization chamber, ionization tube
+n03584254	iPod
+n03584400	video iPod
+n03584829	iron, smoothing iron
+n03585073	iron
+n03585337	iron, branding iron
+n03585438	irons, chains
+n03585551	ironclad
+n03585682	iron foundry
+n03585778	iron horse
+n03585875	ironing
+n03586219	iron lung
+n03586631	ironmongery
+n03586911	ironworks
+n03587205	irrigation ditch
+n03588216	izar
+n03588841	jabot
+n03588951	jack
+n03589313	jack, jackstones
+n03589513	jack
+n03589672	jack
+n03589791	jacket
+n03590306	jacket
+n03590475	jacket
+n03590588	jack-in-the-box
+n03590841	jack-o'-lantern
+n03590932	jack plane
+n03591116	Jacob's ladder, jack ladder, pilot ladder
+n03591313	jaconet
+n03591592	Jacquard loom, Jacquard
+n03591798	jacquard
+n03591901	jag, dag
+n03592245	jail, jailhouse, gaol, clink, slammer, poky, pokey
+n03592669	jalousie
+n03592773	jamb
+n03592931	jammer
+n03593122	jampot, jamjar
+n03593222	japan
+n03593526	jar
+n03593862	Jarvik heart, Jarvik artificial heart
+n03594010	jaunting car, jaunty car
+n03594148	javelin
+n03594277	jaw
+n03594523	Jaws of Life
+n03594734	jean, blue jean, denim
+n03594945	jeep, landrover
+n03595055	jellaba
+n03595264	jerkin
+n03595409	jeroboam, double-magnum
+n03595523	jersey
+n03595614	jersey, T-shirt, tee shirt
+n03595860	jet, jet plane, jet-propelled plane
+n03596099	jet bridge
+n03596285	jet engine
+n03596543	jetliner
+n03597147	jeweler's glass
+n03597317	jewelled headdress, jeweled headdress
+n03597916	jew's harp, jews' harp, mouth bow
+n03598151	jib
+n03598299	jibboom
+n03598385	jig
+n03598515	jig
+n03598646	jiggermast, jigger
+n03598783	jigsaw, scroll saw, fretsaw
+n03598930	jigsaw puzzle
+n03599486	jinrikisha, ricksha, rickshaw
+n03599964	jobcentre
+n03600285	jodhpurs, jodhpur breeches, riding breeches
+n03600475	jodhpur, jodhpur boot, jodhpur shoe
+n03600722	joinery
+n03600977	joint
+n03601442	Joint Direct Attack Munition, JDAM
+n03601638	jointer, jointer plane, jointing plane, long plane
+n03601840	joist
+n03602081	jolly boat, jolly
+n03602194	jorum
+n03602365	joss house
+n03602686	journal bearing
+n03602790	journal box
+n03602883	joystick
+n03603442	jungle gym
+n03603594	junk
+n03603722	jug
+n03604156	jukebox, nickelodeon
+n03604311	jumbojet, jumbo jet
+n03604400	jumper, pinafore, pinny
+n03604536	jumper
+n03604629	jumper
+n03604763	jumper
+n03604843	jumper cable, jumper lead, lead, booster cable
+n03605417	jump seat
+n03605504	jump suit
+n03605598	jump suit, jumpsuit
+n03605722	junction
+n03605915	junction, conjunction
+n03606106	junction barrier, barrier strip
+n03606251	junk shop
+n03606347	jury box
+n03606465	jury mast
+n03607029	kachina
+n03607186	kaffiyeh
+n03607527	kalansuwa
+n03607659	Kalashnikov
+n03607923	kameez
+n03608504	kanzu
+n03609147	katharometer
+n03609235	kayak
+n03609397	kazoo
+n03609542	keel
+n03609786	keelboat
+n03609959	keelson
+n03610098	keep, donjon, dungeon
+n03610418	keg
+n03610524	kennel, doghouse, dog house
+n03610682	kepi, peaked cap, service cap, yachting cap
+n03610836	keratoscope
+n03610992	kerchief
+n03612010	ketch
+n03612814	kettle, boiler
+n03612965	kettle, kettledrum, tympanum, tympani, timpani
+n03613294	key
+n03613592	key
+n03614007	keyboard
+n03614383	keyboard buffer
+n03614532	keyboard instrument
+n03614782	keyhole
+n03614887	keyhole saw
+n03615300	khadi, khaddar
+n03615406	khaki
+n03615563	khakis
+n03615655	khimar
+n03615790	khukuri
+n03616091	kick pleat
+n03616225	kicksorter, pulse height analyzer
+n03616428	kickstand
+n03616763	kick starter, kick start
+n03616979	kid glove, suede glove
+n03617095	kiln
+n03617312	kilt
+n03617480	kimono
+n03617594	kinescope, picture tube, television tube
+n03617834	Kinetoscope
+n03618101	king
+n03618339	king
+n03618546	kingbolt, kingpin, swivel pin
+n03618678	king post
+n03618797	Kipp's apparatus
+n03618982	kirk
+n03619050	kirpan
+n03619196	kirtle
+n03619275	kirtle
+n03619396	kit, outfit
+n03619650	kit
+n03619793	kitbag, kit bag
+n03619890	kitchen
+n03620052	kitchen appliance
+n03620353	kitchenette
+n03620967	kitchen table
+n03621049	kitchen utensil
+n03621377	kitchenware
+n03621694	kite balloon
+n03622058	klaxon, claxon
+n03622401	klieg light
+n03622526	klystron
+n03622839	knee brace
+n03622931	knee-high, knee-hi
+n03623198	knee pad
+n03623338	knee piece
+n03623556	knife
+n03624134	knife
+n03624400	knife blade
+n03624767	knight, horse
+n03625355	knit
+n03625539	knitting machine
+n03625646	knitting needle
+n03625943	knitwear
+n03626115	knob, boss
+n03626272	knob, pommel
+n03626418	knobble
+n03626502	knobkerrie, knobkerry
+n03626760	knocker, doorknocker, rapper
+n03627232	knot
+n03627954	knuckle joint, hinge joint
+n03628071	kohl
+n03628215	koto
+n03628421	kraal
+n03628511	kremlin
+n03628728	kris, creese, crease
+n03628831	krummhorn, crumhorn, cromorne
+n03628984	Kundt's tube
+n03629100	Kurdistan
+n03629231	kurta
+n03629520	kylix, cylix
+n03629643	kymograph, cymograph
+n03630262	lab bench, laboratory bench
+n03630383	lab coat, laboratory coat
+n03631177	lace
+n03631811	lacquer
+n03631922	lacquerware
+n03632100	lacrosse ball
+n03632577	ladder-back
+n03632729	ladder-back, ladder-back chair
+n03632852	ladder truck, aerial ladder truck
+n03632963	ladies' room, powder room
+n03633091	ladle
+n03633341	lady chapel
+n03633632	lagerphone
+n03633886	lag screw, lag bolt
+n03634034	lake dwelling, pile dwelling
+n03634899	lally, lally column
+n03635032	lamasery
+n03635108	lambrequin
+n03635330	lame
+n03635516	laminar flow clean room
+n03635668	laminate
+n03635932	lamination
+n03636248	lamp
+n03636649	lamp
+n03637027	lamp house, lamphouse, lamp housing
+n03637181	lamppost
+n03637318	lampshade, lamp shade
+n03637480	lanai
+n03637787	lancet arch, lancet
+n03637898	lancet window
+n03638014	landau
+n03638180	lander
+n03638623	landing craft
+n03638743	landing flap
+n03638883	landing gear
+n03639077	landing net
+n03639230	landing skid
+n03639497	land line, landline
+n03639675	land mine, ground-emplaced mine, booby trap
+n03639880	land office
+n03640850	lanolin
+n03640988	lantern
+n03641569	lanyard, laniard
+n03641947	lap, lap covering
+n03642144	laparoscope
+n03642341	lapboard
+n03642444	lapel
+n03642573	lap joint, splice
+n03642806	laptop, laptop computer
+n03643149	laryngoscope
+n03643253	laser, optical maser
+n03643491	laser-guided bomb, LGB
+n03643737	laser printer
+n03643907	lash, thong
+n03644073	lashing
+n03644378	lasso, lariat, riata, reata
+n03644858	latch
+n03645011	latch, door latch
+n03645168	latchet
+n03645290	latchkey
+n03645577	lateen, lateen sail
+n03646020	latex paint, latex, rubber-base paint
+n03646148	lath
+n03646296	lathe
+n03646809	latrine
+n03646916	lattice, latticework, fretwork
+n03647423	launch
+n03647520	launcher, rocket launcher
+n03648219	laundry, wash, washing, washables
+n03648431	laundry cart
+n03648667	laundry truck
+n03649003	lavalava
+n03649161	lavaliere, lavalier, lavalliere
+n03649288	laver
+n03649674	lawn chair, garden chair
+n03649797	lawn furniture
+n03649909	lawn mower, mower
+n03650551	layette
+n03651388	lead-acid battery, lead-acid accumulator
+n03651605	lead-in
+n03651843	leading rein
+n03652100	lead pencil
+n03652389	leaf spring
+n03652729	lean-to
+n03652826	lean-to tent
+n03652932	leash, tether, lead
+n03653110	leatherette, imitation leather
+n03653220	leather strip
+n03653454	Leclanche cell
+n03653583	lectern, reading desk
+n03653740	lecture room
+n03653833	lederhosen
+n03653975	ledger board
+n03654576	leg
+n03654826	leg
+n03655072	legging, leging, leg covering
+n03655470	Leiden jar, Leyden jar
+n03655720	leisure wear
+n03656484	lens, lense, lens system
+n03656957	lens, electron lens
+n03657121	lens cap, lens cover
+n03657239	lens implant, interocular lens implant, IOL
+n03657511	leotard, unitard, body suit, cat suit
+n03658102	letter case
+n03658185	letter opener, paper knife, paperknife
+n03658635	levee
+n03658858	level, spirit level
+n03659292	lever
+n03659686	lever, lever tumbler
+n03659809	lever
+n03659950	lever lock
+n03660124	Levi's, levis
+n03660562	Liberty ship
+n03660909	library
+n03661043	library
+n03661340	lid
+n03662301	Liebig condenser
+n03662452	lie detector
+n03662601	lifeboat
+n03662719	life buoy, lifesaver, life belt, life ring
+n03662887	life jacket, life vest, cork jacket
+n03663433	life office
+n03663531	life preserver, preserver, flotation device
+n03663910	life-support system, life support
+n03664159	life-support system, life support
+n03664675	lifting device
+n03664840	lift pump
+n03664943	ligament
+n03665232	ligature
+n03665366	light, light source
+n03665851	light arm
+n03665924	light bulb, lightbulb, bulb, incandescent lamp, electric light, electric-light bulb
+n03666238	light circuit, lighting circuit
+n03666362	light-emitting diode, LED
+n03666591	lighter, light, igniter, ignitor
+n03666917	lighter-than-air craft
+n03667060	light filter, diffusing screen
+n03667235	lighting
+n03667552	light machine gun
+n03667664	light meter, exposure meter, photometer
+n03667829	light microscope
+n03668067	lightning rod, lightning conductor
+n03668279	light pen, electronic stylus
+n03668488	lightship
+n03668803	Lilo
+n03669245	limber
+n03669534	limekiln
+n03669886	limiter, clipper
+n03670208	limousine, limo
+n03671914	linear accelerator, linac
+n03672521	linen
+n03672827	line printer, line-at-a-time printer
+n03673027	liner, ocean liner
+n03673270	liner, lining
+n03673450	lingerie, intimate apparel
+n03673767	lining, liner
+n03674270	link, data link
+n03674440	linkage
+n03674731	Link trainer
+n03674842	linocut
+n03675076	linoleum knife, linoleum cutter
+n03675235	Linotype, Linotype machine
+n03675445	linsey-woolsey
+n03675558	linstock
+n03675907	lion-jaw forceps
+n03676087	lip-gloss
+n03676483	lipstick, lip rouge
+n03676623	liqueur glass
+n03676759	liquid crystal display, LCD
+n03677115	liquid metal reactor
+n03677682	lisle
+n03677766	lister, lister plow, lister plough, middlebreaker, middle buster
+n03678558	litterbin, litter basket, litter-basket
+n03678729	little theater, little theatre
+n03678879	live axle, driving axle
+n03679384	living quarters, quarters
+n03679712	living room, living-room, sitting room, front room, parlor, parlour
+n03680248	load
+n03680355	Loafer
+n03680512	loaner
+n03680734	lobe
+n03680858	lobster pot
+n03680942	local
+n03681477	local area network, LAN
+n03681813	local oscillator, heterodyne oscillator
+n03682380	Lochaber ax
+n03682487	lock
+n03682877	lock, ignition lock
+n03683079	lock, lock chamber
+n03683341	lock
+n03683457	lockage
+n03683606	locker
+n03683708	locker room
+n03683995	locket
+n03684143	lock-gate
+n03684224	locking pliers
+n03684489	lockring, lock ring, lock washer
+n03684611	lockstitch
+n03684740	lockup
+n03684823	locomotive, engine, locomotive engine, railway locomotive
+n03685307	lodge, indian lodge
+n03685486	lodge, hunting lodge
+n03685640	lodge
+n03685820	lodging house, rooming house
+n03686130	loft, attic, garret
+n03686363	loft, pigeon loft
+n03686470	loft
+n03686924	log cabin
+n03687137	loggia
+n03687928	longbow
+n03688066	long iron
+n03688192	long johns
+n03688405	long sleeve
+n03688504	long tom
+n03688605	long trousers, long pants
+n03688707	long underwear, union suit
+n03688832	looking glass, glass
+n03688943	lookout, observation tower, lookout station, observatory
+n03689157	loom
+n03689570	loop knot
+n03690168	lorgnette
+n03690279	Lorraine cross, cross of Lorraine
+n03690473	lorry, camion
+n03690851	lota
+n03690938	lotion
+n03691459	loudspeaker, speaker, speaker unit, loudspeaker system, speaker system
+n03691817	lounge, waiting room, waiting area
+n03692004	lounger
+n03692136	lounging jacket, smoking jacket
+n03692272	lounging pajama, lounging pyjama
+n03692379	loungewear
+n03692522	loupe, jeweler's loupe
+n03692842	louvered window, jalousie
+n03693293	love knot, lovers' knot, lover's knot, true lovers' knot, true lover's knot
+n03693474	love seat, loveseat, tete-a-tete, vis-a-vis
+n03693707	loving cup
+n03693860	lowboy
+n03694196	low-pass filter
+n03694356	low-warp-loom
+n03694639	LP, L-P
+n03694761	L-plate
+n03694949	lubber's hole
+n03695122	lubricating system, force-feed lubricating system, force feed, pressure-feed lubricating system, pressure feed
+n03695452	luff
+n03695616	lug
+n03695753	luge
+n03695857	Luger
+n03695957	luggage carrier
+n03696065	luggage compartment, automobile trunk, trunk
+n03696301	luggage rack, roof rack
+n03696445	lugger
+n03696568	lugsail, lug
+n03696746	lug wrench
+n03696909	lumberjack, lumber jacket
+n03697007	lumbermill, sawmill
+n03697366	lunar excursion module, lunar module, LEM
+n03697552	lunchroom
+n03697812	lunette
+n03697913	lungi, lungyi, longyi
+n03698123	lunula
+n03698226	lusterware
+n03698360	lute
+n03698604	luxury liner, express luxury liner
+n03698723	lyceum
+n03698815	lychgate, lichgate
+n03699280	lyre
+n03699591	machete, matchet, panga
+n03699754	machicolation
+n03699975	machine
+n03700963	machine, simple machine
+n03701191	machine bolt
+n03701391	machine gun
+n03701640	machinery
+n03701790	machine screw
+n03702248	machine tool
+n03702440	machinist's vise, metalworking vise
+n03702582	machmeter
+n03703075	mackinaw
+n03703203	mackinaw, Mackinaw boat
+n03703463	mackinaw, Mackinaw coat
+n03703590	mackintosh, macintosh
+n03703730	macrame
+n03703862	madras
+n03703945	Mae West, air jacket
+n03704549	magazine rack
+n03704834	magic lantern
+n03705379	magnet
+n03705808	magnetic bottle
+n03706229	magnetic compass
+n03706415	magnetic core memory, core memory
+n03706653	magnetic disk, magnetic disc, disk, disc
+n03706939	magnetic head
+n03707171	magnetic mine
+n03707372	magnetic needle
+n03707597	magnetic recorder
+n03707766	magnetic stripe
+n03708036	magnetic tape, mag tape, tape
+n03708425	magneto, magnetoelectric machine
+n03708843	magnetometer, gaussmeter
+n03708962	magnetron
+n03709206	magnifier
+n03709363	magnum
+n03709545	magnus hitch
+n03709644	mail
+n03709823	mailbag, postbag
+n03709960	mailbag, mail pouch
+n03710079	mailboat, mail boat, packet, packet boat
+n03710193	mailbox, letter box
+n03710294	mail car
+n03710421	maildrop
+n03710528	mailer
+n03710637	maillot
+n03710721	maillot, tank suit
+n03710937	mailsorter
+n03711044	mail train
+n03711711	mainframe, mainframe computer
+n03711999	mainmast
+n03712111	main rotor
+n03712337	mainsail
+n03712444	mainspring
+n03712887	main-topmast
+n03712981	main-topsail
+n03713069	main yard
+n03713151	maisonette, maisonnette
+n03713436	majolica, maiolica
+n03714235	makeup, make-up, war paint
+n03715114	Maksutov telescope
+n03715275	malacca, malacca cane
+n03715386	mallet, beetle
+n03715669	mallet, hammer
+n03715892	mallet
+n03716228	mammogram
+n03716887	mandola
+n03716966	mandolin
+n03717131	manger, trough
+n03717285	mangle
+n03717447	manhole
+n03717622	manhole cover
+n03718212	man-of-war, ship of the line
+n03718335	manometer
+n03718458	manor, manor house
+n03718581	manor hall, hall
+n03718699	MANPAD
+n03718789	mansard, mansard roof
+n03718935	manse
+n03719053	mansion, mansion house, manse, hall, residence
+n03719343	mantel, mantelpiece, mantle, mantlepiece, chimneypiece
+n03719560	mantelet, mantilla
+n03719743	mantilla
+n03720005	Mao jacket
+n03720163	map
+n03720665	maquiladora
+n03720891	maraca
+n03721047	marble
+n03721252	marching order
+n03721384	marimba, xylophone
+n03721590	marina
+n03722007	marker
+n03722288	marketplace, market place, mart, market
+n03722646	marlinespike, marlinspike, marlingspike
+n03722944	marocain, crepe marocain
+n03723153	marquee, marquise
+n03723267	marquetry, marqueterie
+n03723439	marriage bed
+n03723781	martello tower
+n03723885	martingale
+n03724066	mascara
+n03724176	maser
+n03724417	masher
+n03724538	mashie, five iron
+n03724623	mashie niblick, seven iron
+n03724756	masjid, musjid
+n03724870	mask
+n03725035	mask
+n03725506	Masonite
+n03725600	Mason jar
+n03725717	masonry
+n03725869	mason's level
+n03726116	massage parlor
+n03726233	massage parlor
+n03726371	mass spectrograph
+n03726516	mass spectrometer, spectrometer
+n03726760	mast
+n03726993	mast
+n03727067	mastaba, mastabah
+n03727465	master bedroom
+n03727605	masterpiece, chef-d'oeuvre
+n03727837	mat
+n03727946	mat, gym mat
+n03728437	match, lucifer, friction match
+n03728982	match
+n03729131	matchboard
+n03729308	matchbook
+n03729402	matchbox
+n03729482	matchlock
+n03729647	match plane, tonguing and grooving plane
+n03729826	matchstick
+n03729951	material
+n03730153	materiel, equipage
+n03730334	maternity hospital
+n03730494	maternity ward
+n03730655	matrix
+n03730788	Matthew Walker, Matthew Walker knot
+n03730893	matting
+n03731019	mattock
+n03731483	mattress cover
+n03731695	maul, sledge, sledgehammer
+n03731882	maulstick, mahlstick
+n03732020	Mauser
+n03732114	mausoleum
+n03732458	maxi
+n03732543	Maxim gun
+n03732658	maximum and minimum thermometer
+n03733131	maypole
+n03733281	maze, labyrinth
+n03733465	mazer
+n03733547	means
+n03733644	measure
+n03733805	measuring cup
+n03733925	measuring instrument, measuring system, measuring device
+n03735637	measuring stick, measure, measuring rod
+n03735963	meat counter
+n03736064	meat grinder
+n03736147	meat hook
+n03736269	meat house
+n03736372	meat safe
+n03736470	meat thermometer
+n03736970	mechanical device
+n03738066	mechanical piano, Pianola, player piano
+n03738241	mechanical system
+n03738472	mechanism
+n03739518	medical building, health facility, healthcare facility
+n03739693	medical instrument
+n03742019	medicine ball
+n03742115	medicine chest, medicine cabinet
+n03742238	MEDLINE
+n03743016	megalith, megalithic structure
+n03743279	megaphone
+n03743902	memorial, monument
+n03744276	memory, computer memory, storage, computer storage, store, memory board
+n03744684	memory chip
+n03744840	memory device, storage device
+n03745146	menagerie, zoo, zoological garden
+n03745487	mending
+n03745571	menhir, standing stone
+n03746005	menorah
+n03746155	Menorah
+n03746330	man's clothing
+n03746486	men's room, men's
+n03748162	mercantile establishment, retail store, sales outlet, outlet
+n03749504	mercury barometer
+n03749634	mercury cell
+n03749807	mercury thermometer, mercury-in-glass thermometer
+n03750206	mercury-vapor lamp
+n03750437	mercy seat
+n03750614	merlon
+n03751065	mess, mess hall
+n03751269	mess jacket, monkey jacket, shell jacket
+n03751458	mess kit
+n03751590	messuage
+n03751757	metal detector
+n03752071	metallic
+n03752185	metal screw
+n03752398	metal wood
+n03752922	meteorological balloon
+n03753077	meter
+n03753514	meterstick, metrestick
+n03757604	metronome
+n03758089	mezzanine, mezzanine floor, entresol
+n03758220	mezzanine, first balcony
+n03758894	microbalance
+n03758992	microbrewery
+n03759243	microfiche
+n03759432	microfilm
+n03759661	micrometer, micrometer gauge, micrometer caliper
+n03759954	microphone, mike
+n03760310	microprocessor
+n03760671	microscope
+n03760944	microtome
+n03761084	microwave, microwave oven
+n03761588	microwave diathermy machine
+n03761731	microwave linear accelerator
+n03762238	middy, middy blouse
+n03762332	midiron, two iron
+n03762434	mihrab
+n03762602	mihrab
+n03762982	military hospital
+n03763727	military quarters
+n03763968	military uniform
+n03764276	military vehicle
+n03764606	milk bar
+n03764736	milk can
+n03764822	milk float
+n03764995	milking machine
+n03765128	milking stool
+n03765467	milk wagon, milkwagon
+n03765561	mill, grinder, milling machinery
+n03765934	milldam
+n03766044	miller, milling machine
+n03766218	milliammeter
+n03766322	millinery, woman's hat
+n03766508	millinery, hat shop
+n03766600	milling
+n03766697	millivoltmeter
+n03766935	millstone
+n03767112	millstone
+n03767203	millwheel, mill wheel
+n03767459	mimeograph, mimeo, mimeograph machine, Roneo, Roneograph
+n03767745	minaret
+n03767966	mincer, mincing machine
+n03768132	mine
+n03768683	mine detector
+n03768823	minelayer
+n03768916	mineshaft
+n03769610	minibar, cellaret
+n03769722	minibike, motorbike
+n03769881	minibus
+n03770085	minicar
+n03770224	minicomputer
+n03770316	ministry
+n03770439	miniskirt, mini
+n03770520	minisub, minisubmarine
+n03770679	minivan
+n03770834	miniver
+n03770954	mink, mink coat
+n03772077	minster
+n03772269	mint
+n03772584	minute hand, big hand
+n03772674	Minuteman
+n03773035	mirror
+n03773504	missile
+n03773835	missile defense system, missile defence system
+n03774327	miter box, mitre box
+n03774461	miter joint, mitre joint, miter, mitre
+n03775071	mitten
+n03775199	mixer
+n03775388	mixer
+n03775546	mixing bowl
+n03775636	mixing faucet
+n03775747	mizzen, mizen
+n03775847	mizzenmast, mizenmast, mizzen, mizen
+n03776167	mobcap
+n03776460	mobile home, manufactured home
+n03776877	moccasin, mocassin
+n03776997	mock-up
+n03777126	mod con
+n03777568	Model T
+n03777754	modem
+n03778459	modillion
+n03778817	module
+n03779000	module
+n03779128	mohair
+n03779246	moire, watered-silk
+n03779370	mold, mould, cast
+n03779884	moldboard, mouldboard
+n03780047	moldboard plow, mouldboard plough
+n03780799	moleskin
+n03781055	Molotov cocktail, petrol bomb, gasoline bomb
+n03781244	monastery
+n03781467	monastic habit
+n03781594	moneybag
+n03781683	money belt
+n03781787	monitor
+n03782006	monitor
+n03782190	monitor, monitoring device
+n03782794	monkey-wrench, monkey wrench
+n03782929	monk's cloth
+n03783304	monochrome
+n03783430	monocle, eyeglass
+n03783575	monofocal lens implant, monofocal IOL
+n03783873	monoplane
+n03784139	monotype
+n03784270	monstrance, ostensorium
+n03784793	mooring tower, mooring mast
+n03784896	Moorish arch, horseshoe arch
+n03785016	moped
+n03785142	mop handle
+n03785237	moquette
+n03785499	morgue, mortuary, dead room
+n03785721	morion, cabasset
+n03786096	morning dress
+n03786194	morning dress
+n03786313	morning room
+n03786621	Morris chair
+n03786715	mortar, howitzer, trench mortar
+n03786901	mortar
+n03787032	mortarboard
+n03787523	mortise joint, mortise-and-tenon joint
+n03788047	mosaic
+n03788195	mosque
+n03788365	mosquito net
+n03788498	motel
+n03788601	motel room
+n03788914	Mother Hubbard, muumuu
+n03789171	motion-picture camera, movie camera, cine-camera
+n03789400	motion-picture film, movie film, cine-film
+n03789603	motley
+n03789794	motley
+n03789946	motor
+n03790230	motorboat, powerboat
+n03790512	motorcycle, bike
+n03790755	motor hotel, motor inn, motor lodge, tourist court, court
+n03790953	motorized wheelchair
+n03791053	motor scooter, scooter
+n03791235	motor vehicle, automotive vehicle
+n03792048	mound, hill
+n03792334	mound, hill, pitcher's mound
+n03792526	mount, setting
+n03792782	mountain bike, all-terrain bike, off-roader
+n03792972	mountain tent
+n03793489	mouse, computer mouse
+n03793850	mouse button
+n03794056	mousetrap
+n03794136	mousse, hair mousse, hair gel
+n03794798	mouthpiece, embouchure
+n03795123	mouthpiece
+n03795269	mouthpiece, gumshield
+n03795758	movement
+n03795976	movie projector, cine projector, film projector
+n03796181	moving-coil galvanometer
+n03796401	moving van
+n03796522	mud brick
+n03796605	mudguard, splash guard, splash-guard
+n03796848	mudhif
+n03796974	muff
+n03797062	muffle
+n03797182	muffler
+n03797264	mufti
+n03797390	mug
+n03797896	mulch
+n03798061	mule, scuff
+n03798442	multichannel recorder
+n03798610	multiengine airplane, multiengine plane
+n03798982	multiplex
+n03799113	multiplexer
+n03799240	multiprocessor
+n03799375	multistage rocket, step rocket
+n03799610	munition, ordnance, ordnance store
+n03799876	Murphy bed
+n03800371	musette, shepherd's pipe
+n03800485	musette pipe
+n03800563	museum
+n03800772	mushroom anchor
+n03800933	musical instrument, instrument
+n03801353	music box, musical box
+n03801533	music hall, vaudeville theater, vaudeville theatre
+n03801671	music school
+n03801760	music stand, music rack
+n03801880	music stool, piano stool
+n03802007	musket
+n03802228	musket ball, ball
+n03802393	muslin
+n03802643	mustache cup, moustache cup
+n03802800	mustard plaster, sinapism
+n03802973	mute
+n03803116	muzzle loader
+n03803284	muzzle
+n03803780	myelogram
+n03804211	nacelle
+n03804744	nail
+n03805180	nailbrush
+n03805280	nailfile
+n03805374	nailhead
+n03805503	nailhead
+n03805725	nail polish, nail enamel, nail varnish
+n03805933	nainsook
+n03807334	Napier's bones, Napier's rods
+n03809211	nard, spikenard
+n03809312	narrowbody aircraft, narrow-body aircraft, narrow-body
+n03809603	narrow wale
+n03809686	narthex
+n03809802	narthex
+n03810412	nasotracheal tube
+n03810952	national monument
+n03811295	nautilus, nuclear submarine, nuclear-powered submarine
+n03811444	navigational system
+n03811847	naval equipment
+n03811965	naval gun
+n03812263	naval missile
+n03812382	naval radar
+n03812789	naval tactical data system
+n03812924	naval weaponry
+n03813078	nave
+n03813176	navigational instrument
+n03813946	nebuchadnezzar
+n03814528	neckband
+n03814639	neck brace
+n03814727	neckcloth, stock
+n03814817	neckerchief
+n03814906	necklace
+n03815149	necklet
+n03815278	neckline
+n03815482	neckpiece
+n03815615	necktie, tie
+n03816005	neckwear
+n03816136	needle
+n03816394	needle
+n03816530	needlenose pliers
+n03816849	needlework, needlecraft
+n03817191	negative
+n03817331	negative magnetic pole, negative pole, south-seeking pole
+n03817522	negative pole
+n03817647	negligee, neglige, peignoir, wrapper, housecoat
+n03818001	neolith
+n03818343	neon lamp, neon induction lamp, neon tube
+n03819047	nephoscope
+n03819336	nest
+n03819448	nest egg
+n03819595	net, network, mesh, meshing, meshwork
+n03819994	net
+n03820154	net
+n03820318	net
+n03820728	network, electronic network
+n03820950	network
+n03821145	neutron bomb
+n03821424	newel
+n03821518	newel post, newel
+n03822171	newspaper, paper
+n03822361	newsroom
+n03822504	newsroom
+n03822656	newsstand
+n03822767	Newtonian telescope, Newtonian reflector
+n03823111	nib, pen nib
+n03823216	niblick, nine iron
+n03823312	nicad, nickel-cadmium accumulator
+n03823673	nickel-iron battery, nickel-iron accumulator
+n03823906	Nicol prism
+n03824197	night bell
+n03824284	nightcap
+n03824381	nightgown, gown, nightie, night-robe, nightdress
+n03824589	night latch
+n03824713	night-light
+n03824999	nightshirt
+n03825080	nightwear, sleepwear, nightclothes
+n03825271	ninepin, skittle, skittle pin
+n03825442	ninepin ball, skittle ball
+n03825673	ninon
+n03825788	nipple
+n03825913	nipple shield
+n03826039	niqab
+n03826186	Nissen hut, Quonset hut
+n03827420	nogging
+n03827536	noisemaker
+n03828020	nonsmoker, nonsmoking car
+n03829340	non-volatile storage, nonvolatile storage
+n03829857	Norfolk jacket
+n03829954	noria
+n03831203	nosebag, feedbag
+n03831382	noseband, nosepiece
+n03831757	nose flute
+n03832144	nosewheel
+n03832673	notebook, notebook computer
+n03833907	nuclear-powered ship
+n03834040	nuclear reactor, reactor
+n03834472	nuclear rocket
+n03834604	nuclear weapon, atomic weapon
+n03835197	nude, nude painting
+n03835729	numdah, numdah rug, nammad
+n03835941	nun's habit
+n03836062	nursery, baby's room
+n03836451	nut and bolt
+n03836602	nutcracker
+n03836906	nylon
+n03836976	nylons, nylon stocking, rayons, rayon stocking, silk stocking
+n03837422	oar
+n03837606	oast
+n03837698	oast house
+n03837869	obelisk
+n03838024	object ball
+n03838298	objective, objective lens, object lens, object glass
+n03838748	oblique bandage
+n03838899	oboe, hautboy, hautbois
+n03839172	oboe da caccia
+n03839276	oboe d'amore
+n03839424	observation dome
+n03839671	observatory
+n03839795	obstacle
+n03840327	obturator
+n03840681	ocarina, sweet potato
+n03840823	octant
+n03841011	odd-leg caliper
+n03841143	odometer, hodometer, mileometer, milometer
+n03841290	oeil de boeuf
+n03841666	office, business office
+n03842012	office building, office block
+n03842156	office furniture
+n03842276	officer's mess
+n03842377	off-line equipment, auxiliary equipment
+n03842585	ogee, cyma reversa
+n03842754	ogee arch, keel arch
+n03842986	ohmmeter
+n03843092	oil, oil color, oil colour
+n03843316	oilcan
+n03843438	oilcloth
+n03843555	oil filter
+n03843883	oil heater, oilstove, kerosene heater, kerosine heater
+n03844045	oil lamp, kerosene lamp, kerosine lamp
+n03844233	oil paint
+n03844550	oil pump
+n03844673	oil refinery, petroleum refinery
+n03844815	oilskin, slicker
+n03844965	oil slick
+n03845107	oilstone
+n03845190	oil tanker, oiler, tanker, tank ship
+n03845990	old school tie
+n03846100	olive drab
+n03846234	olive drab, olive-drab uniform
+n03846431	Olympian Zeus
+n03846677	omelet pan, omelette pan
+n03846772	omnidirectional antenna, nondirectional antenna
+n03846970	omnirange, omnidirectional range, omnidirectional radio range
+n03847471	onion dome
+n03847823	open-air market, open-air marketplace, market square
+n03848033	open circuit
+n03848168	open-end wrench, tappet wrench
+n03848348	opener
+n03848537	open-hearth furnace
+n03849275	openside plane, rabbet plane
+n03849412	open sight
+n03849679	openwork
+n03849814	opera, opera house
+n03849943	opera cloak, opera hood
+n03850053	operating microscope
+n03850245	operating room, OR, operating theater, operating theatre, surgery
+n03850492	operating table
+n03850613	ophthalmoscope
+n03851341	optical device
+n03851787	optical disk, optical disc
+n03852280	optical instrument
+n03852544	optical pyrometer, pyroscope
+n03852688	optical telescope
+n03853291	orchestra pit, pit
+n03853924	ordinary, ordinary bicycle
+n03854065	organ, pipe organ
+n03854421	organdy, organdie
+n03854506	organic light-emitting diode, OLED
+n03854722	organ loft
+n03854815	organ pipe, pipe, pipework
+n03855214	organza
+n03855333	oriel, oriel window
+n03855464	oriflamme
+n03855604	O ring
+n03855756	Orlon
+n03855908	orlop deck, orlop, fourth deck
+n03856012	orphanage, orphans' asylum
+n03856335	orphrey
+n03856465	orrery
+n03856728	orthicon, image orthicon
+n03857026	orthochromatic film
+n03857156	orthopter, ornithopter
+n03857291	orthoscope
+n03857687	oscillograph
+n03857828	oscilloscope, scope, cathode-ray oscilloscope, CRO
+n03858085	ossuary
+n03858183	otoscope, auriscope, auroscope
+n03858418	ottoman, pouf, pouffe, puff, hassock
+n03858533	oubliette
+n03858837	out-basket, out-tray
+n03859000	outboard motor, outboard
+n03859170	outboard motorboat, outboard
+n03859280	outbuilding
+n03859495	outerwear, overclothes
+n03859608	outfall
+n03859958	outfit, getup, rig, turnout
+n03860234	outfitter
+n03860404	outhouse, privy, earth-closet, jakes
+n03861048	output device
+n03861271	outrigger
+n03861430	outrigger canoe
+n03861596	outside caliper
+n03861842	outside mirror
+n03862379	outwork
+n03862676	oven
+n03862862	oven thermometer
+n03863108	overall
+n03863262	overall, boilersuit, boilers suit
+n03863657	overcoat, overcoating
+n03863783	overdrive
+n03863923	overgarment, outer garment
+n03864139	overhand knot
+n03864356	overhang
+n03864692	overhead projector
+n03865288	overmantel
+n03865371	overnighter, overnight bag, overnight case
+n03865557	overpass, flyover
+n03865820	override
+n03865949	overshoe
+n03866082	overskirt
+n03867854	oxbow
+n03868044	Oxbridge
+n03868242	oxcart
+n03868324	oxeye
+n03868406	oxford
+n03868643	oximeter
+n03868763	oxyacetylene torch
+n03868863	oxygen mask
+n03869838	oyster bar
+n03869976	oyster bed, oyster bank, oyster park
+n03870105	pace car
+n03870290	pacemaker, artificial pacemaker
+n03870546	pack
+n03870672	pack
+n03870980	pack, face pack
+n03871083	package, parcel
+n03871371	package store, liquor store, off-licence
+n03871524	packaging
+n03871628	packet
+n03871724	packing box, packing case
+n03871860	packinghouse, packing plant
+n03872016	packinghouse
+n03872167	packing needle
+n03872273	packsaddle
+n03873416	paddle, boat paddle
+n03873699	paddle
+n03873848	paddle
+n03873996	paddle box, paddle-box
+n03874138	paddle steamer, paddle-wheeler
+n03874293	paddlewheel, paddle wheel
+n03874487	paddock
+n03874599	padlock
+n03874823	page printer, page-at-a-time printer
+n03875218	paint, pigment
+n03875806	paintball
+n03875955	paintball gun
+n03876111	paintbox
+n03876231	paintbrush
+n03877351	paisley
+n03877472	pajama, pyjama, pj's, jammies
+n03877674	pajama, pyjama
+n03877845	palace
+n03878066	palace, castle
+n03878211	palace
+n03878294	palanquin, palankeen
+n03878418	paleolith
+n03878511	palestra, palaestra
+n03878674	palette, pallet
+n03878828	palette knife
+n03878963	palisade
+n03879456	pallet
+n03879705	pallette, palette
+n03880032	pallium
+n03880129	pallium
+n03880323	pan
+n03880531	pan, cooking pan
+n03881305	pancake turner
+n03881404	panchromatic film
+n03881534	panda car
+n03882611	paneling, panelling, pane
+n03882960	panhandle
+n03883054	panic button
+n03883385	pannier
+n03883524	pannier
+n03883664	pannikin
+n03883773	panopticon
+n03883944	panopticon
+n03884397	panpipe, pandean pipe, syrinx
+n03884554	pantaloon
+n03884639	pantechnicon
+n03884778	pantheon
+n03884926	pantheon
+n03885028	pantie, panty, scanty, step-in
+n03885194	panting, trousering
+n03885293	pant leg, trouser leg
+n03885410	pantograph
+n03885535	pantry, larder, buttery
+n03885669	pants suit, pantsuit
+n03885788	panty girdle
+n03885904	pantyhose
+n03886053	panzer
+n03886641	paper chain
+n03886762	paper clip, paperclip, gem clip
+n03886940	paper cutter
+n03887185	paper fastener
+n03887330	paper feed
+n03887512	paper mill
+n03887697	paper towel
+n03887899	parabolic mirror
+n03888022	parabolic reflector, paraboloid reflector
+n03888257	parachute, chute
+n03888605	parallel bars, bars
+n03888808	parallel circuit, shunt circuit
+n03888998	parallel interface, parallel port
+n03889397	parang
+n03889503	parapet, breastwork
+n03889626	parapet
+n03889726	parasail
+n03889871	parasol, sunshade
+n03890093	parer, paring knife
+n03890233	parfait glass
+n03890358	pargeting, pargetting, pargetry
+n03890514	pari-mutuel machine, totalizer, totaliser, totalizator, totalisator
+n03891051	parka, windbreaker, windcheater, anorak
+n03891251	park bench
+n03891332	parking meter
+n03891538	parlor, parlour
+n03892178	parquet, parquet floor
+n03892425	parquetry, parqueterie
+n03892557	parsonage, vicarage, rectory
+n03892728	Parsons table
+n03893935	partial denture
+n03894051	particle detector
+n03894379	partition, divider
+n03894677	parts bin
+n03894933	party line
+n03895038	party wall
+n03895170	parvis
+n03895866	passenger car, coach, carriage
+n03896103	passenger ship
+n03896233	passenger train
+n03896419	passenger van
+n03896526	passe-partout
+n03896628	passive matrix display
+n03896984	passkey, passe-partout, master key, master
+n03897130	pass-through
+n03897634	pastry cart
+n03897943	patch
+n03898129	patchcord
+n03898271	patchouli, patchouly, pachouli
+n03898395	patch pocket
+n03898633	patchwork, patchwork quilt
+n03898787	patent log, screw log, taffrail log
+n03899100	paternoster
+n03899612	patina
+n03899768	patio, terrace
+n03899933	patisserie
+n03900028	patka
+n03900194	patrol boat, patrol ship
+n03900301	patty-pan
+n03900393	pave
+n03900979	pavilion, marquee
+n03901229	pavior, paviour, paving machine
+n03901338	pavis, pavise
+n03901750	pawn
+n03901974	pawnbroker's shop, pawnshop, loan office
+n03902125	pay-phone, pay-station
+n03902220	PC board
+n03902482	peach orchard
+n03902756	pea jacket, peacoat
+n03903133	peavey, peavy, cant dog, dog hook
+n03903290	pectoral, pectoral medallion
+n03903424	pedal, treadle, foot pedal, foot lever
+n03903733	pedal pusher, toreador pants
+n03903868	pedestal, plinth, footstall
+n03904060	pedestal table
+n03904183	pedestrian crossing, zebra crossing
+n03904433	pedicab, cycle rickshaw
+n03904657	pediment
+n03904782	pedometer
+n03904909	peeler
+n03905361	peep sight
+n03905540	peg, nog
+n03905730	peg, pin, thole, tholepin, rowlock, oarlock
+n03905947	peg
+n03906106	peg, wooden leg, leg, pegleg
+n03906224	pegboard
+n03906463	Pelham
+n03906590	pelican crossing
+n03906789	pelisse
+n03906894	pelvimeter
+n03906997	pen
+n03907475	penal colony
+n03907654	penal institution, penal facility
+n03907908	penalty box
+n03908111	pen-and-ink
+n03908204	pencil
+n03908456	pencil
+n03908618	pencil box, pencil case
+n03908714	pencil sharpener
+n03909020	pendant earring, drop earring, eardrop
+n03909160	pendulum
+n03909406	pendulum clock
+n03909516	pendulum watch
+n03909658	penetration bomb
+n03911406	penile implant
+n03911513	penitentiary, pen
+n03911658	penknife
+n03911767	penlight
+n03911866	pennant, pennon, streamer, waft
+n03912218	pennywhistle, tin whistle, whistle
+n03912821	penthouse
+n03913343	pentode
+n03913930	peplos, peplus, peplum
+n03914106	peplum
+n03914337	pepper mill, pepper grinder
+n03914438	pepper shaker, pepper box, pepper pot
+n03914583	pepper spray
+n03914831	percale
+n03915118	percolator
+n03915320	percussion cap
+n03915437	percussion instrument, percussive instrument
+n03915900	perforation
+n03916031	perfume, essence
+n03916289	perfumery
+n03916385	perfumery
+n03916470	perfumery
+n03916720	peripheral, computer peripheral, peripheral device
+n03917048	periscope
+n03917198	peristyle
+n03917327	periwig, peruke
+n03917814	permanent press, durable press
+n03918074	perpetual motion machine
+n03918480	personal computer, PC, microcomputer
+n03918737	personal digital assistant, PDA, personal organizer, personal organiser, organizer, organiser
+n03919096	personnel carrier
+n03919289	pestle
+n03919430	pestle, muller, pounder
+n03919808	petcock
+n03920288	Petri dish
+n03920384	petrolatum gauze
+n03920641	pet shop
+n03920737	petticoat, half-slip, underskirt
+n03920867	pew, church bench
+n03923379	phial, vial, ampule, ampul, ampoule
+n03923564	Phillips screw
+n03923692	Phillips screwdriver
+n03923918	phonograph needle, needle
+n03924069	phonograph record, phonograph recording, record, disk, disc, platter
+n03924407	photocathode
+n03924532	photocoagulator
+n03924679	photocopier
+n03926148	photographic equipment
+n03926412	photographic paper, photographic material
+n03926876	photometer
+n03927091	photomicrograph
+n03927299	Photostat, Photostat machine
+n03927539	photostat
+n03927792	physical pendulum, compound pendulum
+n03928116	piano, pianoforte, forte-piano
+n03928589	piano action
+n03928814	piano keyboard, fingerboard, clavier
+n03928994	piano wire
+n03929091	piccolo
+n03929202	pick, pickax, pickaxe
+n03929443	pick
+n03929660	pick, plectrum, plectron
+n03929855	pickelhaube
+n03930229	picket boat
+n03930313	picket fence, paling
+n03930431	picket ship
+n03930515	pickle barrel
+n03930630	pickup, pickup truck
+n03931765	picture frame
+n03931885	picture hat
+n03931980	picture rail
+n03932080	picture window
+n03932670	piece of cloth, piece of material
+n03933391	pied-a-terre
+n03933933	pier
+n03934042	pier
+n03934229	pier arch
+n03934311	pier glass, pier mirror
+n03934565	pier table
+n03934656	pieta
+n03934890	piezometer
+n03935116	pig bed, pig
+n03935234	piggery, pig farm
+n03935335	piggy bank, penny bank
+n03935883	pilaster
+n03936269	pile, spile, piling, stilt
+n03936466	pile driver
+n03937543	pill bottle
+n03937835	pillbox, toque, turban
+n03937931	pillion
+n03938037	pillory
+n03938244	pillow
+n03938401	pillow block
+n03938522	pillow lace, bobbin lace
+n03938725	pillow sham
+n03939062	pilot bit
+n03939178	pilot boat
+n03939281	pilot burner, pilot light, pilot
+n03939440	pilot cloth
+n03939565	pilot engine
+n03939677	pilothouse, wheelhouse
+n03939844	pilot light, pilot lamp, indicator lamp
+n03940256	pin
+n03940894	pin, flag
+n03941013	pin, pin tumbler
+n03941231	pinata
+n03941417	pinball machine, pin table
+n03941586	pince-nez
+n03941684	pincer, pair of pincers, tweezer, pair of tweezers
+n03941887	pinch bar
+n03942028	pincurl clip
+n03942600	pinfold
+n03942813	ping-pong ball
+n03942920	pinhead
+n03943115	pinion
+n03943266	pinnacle
+n03943623	pinprick
+n03943714	pinstripe
+n03943833	pinstripe
+n03943920	pinstripe
+n03944024	pintle
+n03944138	pinwheel, pinwheel wind collector
+n03944341	pinwheel
+n03945459	tabor pipe
+n03945615	pipe
+n03945817	pipe bomb
+n03945928	pipe cleaner
+n03946076	pipe cutter
+n03946162	pipefitting, pipe fitting
+n03947111	pipet, pipette
+n03947343	pipe vise, pipe clamp
+n03947466	pipe wrench, tube wrench
+n03947798	pique
+n03947888	pirate, pirate ship
+n03948242	piste
+n03948459	pistol, handgun, side arm, shooting iron
+n03948830	pistol grip
+n03948950	piston, plunger
+n03949145	piston ring
+n03949317	piston rod
+n03949761	pit
+n03950228	pitcher, ewer
+n03950359	pitchfork
+n03950537	pitching wedge
+n03950647	pitch pipe
+n03950899	pith hat, pith helmet, sun helmet, topee, topi
+n03951068	piton
+n03951213	Pitot-static tube, Pitot head, Pitot tube
+n03951453	Pitot tube, Pitot
+n03951800	pitsaw
+n03951971	pivot, pin
+n03952150	pivoting window
+n03952576	pizzeria, pizza shop, pizza parlor
+n03953020	place of business, business establishment
+n03953416	place of worship, house of prayer, house of God, house of worship
+n03953901	placket
+n03954393	planchet, coin blank
+n03954731	plane, carpenter's plane, woodworking plane
+n03955296	plane, planer, planing machine
+n03955489	plane seat
+n03955809	planetarium
+n03955941	planetarium
+n03956157	planetarium
+n03956331	planetary gear, epicyclic gear, planet wheel, planet gear
+n03956531	plank-bed
+n03956623	planking
+n03956785	planner
+n03956922	plant, works, industrial plant
+n03957315	planter
+n03957420	plaster, adhesive plaster, sticking plaster
+n03957762	plasterboard, gypsum board
+n03957991	plastering trowel
+n03958227	plastic bag
+n03958338	plastic bomb
+n03958630	plastic laminate
+n03958752	plastic wrap
+n03959014	plastron
+n03959123	plastron
+n03959227	plastron
+n03959701	plate, scale, shell
+n03960374	plate, collection plate
+n03960490	plate
+n03961394	platen
+n03961630	platen
+n03961711	plate rack
+n03961828	plate rail
+n03961939	platform
+n03962525	platform, weapons platform
+n03962685	platform
+n03962852	platform bed
+n03962932	platform rocker
+n03963028	plating, metal plating
+n03963198	platter
+n03963294	playback
+n03963483	playbox, play-box
+n03963645	playground
+n03964495	playpen, pen
+n03964611	playsuit
+n03965456	plaza, mall, center, shopping mall, shopping center, shopping centre
+n03965907	pleat, plait
+n03966206	plenum
+n03966325	plethysmograph
+n03966582	pleximeter, plessimeter
+n03966751	plexor, plessor, percussor
+n03966976	pliers, pair of pliers, plyers
+n03967270	plimsoll
+n03967396	plotter
+n03967562	plow, plough
+n03967942	plug, stopper, stopple
+n03968293	plug, male plug
+n03968479	plug fuse
+n03968581	plughole
+n03968728	plumb bob, plumb, plummet
+n03969510	plumb level
+n03970156	plunger, plumber's helper
+n03970363	plus fours
+n03970546	plush
+n03971218	plywood, plyboard
+n03971321	pneumatic drill
+n03971960	p-n junction
+n03972146	p-n-p transistor
+n03972372	poacher
+n03972524	pocket
+n03973003	pocket battleship
+n03973285	pocketcomb, pocket comb
+n03973402	pocket flap
+n03973520	pocket-handkerchief
+n03973628	pocketknife, pocket knife
+n03973839	pocket watch
+n03973945	pod, fuel pod
+n03974070	pogo stick
+n03974915	point-and-shoot camera
+n03975035	pointed arch
+n03975657	pointing trowel
+n03975788	point lace, needlepoint
+n03975926	poker, stove poker, fire hook, salamander
+n03976105	polarimeter, polariscope
+n03976268	Polaroid
+n03976467	Polaroid camera, Polaroid Land camera
+n03976657	pole
+n03977158	pole
+n03977266	poleax, poleaxe
+n03977430	poleax, poleaxe
+n03977592	police boat
+n03977966	police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria
+n03978421	polling booth
+n03978575	polo ball
+n03978686	polo mallet, polo stick
+n03978815	polonaise
+n03978966	polo shirt, sport shirt
+n03979377	polyester
+n03979492	polygraph
+n03980026	pomade, pomatum
+n03980478	pommel horse, side horse
+n03980874	poncho
+n03980986	pongee
+n03981094	poniard, bodkin
+n03981340	pontifical
+n03981566	pontoon
+n03981760	pontoon bridge, bateau bridge, floating bridge
+n03981924	pony cart, ponycart, donkey cart, tub-cart
+n03982232	pool ball
+n03982331	poolroom
+n03982430	pool table, billiard table, snooker table
+n03982642	poop deck
+n03982767	poor box, alms box, mite box
+n03982895	poorhouse
+n03983396	pop bottle, soda bottle
+n03983499	popgun
+n03983612	poplin
+n03983712	popper
+n03983928	poppet, poppet valve
+n03984125	pop tent
+n03984234	porcelain
+n03984381	porch
+n03984643	porkpie, porkpie hat
+n03984759	porringer
+n03985069	portable
+n03985232	portable computer
+n03985441	portable circular saw, portable saw
+n03985881	portcullis
+n03986071	porte-cochere
+n03986224	porte-cochere
+n03986355	portfolio
+n03986562	porthole
+n03986704	portico
+n03986857	portiere
+n03986949	portmanteau, Gladstone, Gladstone bag
+n03987266	portrait camera
+n03987376	portrait lens
+n03987674	positive pole, positive magnetic pole, north-seeking pole
+n03987865	positive pole
+n03987990	positron emission tomography scanner, PET scanner
+n03988170	post
+n03988758	postage meter
+n03988926	post and lintel
+n03989199	post chaise
+n03989349	postern
+n03989447	post exchange, PX
+n03989665	posthole digger, post-hole digger
+n03989777	post horn
+n03989898	posthouse, post house
+n03990474	pot
+n03991062	pot, flowerpot
+n03991202	potbelly, potbelly stove
+n03991321	Potemkin village
+n03991443	potential divider, voltage divider
+n03991646	potentiometer, pot
+n03991837	potentiometer
+n03992325	potpourri
+n03992436	potsherd
+n03992509	potter's wheel
+n03992703	pottery, clayware
+n03992975	pottle
+n03993053	potty seat, potty chair
+n03993180	pouch
+n03993403	poultice, cataplasm, plaster
+n03993703	pound, dog pound
+n03993878	pound net
+n03994008	powder
+n03994297	powder and shot
+n03994417	powdered mustard, dry mustard
+n03994614	powder horn, powder flask
+n03994757	powder keg
+n03995018	power brake
+n03995265	power cord
+n03995372	power drill
+n03995535	power line, power cable
+n03995661	power loom
+n03995856	power mower, motor mower
+n03996004	power pack
+n03996145	power saw, saw, sawing machine
+n03996416	power shovel, excavator, digger, shovel
+n03996849	power steering, power-assisted steering
+n03997274	power takeoff, PTO
+n03997484	power tool
+n03997875	praetorium, pretorium
+n03998194	prayer rug, prayer mat
+n03998333	prayer shawl, tallith, tallis
+n03998673	precipitator, electrostatic precipitator, Cottrell precipitator
+n03999064	prefab
+n03999160	presbytery
+n03999621	presence chamber
+n03999992	press, mechanical press
+n04000311	press, printing press
+n04000480	press
+n04000592	press box
+n04000716	press gallery
+n04000998	press of sail, press of canvas
+n04001132	pressure cabin
+n04001265	pressure cooker
+n04001397	pressure dome
+n04001499	pressure gauge, pressure gage
+n04001661	pressurized water reactor, PWR
+n04001845	pressure suit
+n04002262	pricket
+n04002371	prie-dieu
+n04002629	primary coil, primary winding, primary
+n04003241	Primus stove, Primus
+n04003359	Prince Albert
+n04003856	print
+n04004099	print buffer
+n04004210	printed circuit
+n04004475	printer, printing machine
+n04004767	printer
+n04004990	printer cable
+n04005197	priory
+n04005630	prison, prison house
+n04005912	prison camp, internment camp, prisoner of war camp, POW camp
+n04006067	privateer
+n04006227	private line
+n04006330	privet hedge
+n04006411	probe
+n04007415	proctoscope
+n04007664	prod, goad
+n04008385	production line, assembly line, line
+n04008634	projectile, missile
+n04009552	projector
+n04009801	projector
+n04009923	prolonge
+n04010057	prolonge knot, sailor's breastplate
+n04010779	prompter, autocue
+n04010927	prong
+n04011827	propeller, propellor
+n04012084	propeller plane
+n04012482	propjet, turboprop, turbo-propeller plane
+n04012665	proportional counter tube, proportional counter
+n04013060	propulsion system
+n04013176	proscenium, proscenium wall
+n04013600	proscenium arch
+n04013729	prosthesis, prosthetic device
+n04014297	protective covering, protective cover, protection
+n04015204	protective garment
+n04015786	proton accelerator
+n04015908	protractor
+n04016240	pruner, pruning hook, lopper
+n04016479	pruning knife
+n04016576	pruning saw
+n04016684	pruning shears
+n04016846	psaltery
+n04017571	psychrometer
+n04017807	PT boat, mosquito boat, mosquito craft, motor torpedo boat
+n04018155	public address system, P.A. system, PA system, P.A., PA
+n04018399	public house, pub, saloon, pothouse, gin mill, taphouse
+n04018667	public toilet, comfort station, public convenience, convenience, public lavatory, restroom, toilet facility, wash room
+n04019101	public transport
+n04019335	public works
+n04019541	puck, hockey puck
+n04019696	pull
+n04019881	pullback, tieback
+n04020087	pull chain
+n04020298	pulley, pulley-block, pulley block, block
+n04020744	pull-off, rest area, rest stop, layby, lay-by
+n04020912	Pullman, Pullman car
+n04021028	pullover, slipover
+n04021164	pull-through
+n04021362	pulse counter
+n04021503	pulse generator
+n04021704	pulse timing circuit
+n04021798	pump
+n04022332	pump
+n04022434	pump action, slide action
+n04022708	pump house, pumping station
+n04022866	pump room
+n04023021	pump-type pliers
+n04023119	pump well
+n04023249	punch, puncher
+n04023422	punchboard
+n04023695	punch bowl
+n04023962	punching bag, punch bag, punching ball, punchball
+n04024137	punch pliers
+n04024274	punch press
+n04024862	punnet
+n04024983	punt
+n04025508	pup tent, shelter tent
+n04025633	purdah
+n04026053	purifier
+n04026180	purl, purl stitch
+n04026417	purse
+n04026813	push-bike
+n04026918	push broom
+n04027023	push button, push, button
+n04027367	push-button radio
+n04027706	pusher, zori
+n04027820	put-put
+n04027935	puttee
+n04028074	putter, putting iron
+n04028221	putty knife
+n04028315	puzzle
+n04028581	pylon, power pylon
+n04028764	pylon
+n04029416	pyramidal tent
+n04029647	pyrograph
+n04029734	pyrometer
+n04029913	pyrometric cone
+n04030054	pyrostat
+n04030161	pyx, pix
+n04030274	pyx, pix, pyx chest, pix chest
+n04030414	pyxis
+n04030518	quad, quadrangle
+n04030846	quadrant
+n04030965	quadraphony, quadraphonic system, quadriphonic system
+n04031884	quartering
+n04032509	quarterstaff
+n04032603	quartz battery, quartz mill
+n04032936	quartz lamp
+n04033287	queen
+n04033425	queen
+n04033557	queen post
+n04033801	quern
+n04033901	quill, quill pen
+n04033995	quilt, comforter, comfort, puff
+n04034262	quilted bedspread
+n04034367	quilting
+n04035231	quipu
+n04035634	quirk molding, quirk moulding
+n04035748	quirt
+n04035836	quiver
+n04035912	quoin, coign, coigne
+n04036155	quoit
+n04036303	QWERTY keyboard
+n04036776	rabbet, rebate
+n04036963	rabbet joint
+n04037076	rabbit ears
+n04037220	rabbit hutch
+n04037298	raceabout
+n04037443	racer, race car, racing car
+n04037873	raceway, race
+n04037964	racing boat
+n04038231	racing gig
+n04038338	racing skiff, single shell
+n04038440	rack, stand
+n04038727	rack
+n04039041	rack, wheel
+n04039209	rack and pinion
+n04039381	racket, racquet
+n04039742	racquetball
+n04039848	radar, microwave radar, radio detection and ranging, radiolocation
+n04040247	radial, radial tire, radial-ply tire
+n04040373	radial engine, rotary engine
+n04040540	radiation pyrometer
+n04040759	radiator
+n04041069	radiator
+n04041243	radiator cap
+n04041408	radiator hose
+n04041544	radio, wireless
+n04041747	radio antenna, radio aerial
+n04042076	radio chassis
+n04042204	radio compass
+n04042358	radiogram, radiograph, shadowgraph, skiagraph, skiagram
+n04042632	radio interferometer
+n04042795	radio link, link
+n04042985	radiometer
+n04043168	radiomicrometer
+n04043411	radio-phonograph, radio-gramophone
+n04043733	radio receiver, receiving set, radio set, radio, tuner, wireless
+n04044307	radiotelegraph, radiotelegraphy, wireless telegraph, wireless telegraphy
+n04044498	radiotelephone, radiophone, wireless telephone
+n04044716	radio telescope, radio reflector
+n04044955	radiotherapy equipment
+n04045085	radio transmitter
+n04045255	radome, radar dome
+n04045397	raft
+n04045644	rafter, balk, baulk
+n04045787	raft foundation
+n04045941	rag, shred, tag, tag end, tatter
+n04046091	ragbag
+n04046277	raglan
+n04046400	raglan sleeve
+n04046590	rail
+n04046974	rail fence
+n04047139	railhead
+n04047401	railing, rail
+n04047733	railing
+n04047834	railroad bed
+n04048441	railroad tunnel
+n04049303	rain barrel
+n04049405	raincoat, waterproof
+n04049585	rain gauge, rain gage, pluviometer, udometer
+n04049753	rain stick
+n04050066	rake
+n04050313	rake handle
+n04050600	RAM disk
+n04050933	ramekin, ramequin
+n04051269	ramjet, ramjet engine, atherodyde, athodyd, flying drainpipe
+n04051439	rammer
+n04051549	ramp, incline
+n04051705	rampant arch
+n04051825	rampart, bulwark, wall
+n04052235	ramrod
+n04052346	ramrod
+n04052442	ranch, spread, cattle ranch, cattle farm
+n04052658	ranch house
+n04052757	random-access memory, random access memory, random memory, RAM, read/write memory
+n04053508	rangefinder, range finder
+n04053677	range hood
+n04053767	range pole, ranging pole, flagpole
+n04054361	rapier, tuck
+n04054566	rariora
+n04054670	rasp, wood file
+n04055180	ratchet, rachet, ratch
+n04055447	ratchet wheel
+n04055700	rathskeller
+n04055861	ratline, ratlin
+n04056073	rat-tail file
+n04056180	rattan, ratan
+n04056413	rattrap
+n04056932	rayon
+n04057047	razor
+n04057215	razorblade
+n04057435	reaction-propulsion engine, reaction engine
+n04057673	reaction turbine
+n04057846	reactor
+n04057981	reading lamp
+n04058096	reading room
+n04058239	read-only memory, ROM, read-only storage, fixed storage
+n04058486	read-only memory chip
+n04058594	readout, read-out
+n04058721	read/write head, head
+n04059157	ready-to-wear
+n04059298	real storage
+n04059399	reamer
+n04059516	reamer, juicer, juice reamer
+n04059947	rearview mirror
+n04060198	Reaumur thermometer
+n04060448	rebozo
+n04060647	receiver, receiving system
+n04060904	receptacle
+n04061681	reception desk
+n04061793	reception room
+n04061969	recess, niche
+n04062179	reciprocating engine
+n04062428	recliner, reclining chair, lounger
+n04062644	reconnaissance plane
+n04062807	reconnaissance vehicle, scout car
+n04063154	record changer, auto-changer, changer
+n04063373	recorder, recording equipment, recording machine
+n04063868	recording
+n04064213	recording system
+n04064401	record player, phonograph
+n04064747	record sleeve, record cover
+n04064862	recovery room
+n04065272	recreational vehicle, RV, R.V.
+n04065464	recreation room, rec room
+n04065789	recycling bin
+n04065909	recycling plant
+n04066023	redbrick university
+n04066270	red carpet
+n04066388	redoubt
+n04066476	redoubt
+n04066767	reduction gear
+n04067143	reed pipe
+n04067231	reed stop
+n04067353	reef knot, flat knot
+n04067472	reel
+n04067658	reel
+n04067818	refectory
+n04067921	refectory table
+n04068441	refinery
+n04068601	reflecting telescope, reflector
+n04069166	reflectometer
+n04069276	reflector
+n04069434	reflex camera
+n04069582	reflux condenser
+n04069777	reformatory, reform school, training school
+n04070003	reformer
+n04070207	refracting telescope
+n04070415	refractometer
+n04070545	refrigeration system
+n04070727	refrigerator, icebox
+n04070964	refrigerator car
+n04071102	refuge, sanctuary, asylum
+n04071263	regalia
+n04071393	regimentals
+n04072193	regulator
+n04072551	rein
+n04072960	relay, electrical relay
+n04073425	release, button
+n04073948	religious residence, cloister
+n04074185	reliquary
+n04074963	remote control, remote
+n04075291	remote terminal, link-attached terminal, remote station, link-attached station
+n04075468	removable disk
+n04075715	rendering
+n04075813	rep, repp
+n04075916	repair shop, fix-it shop
+n04076052	repeater
+n04076284	repeating firearm, repeater
+n04076713	repository, monument
+n04077430	reproducer
+n04077594	rerebrace, upper cannon
+n04077734	rescue equipment
+n04077889	research center, research facility
+n04078002	reseau
+n04078574	reservoir
+n04078955	reset
+n04079106	reset button
+n04079244	residence
+n04079603	resistance pyrometer
+n04079933	resistor, resistance
+n04080138	resonator
+n04080454	resonator, cavity resonator, resonating chamber
+n04080705	resort hotel, spa
+n04080833	respirator, inhalator
+n04081281	restaurant, eating house, eating place, eatery
+n04081699	rest house
+n04081844	restraint, constraint
+n04082344	resuscitator
+n04082562	retainer
+n04082710	retaining wall
+n04082886	reticle, reticule, graticule
+n04083113	reticulation
+n04083309	reticule
+n04083649	retort
+n04083800	retractor
+n04084517	return key, return
+n04084682	reverberatory furnace
+n04084889	revers, revere
+n04085017	reverse, reverse gear
+n04085574	reversible
+n04085873	revetment, revetement, stone facing
+n04086066	revetment
+n04086273	revolver, six-gun, six-shooter
+n04086446	revolving door, revolver
+n04086663	rheometer
+n04086794	rheostat, variable resistor
+n04086937	rhinoscope
+n04087126	rib
+n04087432	riband, ribband
+n04087709	ribbed vault
+n04087826	ribbing
+n04088229	ribbon development
+n04088343	rib joint pliers
+n04088441	ricer
+n04088696	riddle
+n04088797	ride
+n04089152	ridge, ridgepole, rooftree
+n04089376	ridge rope
+n04089666	riding boot
+n04089836	riding crop, hunting crop
+n04089976	riding mower
+n04090263	rifle
+n04090548	rifle ball
+n04090781	rifle grenade
+n04091097	rig
+n04091466	rigger, rigger brush
+n04091584	rigger
+n04091693	rigging, tackle
+n04092168	rigout
+n04093157	ringlet
+n04093223	rings
+n04093625	rink, skating rink
+n04093775	riot gun
+n04093915	ripcord
+n04094060	ripcord
+n04094250	ripping bar
+n04094438	ripping chisel
+n04094608	ripsaw, splitsaw
+n04094720	riser
+n04094859	riser, riser pipe, riser pipeline, riser main
+n04095109	Ritz
+n04095210	river boat
+n04095342	rivet
+n04095577	riveting machine, riveter, rivetter
+n04095938	roach clip, roach holder
+n04096066	road, route
+n04096733	roadbed
+n04096848	roadblock, barricade
+n04097085	roadhouse
+n04097373	roadster, runabout, two-seater
+n04097622	roadway
+n04097760	roaster
+n04097866	robe
+n04098169	robotics equipment
+n04098260	Rochon prism, Wollaston prism
+n04098399	rock bit, roller bit
+n04098513	rocker
+n04098795	rocker, cradle
+n04099003	rocker arm, valve rocker
+n04099175	rocket, rocket engine
+n04099429	rocket, projectile
+n04099969	rocking chair, rocker
+n04100174	rod
+n04100519	rodeo
+n04101375	roll
+n04101497	roller
+n04101701	roller
+n04101860	roller bandage
+n04102037	in-line skate
+n04102162	Rollerblade
+n04102285	roller blind
+n04102406	roller coaster, big dipper, chute-the-chute
+n04102618	roller skate
+n04102760	roller towel
+n04102872	roll film
+n04102962	rolling hitch
+n04103094	rolling mill
+n04103206	rolling pin
+n04103364	rolling stock
+n04103665	roll-on
+n04103769	roll-on
+n04103918	roll-on roll-off
+n04104147	Rolodex
+n04104384	Roman arch, semicircular arch
+n04104500	Roman building
+n04104770	romper, romper suit
+n04104925	rood screen
+n04105068	roof
+n04105438	roof
+n04105704	roofing
+n04105893	room
+n04107598	roomette
+n04107743	room light
+n04107984	roost
+n04108268	rope
+n04108822	rope bridge
+n04108999	rope tow
+n04110068	rose water
+n04110178	rose window, rosette
+n04110281	rosin bag
+n04110439	rotary actuator, positioner
+n04110654	rotary engine
+n04110841	rotary press
+n04110955	rotating mechanism
+n04111190	rotating shaft, shaft
+n04111414	rotisserie
+n04111531	rotisserie
+n04111668	rotor
+n04111962	rotor, rotor coil
+n04112147	rotor
+n04112252	rotor blade, rotary wing
+n04112430	rotor head, rotor shaft
+n04112579	rotunda
+n04112654	rotunda
+n04112752	rouge, paint, blusher
+n04112921	roughcast
+n04113038	rouleau
+n04113194	roulette, toothed wheel
+n04113316	roulette ball
+n04113406	roulette wheel, wheel
+n04113641	round, unit of ammunition, one shot
+n04113765	round arch
+n04113968	round-bottom flask
+n04114069	roundel
+n04114301	round file
+n04114428	roundhouse
+n04114719	router
+n04114844	router
+n04114996	router plane
+n04115144	rowel
+n04115256	row house, town house
+n04115456	rowing boat
+n04115542	rowlock arch
+n04115802	royal
+n04115996	royal mast
+n04116098	rubber band, elastic band, elastic
+n04116294	rubber boot, gum boot
+n04116389	rubber bullet
+n04116512	rubber eraser, rubber, pencil eraser
+n04117216	rudder
+n04117464	rudder
+n04117639	rudder blade
+n04118021	rug, carpet, carpeting
+n04118538	rugby ball
+n04118635	ruin
+n04118776	rule, ruler
+n04119091	rumble
+n04119230	rumble seat
+n04119360	rummer
+n04119478	rumpus room, playroom, game room
+n04119630	runcible spoon
+n04119751	rundle, spoke, rung
+n04120489	running shoe
+n04120695	running suit
+n04120842	runway
+n04121228	rushlight, rush candle
+n04121342	russet
+n04121426	rya, rya rug
+n04121511	saber, sabre
+n04121728	saber saw, jigsaw, reciprocating saw
+n04122262	sable
+n04122349	sable, sable brush, sable's hair pencil
+n04122492	sable coat
+n04122578	sabot, wooden shoe
+n04122685	sachet
+n04122825	sack, poke, paper bag, carrier bag
+n04123026	sack, sacque
+n04123123	sackbut
+n04123228	sackcloth
+n04123317	sackcloth
+n04123448	sack coat
+n04123567	sacking, bagging
+n04123740	saddle
+n04124098	saddlebag
+n04124202	saddle blanket, saddlecloth, horse blanket
+n04124370	saddle oxford, saddle shoe
+n04124488	saddlery
+n04124573	saddle seat
+n04124887	saddle stitch
+n04125021	safe
+n04125116	safe
+n04125257	safe-deposit, safe-deposit box, safety-deposit, safety deposit box, deposit box, lockbox
+n04125541	safe house
+n04125692	safety arch
+n04125853	safety belt, life belt, safety harness
+n04126066	safety bicycle, safety bike
+n04126244	safety bolt, safety lock
+n04126541	safety curtain
+n04126659	safety fuse
+n04126852	safety lamp, Davy lamp
+n04126980	safety match, book matches
+n04127117	safety net
+n04127249	safety pin
+n04127395	safety rail, guardrail
+n04127521	safety razor
+n04127633	safety valve, relief valve, escape valve, escape cock, escape
+n04127904	sail, canvas, canvass, sheet
+n04128413	sail
+n04128499	sailboat, sailing boat
+n04128710	sailcloth
+n04128837	sailing vessel, sailing ship
+n04129490	sailing warship
+n04129688	sailor cap
+n04129766	sailor suit
+n04130143	salad bar
+n04130257	salad bowl
+n04130566	salinometer
+n04130907	sallet, salade
+n04131015	salon
+n04131113	salon
+n04131208	salon, beauty salon, beauty parlor, beauty parlour, beauty shop
+n04131368	saltbox
+n04131499	saltcellar
+n04131690	saltshaker, salt shaker
+n04131811	saltworks
+n04131929	salver
+n04132158	salwar, shalwar
+n04132465	Sam Browne belt
+n04132603	samisen, shamisen
+n04132829	samite
+n04132985	samovar
+n04133114	sampan
+n04133789	sandal
+n04134008	sandbag
+n04134170	sandblaster
+n04134523	sandbox
+n04134632	sandglass
+n04135024	sand wedge
+n04135118	sandwich board
+n04135315	sanitary napkin, sanitary towel, Kotex
+n04135710	cling film, clingfilm, Saran Wrap
+n04135933	sarcenet, sarsenet
+n04136045	sarcophagus
+n04136161	sari, saree
+n04136333	sarong
+n04136510	sash, window sash
+n04136800	sash fastener, sash lock, window lock
+n04137089	sash window
+n04137217	satchel
+n04137355	sateen
+n04137444	satellite, artificial satellite, orbiter
+n04137773	satellite receiver
+n04137897	satellite television, satellite TV
+n04138131	satellite transmitter
+n04138261	satin
+n04138869	Saturday night special
+n04138977	saucepan
+n04139140	saucepot
+n04139395	sauna, sweat room
+n04139859	savings bank, coin bank, money box, bank
+n04140064	saw
+n04140539	sawed-off shotgun
+n04140631	sawhorse, horse, sawbuck, buck
+n04140777	sawmill
+n04140853	saw set
+n04141076	sax, saxophone
+n04141198	saxhorn
+n04141327	scabbard
+n04141712	scaffolding, staging
+n04141838	scale
+n04141975	scale, weighing machine
+n04142175	scaler
+n04142327	scaling ladder
+n04142434	scalpel
+n04142731	scanner, electronic scanner
+n04142999	scanner
+n04143140	scanner, digital scanner, image scanner
+n04143365	scantling, stud
+n04143897	scarf
+n04144241	scarf joint, scarf
+n04144539	scatter rug, throw rug
+n04144651	scauper, scorper
+n04145863	Schmidt telescope, Schmidt camera
+n04146050	school, schoolhouse
+n04146343	schoolbag
+n04146504	school bell
+n04146614	school bus
+n04146862	school ship, training ship
+n04146976	school system
+n04147183	schooner
+n04147291	schooner
+n04147495	scientific instrument
+n04147793	scimitar
+n04147916	scintillation counter
+n04148054	scissors, pair of scissors
+n04148285	sclerometer
+n04148464	scoinson arch, sconcheon arch
+n04148579	sconce
+n04148703	sconce
+n04149083	scoop
+n04149374	scooter
+n04149813	scoreboard
+n04150153	scouring pad
+n04150273	scow
+n04150371	scow
+n04150980	scraper
+n04151108	scratcher
+n04151581	screen
+n04151940	screen, cover, covert, concealment
+n04152387	screen
+n04152593	screen, CRT screen
+n04153025	screen door, screen
+n04153330	screening
+n04153751	screw
+n04154152	screw, screw propeller
+n04154340	screw
+n04154565	screwdriver
+n04154753	screw eye
+n04154854	screw key
+n04154938	screw thread, thread
+n04155068	screwtop
+n04155177	screw wrench
+n04155457	scriber, scribe, scratch awl
+n04155625	scrim
+n04155735	scrimshaw
+n04155889	scriptorium
+n04156040	scrubber
+n04156140	scrub brush, scrubbing brush, scrubber
+n04156297	scrub plane
+n04156411	scuffer
+n04156591	scuffle, scuffle hoe, Dutch hoe
+n04156814	scull
+n04156946	scull
+n04157099	scullery
+n04157320	sculpture
+n04158002	scuttle, coal scuttle
+n04158138	scyphus
+n04158250	scythe
+n04158672	seabag
+n04158807	sea boat
+n04158956	sea chest
+n04160036	sealing wax, seal
+n04160261	sealskin
+n04160372	seam
+n04160586	seaplane, hydroplane
+n04160847	searchlight
+n04161010	searing iron
+n04161358	seat
+n04161981	seat
+n04162433	seat
+n04162706	seat belt, seatbelt
+n04163530	secateurs
+n04164002	secondary coil, secondary winding, secondary
+n04164199	second balcony, family circle, upper balcony, peanut gallery
+n04164406	second base
+n04164757	second hand
+n04164868	secretary, writing table, escritoire, secretaire
+n04165409	sectional
+n04165675	security blanket
+n04165945	security system, security measure, security
+n04166111	security system
+n04166281	sedan, saloon
+n04166436	sedan, sedan chair
+n04167346	seeder
+n04167489	seeker
+n04167661	seersucker
+n04168084	segmental arch
+n04168199	Segway, Segway Human Transporter, Segway HT
+n04168472	seidel
+n04168541	seine
+n04168840	seismograph
+n04169437	selector, selector switch
+n04169597	selenium cell
+n04170037	self-propelled vehicle
+n04170384	self-registering thermometer
+n04170515	self-starter
+n04170694	selsyn, synchro
+n04170933	selvage, selvedge
+n04171208	semaphore
+n04171459	semiautomatic firearm
+n04171629	semiautomatic pistol, semiautomatic
+n04171831	semiconductor device, semiconductor unit, semiconductor
+n04172107	semi-detached house
+n04172230	semigloss
+n04172342	semitrailer, semi
+n04172512	sennit
+n04172607	sensitometer
+n04172776	sentry box
+n04172904	separate
+n04173046	septic tank
+n04173172	sequence, episode
+n04173511	sequencer, sequenator
+n04173907	serape, sarape
+n04174026	serge
+n04174101	serger
+n04174234	serial port
+n04174500	serpent
+n04174705	serration
+n04175039	server
+n04175147	server, host
+n04175574	service club
+n04176068	serving cart
+n04176190	serving dish
+n04176295	servo, servomechanism, servosystem
+n04176528	set
+n04177041	set gun, spring gun
+n04177329	setscrew
+n04177545	setscrew
+n04177654	set square
+n04177755	settee
+n04177820	settle, settee
+n04177931	settlement house
+n04178190	seventy-eight, 78
+n04178329	Seven Wonders of the Ancient World, Seven Wonders of the World
+n04178668	sewage disposal plant, disposal plant
+n04179126	sewer, sewerage, cloaca
+n04179712	sewing basket
+n04179824	sewing kit
+n04179913	sewing machine
+n04180063	sewing needle
+n04180229	sewing room
+n04180888	sextant
+n04181083	sgraffito
+n04181228	shackle, bond, hamper, trammel
+n04181561	shackle
+n04181718	shade
+n04182152	shadow box
+n04182322	shaft
+n04183217	shag rug
+n04183329	shaker
+n04183957	shank
+n04184095	shank, stem
+n04184316	shantung
+n04184435	shaper, shaping machine
+n04184600	shaping tool
+n04184880	sharkskin
+n04185071	sharpener
+n04185529	Sharpie
+n04185804	shaver, electric shaver, electric razor
+n04185946	shaving brush
+n04186051	shaving cream, shaving soap
+n04186268	shaving foam
+n04186455	shawl
+n04186624	shawm
+n04186848	shears
+n04187061	sheath
+n04187233	sheathing, overlay, overlayer
+n04187547	shed
+n04187751	sheep bell
+n04187885	sheepshank
+n04187970	sheepskin coat, afghan
+n04188064	sheepwalk, sheeprun
+n04188179	sheet, bed sheet
+n04189092	sheet bend, becket bend, weaver's knot, weaver's hitch
+n04189282	sheeting
+n04189651	sheet pile, sheath pile, sheet piling
+n04189816	Sheetrock
+n04190052	shelf
+n04190376	shelf bracket
+n04190464	shell
+n04190747	shell, case, casing
+n04190997	shell, racing shell
+n04191150	shellac, shellac varnish
+n04191595	shelter
+n04191943	shelter
+n04192238	shelter
+n04192361	sheltered workshop
+n04192521	Sheraton
+n04192698	shield, buckler
+n04192858	shield
+n04193179	shielding
+n04193377	shift key, shift
+n04193742	shillelagh, shillalah
+n04193883	shim
+n04194009	shingle
+n04194127	shin guard, shinpad
+n04194289	ship
+n04196080	shipboard system
+n04196502	shipping, cargo ships, merchant marine, merchant vessels
+n04196803	shipping room
+n04196925	ship-towed long-range acoustic detection system
+n04197110	shipwreck
+n04197391	shirt
+n04197781	shirt button
+n04197878	shirtdress
+n04198015	shirtfront
+n04198233	shirting
+n04198355	shirtsleeve
+n04198453	shirttail
+n04198562	shirtwaist, shirtwaister
+n04198722	shiv
+n04198797	shock absorber, shock, cushion
+n04199027	shoe
+n04200000	shoe
+n04200258	shoebox
+n04200537	shoehorn
+n04200800	shoe shop, shoe-shop, shoe store
+n04200908	shoetree
+n04201064	shofar, shophar
+n04201297	shoji
+n04201733	shooting brake
+n04202142	shooting lodge, shooting box
+n04202282	shooting stick
+n04202417	shop, store
+n04203356	shop bell
+n04204081	shopping bag
+n04204238	shopping basket
+n04204347	shopping cart
+n04204755	short circuit, short
+n04205062	short iron
+n04205318	short pants, shorts, trunks
+n04205505	short sleeve
+n04205613	shortwave diathermy machine
+n04206070	shot
+n04206225	shot glass, jigger, pony
+n04206356	shotgun, scattergun
+n04206570	shotgun shell
+n04206790	shot tower
+n04207151	shoulder
+n04207343	shoulder bag
+n04207596	shouldered arch
+n04207763	shoulder holster
+n04207903	shoulder pad
+n04208065	shoulder patch
+n04208210	shovel
+n04208427	shovel
+n04208582	shovel hat
+n04208760	showboat
+n04208936	shower
+n04209133	shower cap
+n04209239	shower curtain
+n04209509	shower room
+n04209613	shower stall, shower bath
+n04209811	showroom, salesroom, saleroom
+n04210012	shrapnel
+n04210120	shredder
+n04210288	shrimper
+n04210390	shrine
+n04210591	shrink-wrap
+n04210858	shunt
+n04211001	shunt, electrical shunt, bypass
+n04211219	shunter
+n04211356	shutter
+n04211528	shutter
+n04211857	shuttle
+n04211970	shuttle
+n04212165	shuttle bus
+n04212282	shuttlecock, bird, birdie, shuttle
+n04212467	shuttle helicopter
+n04212810	Sibley tent
+n04213105	sickbay, sick berth
+n04213264	sickbed
+n04213353	sickle, reaping hook, reap hook
+n04213530	sickroom
+n04214046	sideboard
+n04214282	sidecar
+n04214413	side chapel
+n04214649	sidelight, running light
+n04215153	sidesaddle
+n04215402	sidewalk, pavement
+n04215588	sidewall
+n04215800	side-wheeler
+n04215910	sidewinder
+n04216634	sieve, screen
+n04216860	sifter
+n04216963	sights
+n04217387	sigmoidoscope, flexible sigmoidoscope
+n04217546	signal box, signal tower
+n04217718	signaling device
+n04217882	signboard, sign
+n04218564	silencer, muffler
+n04218921	silent butler
+n04219185	Silex
+n04219424	silk
+n04219580	silks
+n04220250	silo
+n04220805	silver plate
+n04221076	silverpoint
+n04221673	simple pendulum
+n04221823	simulator
+n04222210	single bed
+n04222307	single-breasted jacket
+n04222470	single-breasted suit
+n04222723	single prop, single-propeller plane
+n04222847	single-reed instrument, single-reed woodwind
+n04223066	single-rotor helicopter
+n04223170	singlestick, fencing stick, backsword
+n04223299	singlet, vest, undershirt
+n04224395	siren
+n04224543	sister ship
+n04224842	sitar
+n04225031	sitz bath, hip bath
+n04225222	six-pack, six pack, sixpack
+n04225729	skate
+n04225987	skateboard
+n04226322	skeg
+n04226464	skein
+n04226537	skeleton, skeletal frame, frame, underframe
+n04226826	skeleton key
+n04226962	skep
+n04227050	skep
+n04227144	sketch, study
+n04227519	sketcher
+n04227787	skew arch
+n04227900	skewer
+n04228054	ski
+n04228215	ski binding, binding
+n04228422	skibob
+n04228581	ski boot
+n04228693	ski cap, stocking cap, toboggan cap
+n04229007	skidder
+n04229107	skid lid
+n04229480	skiff
+n04229620	ski jump
+n04229737	ski lodge
+n04229816	ski mask
+n04229959	skimmer
+n04230387	ski parka, ski jacket
+n04230487	ski-plane
+n04230603	ski pole
+n04230707	ski rack
+n04230808	skirt
+n04231272	skirt
+n04231693	ski tow, ski lift, lift
+n04231905	Skivvies
+n04232153	skullcap
+n04232312	skybox
+n04232437	skyhook
+n04232800	skylight, fanlight
+n04233027	skysail
+n04233124	skyscraper
+n04233295	skywalk
+n04233715	slacks
+n04233832	slack suit
+n04234160	slasher
+n04234260	slash pocket
+n04234455	slat, spline
+n04234670	slate
+n04234763	slate pencil
+n04234887	slate roof
+n04235291	sled, sledge, sleigh
+n04235646	sleeper
+n04235771	sleeper
+n04235860	sleeping bag
+n04236001	sleeping car, sleeper, wagon-lit
+n04236377	sleeve, arm
+n04236702	sleeve
+n04236809	sleigh bed
+n04236935	sleigh bell, cascabel
+n04237174	slice bar
+n04237287	slicer
+n04237423	slicer
+n04238128	slide, playground slide, sliding board
+n04238321	slide fastener, zip, zipper, zip fastener
+n04238617	slide projector
+n04238763	slide rule, slipstick
+n04238953	slide valve
+n04239074	sliding door
+n04239218	sliding seat
+n04239333	sliding window
+n04239436	sling, scarf bandage, triangular bandage
+n04239639	sling
+n04239786	slingback, sling
+n04239900	slinger ring
+n04240434	slip clutch, slip friction clutch
+n04240752	slipcover
+n04240867	slip-joint pliers
+n04241042	slipknot
+n04241249	slip-on
+n04241394	slipper, carpet slipper
+n04241573	slip ring
+n04242084	slit lamp
+n04242315	slit trench
+n04242408	sloop
+n04242587	sloop of war
+n04242704	slop basin, slop bowl
+n04243003	slop pail, slop jar
+n04243142	slops
+n04243251	slopshop, slopseller's shop
+n04243546	slot, one-armed bandit
+n04243941	slot machine, coin machine
+n04244379	sluice, sluiceway, penstock
+n04244847	smack
+n04244997	small boat
+n04245218	small computer system interface, SCSI
+n04245412	small ship
+n04245508	small stores
+n04245847	smart bomb
+n04246060	smelling bottle
+n04246271	smocking
+n04246459	smoke bomb, smoke grenade
+n04246731	smokehouse, meat house
+n04246855	smoker, smoking car, smoking carriage, smoking compartment
+n04247011	smoke screen, smokescreen
+n04247440	smoking room
+n04247544	smoothbore
+n04247630	smooth plane, smoothing plane
+n04247736	snack bar, snack counter, buffet
+n04247876	snaffle, snaffle bit
+n04248209	snap, snap fastener, press stud
+n04248396	snap brim
+n04248507	snap-brim hat
+n04248851	snare, gin, noose
+n04249415	snare drum, snare, side drum
+n04249582	snatch block
+n04249882	snifter, brandy snifter, brandy glass
+n04250224	sniper rifle, precision rifle
+n04250473	snips, tinsnips
+n04250599	Sno-cat
+n04250692	snood
+n04250850	snorkel, schnorkel, schnorchel, snorkel breather, breather
+n04251144	snorkel
+n04251701	snowbank, snow bank
+n04251791	snowboard
+n04252077	snowmobile
+n04252225	snowplow, snowplough
+n04252331	snowshoe
+n04252560	snowsuit
+n04252653	snow thrower, snow blower
+n04253057	snuffbox
+n04253168	snuffer
+n04253304	snuffers
+n04253931	soapbox
+n04254009	soap dish
+n04254120	soap dispenser
+n04254450	soap pad
+n04254680	soccer ball
+n04254777	sock
+n04255163	socket
+n04255346	socket wrench
+n04255499	socle
+n04255586	soda can
+n04255670	soda fountain
+n04255768	soda fountain
+n04255899	sod house, soddy, adobe house
+n04256318	sodium-vapor lamp, sodium-vapour lamp
+n04256520	sofa, couch, lounge
+n04256758	soffit
+n04256891	softball, playground ball
+n04257223	soft pedal
+n04257684	soil pipe
+n04257790	solar array, solar battery, solar panel
+n04257986	solar cell, photovoltaic cell
+n04258138	solar dish, solar collector, solar furnace
+n04258333	solar heater
+n04258438	solar house
+n04258618	solar telescope
+n04258732	solar thermal system
+n04258859	soldering iron
+n04259202	solenoid
+n04259468	solleret, sabaton
+n04259630	sombrero
+n04260192	sonic depth finder, fathometer
+n04260364	sonogram, echogram
+n04260589	sonograph
+n04261116	sorter
+n04261281	souk
+n04261369	sound bow
+n04261506	soundbox, body
+n04261638	sound camera
+n04261767	sounder
+n04261868	sound film
+n04262161	sounding board, soundboard
+n04262530	sounding rocket
+n04262678	sound recording, audio recording, audio
+n04262869	sound spectrograph
+n04263257	soup bowl
+n04263336	soup ladle
+n04263502	soupspoon, soup spoon
+n04263760	source of illumination
+n04263950	sourdine
+n04264134	soutache
+n04264233	soutane
+n04264361	sou'wester
+n04264485	soybean future
+n04264628	space bar
+n04264765	space capsule, capsule
+n04264914	spacecraft, ballistic capsule, space vehicle
+n04265275	space heater
+n04265428	space helmet
+n04265904	space rocket
+n04266014	space shuttle
+n04266162	space station, space platform, space laboratory
+n04266375	spacesuit
+n04266486	spade
+n04266849	spade bit
+n04266968	spaghetti junction
+n04267091	Spandau
+n04267165	spandex
+n04267246	spandrel, spandril
+n04267435	spanker
+n04267577	spar
+n04267985	sparge pipe
+n04268142	spark arrester, sparker
+n04268275	spark arrester
+n04268418	spark chamber, spark counter
+n04268565	spark coil
+n04268799	spark gap
+n04269086	spark lever
+n04269270	spark plug, sparking plug, plug
+n04269502	sparkplug wrench
+n04269668	spark transmitter
+n04269822	spat, gaiter
+n04269944	spatula
+n04270147	spatula
+n04270371	speakerphone
+n04270576	speaking trumpet
+n04270891	spear, lance, shaft
+n04271148	spear, gig, fizgig, fishgig, lance
+n04271531	specialty store
+n04271793	specimen bottle
+n04271891	spectacle
+n04272054	spectacles, specs, eyeglasses, glasses
+n04272389	spectator pump, spectator
+n04272782	spectrograph
+n04272928	spectrophotometer
+n04273064	spectroscope, prism spectroscope
+n04273285	speculum
+n04273569	speedboat
+n04273659	speed bump
+n04273796	speedometer, speed indicator
+n04273972	speed skate, racing skate
+n04274686	spherometer
+n04274985	sphygmomanometer
+n04275093	spicemill
+n04275175	spice rack
+n04275283	spider
+n04275548	spider web, spider's web
+n04275661	spike
+n04275904	spike
+n04277352	spindle
+n04277493	spindle, mandrel, mandril, arbor
+n04277669	spindle
+n04277826	spin dryer, spin drier
+n04278247	spinet
+n04278353	spinet
+n04278447	spinnaker
+n04278605	spinner
+n04278932	spinning frame
+n04279063	spinning jenny
+n04279172	spinning machine
+n04279353	spinning rod
+n04279462	spinning wheel
+n04279858	spiral bandage
+n04279987	spiral ratchet screwdriver, ratchet screwdriver
+n04280259	spiral spring
+n04280373	spirit lamp
+n04280487	spirit stove
+n04280845	spirometer
+n04280970	spit
+n04281260	spittoon, cuspidor
+n04281375	splashboard, splasher, dashboard
+n04281571	splasher
+n04281998	splice, splicing
+n04282231	splicer
+n04282494	splint
+n04282872	split rail, fence rail
+n04282992	Spode
+n04283096	spoiler
+n04283255	spoiler
+n04283378	spoke, wheel spoke, radius
+n04283585	spokeshave
+n04283784	sponge cloth
+n04283905	sponge mop
+n04284002	spoon
+n04284341	spoon
+n04284438	Spork
+n04284572	sporran
+n04284869	sport kite, stunt kite
+n04285008	sports car, sport car
+n04285146	sports equipment
+n04285622	sports implement
+n04285803	sportswear, athletic wear, activewear
+n04285965	sport utility, sport utility vehicle, S.U.V., SUV
+n04286128	spot
+n04286575	spotlight, spot
+n04286960	spot weld, spot-weld
+n04287351	spouter
+n04287451	sprag
+n04287747	spray gun
+n04287898	spray paint
+n04287986	spreader
+n04288165	sprig
+n04288272	spring
+n04288533	spring balance, spring scale
+n04288673	springboard
+n04289027	sprinkler
+n04289195	sprinkler system
+n04289449	sprit
+n04289576	spritsail
+n04289690	sprocket, sprocket wheel
+n04289827	sprocket
+n04290079	spun yarn
+n04290259	spur, gad
+n04290507	spur gear, spur wheel
+n04290615	sputnik
+n04290762	spy satellite
+n04291069	squad room
+n04291242	square
+n04291759	square knot
+n04291992	square-rigger
+n04292080	square sail
+n04292221	squash ball
+n04292414	squash racket, squash racquet, bat
+n04292572	squawk box, squawker, intercom speaker
+n04292921	squeegee
+n04293119	squeezer
+n04293258	squelch circuit, squelch, squelcher
+n04293744	squinch
+n04294212	stabilizer, stabiliser
+n04294426	stabilizer
+n04294614	stabilizer bar, anti-sway bar
+n04294879	stable, stalls, horse barn
+n04295081	stable gear, saddlery, tack
+n04295353	stabling
+n04295571	stacks
+n04295777	staddle
+n04295881	stadium, bowl, arena, sports stadium
+n04296562	stage
+n04297098	stagecoach, stage
+n04297750	stained-glass window
+n04297847	stair-carpet
+n04298053	stair-rod
+n04298661	stairwell
+n04298765	stake
+n04299215	stall, stand, sales booth
+n04299370	stall
+n04299963	stamp
+n04300358	stamp mill, stamping mill
+n04300509	stamping machine, stamper
+n04300643	stanchion
+n04301000	stand
+n04301242	standard
+n04301474	standard cell
+n04301760	standard transmission, stick shift
+n04302200	standing press
+n04302863	stanhope
+n04302988	Stanley Steamer
+n04303095	staple
+n04303258	staple
+n04303357	staple gun, staplegun, tacker
+n04303497	stapler, stapling machine
+n04304215	starship, spaceship
+n04304375	starter, starter motor, starting motor
+n04304680	starting gate, starting stall
+n04305016	Stassano furnace, electric-arc furnace
+n04305210	Statehouse
+n04305323	stately home
+n04305471	state prison
+n04305572	stateroom
+n04305947	static tube
+n04306080	station
+n04306592	stator, stator coil
+n04306847	statue
+n04307419	stay
+n04307767	staysail
+n04307878	steakhouse, chophouse
+n04307986	steak knife
+n04308084	stealth aircraft
+n04308273	stealth bomber
+n04308397	stealth fighter
+n04308583	steam bath, steam room, vapor bath, vapour bath
+n04308807	steamboat
+n04308915	steam chest
+n04309049	steam engine
+n04309348	steamer, steamship
+n04309548	steamer
+n04309833	steam iron
+n04310018	steam locomotive
+n04310157	steamroller, road roller
+n04310507	steam shovel
+n04310604	steam turbine
+n04310721	steam whistle
+n04310904	steel
+n04311004	steel arch bridge
+n04311174	steel drum
+n04311595	steel mill, steelworks, steel plant, steel factory
+n04312020	steel-wool pad
+n04312154	steelyard, lever scale, beam scale
+n04312432	steeple, spire
+n04312654	steerage
+n04312756	steering gear
+n04312916	steering linkage
+n04313220	steering system, steering mechanism
+n04313503	steering wheel, wheel
+n04313628	stele, stela
+n04314107	stem-winder
+n04314216	stencil
+n04314522	Sten gun
+n04314632	stenograph
+n04314914	step, stair
+n04315342	step-down transformer
+n04315713	step stool
+n04315828	step-up transformer
+n04315948	stereo, stereophony, stereo system, stereophonic system
+n04316498	stereoscope
+n04316815	stern chaser
+n04316924	sternpost
+n04317063	sternwheeler
+n04317175	stethoscope
+n04317325	stewing pan, stewpan
+n04317420	stick
+n04317833	stick
+n04317976	stick, control stick, joystick
+n04318131	stick
+n04318787	stile
+n04318892	stiletto
+n04318982	still
+n04319545	stillroom, still room
+n04319774	Stillson wrench
+n04319937	stilt
+n04320405	Stinger
+n04320598	stink bomb, stench bomb
+n04320871	stirrer
+n04320973	stirrup, stirrup iron
+n04321121	stirrup pump
+n04321453	stob
+n04322026	stock, gunstock
+n04322531	stockade
+n04322692	stockcar
+n04322801	stock car
+n04323519	stockinet, stockinette
+n04323819	stocking
+n04324120	stock-in-trade
+n04324297	stockpot
+n04324387	stockroom, stock room
+n04324515	stocks
+n04325041	stock saddle, Western saddle
+n04325208	stockyard
+n04325704	stole
+n04325804	stomacher
+n04325968	stomach pump
+n04326547	stone wall
+n04326676	stoneware
+n04326799	stonework
+n04326896	stool
+n04327204	stoop, stoep
+n04327544	stop bath, short-stop, short-stop bath
+n04327682	stopcock, cock, turncock
+n04328054	stopper knot
+n04328186	stopwatch, stop watch
+n04328329	storage battery, accumulator
+n04328580	storage cell, secondary cell
+n04328703	storage ring
+n04328946	storage space
+n04329477	storeroom, storage room, stowage
+n04329681	storm cellar, cyclone cellar, tornado cellar
+n04329834	storm door
+n04329958	storm window, storm sash
+n04330109	stoup, stoop
+n04330189	stoup
+n04330267	stove
+n04330340	stove, kitchen stove, range, kitchen range, cooking stove
+n04330669	stove bolt
+n04330746	stovepipe
+n04330896	stovepipe iron
+n04330998	Stradavarius, Strad
+n04331277	straight chair, side chair
+n04331443	straightedge
+n04331639	straightener
+n04331765	straight flute, straight-fluted drill
+n04331892	straight pin
+n04332074	straight razor
+n04332243	strainer
+n04332580	straitjacket, straightjacket
+n04332987	strap
+n04333129	strap
+n04333869	strap hinge, joint hinge
+n04334105	strapless
+n04334365	streamer fly
+n04334504	streamliner
+n04334599	street
+n04335209	street
+n04335435	streetcar, tram, tramcar, trolley, trolley car
+n04335693	street clothes
+n04335886	streetlight, street lamp
+n04336792	stretcher
+n04337157	stretcher
+n04337287	stretch pants
+n04337503	strickle
+n04337650	strickle
+n04338517	stringed instrument
+n04338963	stringer
+n04339062	stringer
+n04339191	string tie
+n04339638	strip
+n04339879	strip lighting
+n04340019	strip mall
+n04340521	stroboscope, strobe, strobe light
+n04340750	strongbox, deedbox
+n04340935	stronghold, fastness
+n04341133	strongroom
+n04341288	strop
+n04341414	structural member
+n04341686	structure, construction
+n04343511	student center
+n04343630	student lamp
+n04343740	student union
+n04344003	stud finder
+n04344734	studio apartment, studio
+n04344873	studio couch, day bed
+n04345028	study
+n04345201	study hall
+n04345787	stuffing nut, packing nut
+n04346003	stump
+n04346157	stun gun, stun baton
+n04346328	stupa, tope
+n04346428	sty, pigsty, pigpen
+n04346511	stylus, style
+n04346679	stylus
+n04346855	sub-assembly
+n04347119	subcompact, subcompact car
+n04347519	submachine gun
+n04347754	submarine, pigboat, sub, U-boat
+n04348070	submarine torpedo
+n04348184	submersible, submersible warship
+n04348359	submersible
+n04348988	subtracter
+n04349189	subway token
+n04349306	subway train
+n04349401	subwoofer
+n04349913	suction cup
+n04350104	suction pump
+n04350235	sudatorium, sudatory
+n04350458	suede cloth, suede
+n04350581	sugar bowl
+n04350688	sugar refinery
+n04350769	sugar spoon, sugar shell
+n04350905	suit, suit of clothes
+n04351550	suite, rooms
+n04351699	suiting
+n04353573	sulky
+n04354026	summer house
+n04354182	sumo ring
+n04354387	sump
+n04354487	sump pump
+n04354589	sunbonnet
+n04355115	Sunday best, Sunday clothes
+n04355267	sun deck
+n04355338	sundial
+n04355511	sundress
+n04355684	sundries
+n04355821	sun gear
+n04355933	sunglass
+n04356056	sunglasses, dark glasses, shades
+n04356595	sunhat, sun hat
+n04356772	sunlamp, sun lamp, sunray lamp, sun-ray lamp
+n04356925	sun parlor, sun parlour, sun porch, sunporch, sunroom, sun lounge, solarium
+n04357121	sunroof, sunshine-roof
+n04357314	sunscreen, sunblock, sun blocker
+n04357531	sunsuit
+n04357930	supercharger
+n04358117	supercomputer
+n04358256	superconducting supercollider
+n04358491	superhighway, information superhighway
+n04358707	supermarket
+n04358874	superstructure
+n04359034	supertanker
+n04359124	supper club
+n04359217	supplejack
+n04359335	supply chamber
+n04359500	supply closet
+n04359589	support
+n04360501	support
+n04360798	support column
+n04360914	support hose, support stocking
+n04361095	supporting structure
+n04361260	supporting tower
+n04361937	surcoat
+n04362624	surface gauge, surface gage, scribing block
+n04362821	surface lift
+n04362972	surface search radar
+n04363082	surface ship
+n04363210	surface-to-air missile, SAM
+n04363412	surface-to-air missile system
+n04363671	surfboat
+n04363777	surcoat
+n04363874	surgeon's knot
+n04363991	surgery
+n04364160	surge suppressor, surge protector, spike suppressor, spike arrester, lightning arrester
+n04364397	surgical dressing
+n04364545	surgical instrument
+n04364827	surgical knife
+n04364994	surplice
+n04365112	surrey
+n04365229	surtout
+n04365328	surveillance system
+n04365484	surveying instrument, surveyor's instrument
+n04365751	surveyor's level
+n04366033	sushi bar
+n04366116	suspension, suspension system
+n04366367	suspension bridge
+n04366832	suspensory, suspensory bandage
+n04367011	sustaining pedal, loud pedal
+n04367371	suture, surgical seam
+n04367480	swab, swob, mop
+n04367746	swab
+n04367950	swaddling clothes, swaddling bands
+n04368109	swag
+n04368235	swage block
+n04368365	swagger stick
+n04368496	swallow-tailed coat, swallowtail, morning coat
+n04368695	swamp buggy, marsh buggy
+n04368840	swan's down
+n04369025	swathe, wrapping
+n04369282	swatter, flyswatter, flyswat
+n04369485	sweat bag
+n04369618	sweatband
+n04370048	sweater, jumper
+n04370288	sweat pants, sweatpants
+n04370456	sweatshirt
+n04370600	sweatshop
+n04370774	sweat suit, sweatsuit, sweats, workout suit
+n04370955	sweep, sweep oar
+n04371050	sweep hand, sweep-second
+n04371430	swimming trunks, bathing trunks
+n04371563	swimsuit, swimwear, bathing suit, swimming costume, bathing costume
+n04371774	swing
+n04371979	swing door, swinging door
+n04372370	switch, electric switch, electrical switch
+n04373089	switchblade, switchblade knife, flick-knife, flick knife
+n04373428	switch engine, donkey engine
+n04373563	swivel
+n04373704	swivel chair
+n04373795	swizzle stick
+n04373894	sword, blade, brand, steel
+n04374315	sword cane, sword stick
+n04374521	S wrench
+n04374735	synagogue, temple, tabernacle
+n04374907	synchrocyclotron
+n04375080	synchroflash
+n04375241	synchromesh
+n04375405	synchronous converter, rotary, rotary converter
+n04375615	synchronous motor
+n04375775	synchrotron
+n04375926	synchroscope, synchronoscope, synchronizer, synchroniser
+n04376400	synthesizer, synthesiser
+n04376876	syringe
+n04377057	system
+n04378489	tabard
+n04378651	Tabernacle
+n04378956	tabi, tabis
+n04379096	tab key, tab
+n04379243	table
+n04379964	table
+n04380255	tablefork
+n04380346	table knife
+n04380533	table lamp
+n04380916	table saw
+n04381073	tablespoon
+n04381450	tablet-armed chair
+n04381587	table-tennis table, ping-pong table, pingpong table
+n04381724	table-tennis racquet, table-tennis bat, pingpong paddle
+n04381860	tabletop
+n04381994	tableware
+n04382334	tabor, tabour
+n04382438	taboret, tabouret
+n04382537	tachistoscope, t-scope
+n04382695	tachograph
+n04382880	tachometer, tach
+n04383015	tachymeter, tacheometer
+n04383130	tack
+n04383301	tack hammer
+n04383839	taffeta
+n04383923	taffrail
+n04384593	tailgate, tailboard
+n04384910	taillight, tail lamp, rear light, rear lamp
+n04385079	tailor-made
+n04385157	tailor's chalk
+n04385536	tailpipe
+n04385799	tail rotor, anti-torque rotor
+n04386051	tailstock
+n04386456	take-up
+n04386664	talaria
+n04386792	talcum, talcum powder
+n04387095	tam, tam-o'-shanter, tammy
+n04387201	tambour
+n04387261	tambour, embroidery frame, embroidery hoop
+n04387400	tambourine
+n04387531	tammy
+n04387706	tamp, tamper, tamping bar
+n04387932	Tampax
+n04388040	tampion, tompion
+n04388162	tampon
+n04388473	tandoor
+n04388574	tangram
+n04388743	tank, storage tank
+n04389033	tank, army tank, armored combat vehicle, armoured combat vehicle
+n04389430	tankard
+n04389521	tank car, tank
+n04389718	tank destroyer
+n04389854	tank engine, tank locomotive
+n04389999	tanker plane
+n04390483	tank shell
+n04390577	tank top
+n04390873	tannoy
+n04390977	tap, spigot
+n04391445	tapa, tappa
+n04391838	tape, tape recording, taping
+n04392113	tape, tapeline, tape measure
+n04392526	tape deck
+n04392764	tape drive, tape transport, transport
+n04392985	tape player
+n04393095	tape recorder, tape machine
+n04393301	taper file
+n04393549	tapestry, tapis
+n04393808	tappet
+n04393913	tap wrench
+n04394031	tare
+n04394261	target, butt
+n04394421	target acquisition system
+n04394630	tarmacadam, tarmac, macadam
+n04395024	tarpaulin, tarp
+n04395106	tartan, plaid
+n04395332	tasset, tasse
+n04395651	tattoo
+n04395875	tavern, tap house
+n04396226	tawse
+n04396335	taximeter
+n04396650	T-bar lift, T-bar, Alpine lift
+n04396808	tea bag
+n04396902	tea ball
+n04397027	tea cart, teacart, tea trolley, tea wagon
+n04397168	tea chest
+n04397261	teaching aid
+n04397452	teacup
+n04397645	tea gown
+n04397768	teakettle
+n04397860	tea maker
+n04398044	teapot
+n04398497	teashop, teahouse, tearoom, tea parlor, tea parlour
+n04398688	teaspoon
+n04398834	tea-strainer
+n04398951	tea table
+n04399046	tea tray
+n04399158	tea urn
+n04399382	teddy, teddy bear
+n04399537	tee, golf tee
+n04399846	tee hinge, T hinge
+n04400109	telecom hotel, telco building
+n04400289	telecommunication system, telecom system, telecommunication equipment, telecom equipment
+n04400499	telegraph, telegraphy
+n04400737	telegraph key
+n04400899	telemeter
+n04401088	telephone, phone, telephone set
+n04401578	telephone bell
+n04401680	telephone booth, phone booth, call box, telephone box, telephone kiosk
+n04401828	telephone cord, phone cord
+n04401949	telephone jack, phone jack
+n04402057	telephone line, phone line, telephone circuit, subscriber line, line
+n04402342	telephone plug, phone plug
+n04402449	telephone pole, telegraph pole, telegraph post
+n04402580	telephone receiver, receiver
+n04402746	telephone system, phone system
+n04402984	telephone wire, telephone line, telegraph wire, telegraph line
+n04403413	telephoto lens, zoom lens
+n04403524	Teleprompter
+n04403638	telescope, scope
+n04403925	telescopic sight, telescope sight
+n04404072	telethermometer
+n04404200	teletypewriter, teleprinter, teletype machine, telex, telex machine
+n04404412	television, television system
+n04404817	television antenna, tv-antenna
+n04404997	television camera, tv camera, camera
+n04405540	television equipment, video equipment
+n04405762	television monitor, tv monitor
+n04405907	television receiver, television, television set, tv, tv set, idiot box, boob tube, telly, goggle box
+n04406239	television room, tv room
+n04406552	television transmitter
+n04406687	telpher, telfer
+n04406817	telpherage, telferage
+n04407257	tempera, poster paint, poster color, poster colour
+n04407435	temple
+n04407686	temple
+n04408871	temporary hookup, patch
+n04409011	tender, supply ship
+n04409128	tender, ship's boat, pinnace, cutter
+n04409279	tender
+n04409384	tenement, tenement house
+n04409515	tennis ball
+n04409625	tennis camp
+n04409806	tennis racket, tennis racquet
+n04409911	tenon
+n04410086	tenor drum, tom-tom
+n04410365	tenoroon
+n04410485	tenpenny nail
+n04410565	tenpin
+n04410663	tensimeter
+n04410760	tensiometer
+n04410886	tensiometer
+n04411019	tensiometer
+n04411264	tent, collapsible shelter
+n04411835	tenter
+n04411966	tenterhook
+n04412097	tent-fly, rainfly, fly sheet, fly, tent flap
+n04412300	tent peg
+n04412416	tepee, tipi, teepee
+n04413151	terminal, pole
+n04413419	terminal
+n04413969	terraced house
+n04414101	terra cotta
+n04414199	terrarium
+n04414319	terra sigillata, Samian ware
+n04414476	terry, terry cloth, terrycloth
+n04414675	Tesla coil
+n04414909	tessera
+n04415257	test equipment
+n04415663	test rocket, research rocket, test instrument vehicle
+n04415815	test room, testing room
+n04416005	testudo
+n04416901	tetraskelion, tetraskele
+n04417086	tetrode
+n04417180	textile machine
+n04417361	textile mill
+n04417672	thatch, thatched roof
+n04417809	theater, theatre, house
+n04418357	theater curtain, theatre curtain
+n04418644	theater light
+n04419073	theodolite, transit
+n04419642	theremin
+n04419868	thermal printer
+n04420024	thermal reactor
+n04420720	thermocouple, thermocouple junction
+n04421083	thermoelectric thermometer, thermel, electric thermometer
+n04421258	thermograph, thermometrograph
+n04421417	thermograph
+n04421582	thermohydrometer, thermogravimeter
+n04421740	thermojunction
+n04421872	thermometer
+n04422409	thermonuclear reactor, fusion reactor
+n04422566	thermopile
+n04422727	thermos, thermos bottle, thermos flask
+n04422875	thermostat, thermoregulator
+n04423552	thigh pad
+n04423687	thill
+n04423845	thimble
+n04424692	thinning shears
+n04425804	third base, third
+n04425977	third gear, third
+n04426184	third rail
+n04426316	thong
+n04426427	thong
+n04427216	three-centered arch, basket-handle arch
+n04427473	three-decker
+n04427559	three-dimensional radar, 3d radar
+n04427715	three-piece suit
+n04427857	three-quarter binding
+n04428008	three-way switch, three-point switch
+n04428191	thresher, thrasher, threshing machine
+n04428382	threshing floor
+n04428634	thriftshop, second-hand store
+n04429038	throat protector
+n04429376	throne
+n04430475	thrust bearing
+n04430605	thruster
+n04430896	thumb
+n04431025	thumbhole
+n04431436	thumbscrew
+n04431648	thumbstall
+n04431745	thumbtack, drawing pin, pushpin
+n04431925	thunderer
+n04432043	thwart, cross thwart
+n04432203	tiara
+n04432662	ticking
+n04432785	tickler coil
+n04433377	tie, tie beam
+n04433585	tie, railroad tie, crosstie, sleeper
+n04434207	tie rack
+n04434531	tie rod
+n04434932	tights, leotards
+n04435180	tile
+n04435552	tile cutter
+n04435653	tile roof
+n04435759	tiller
+n04435870	tilter
+n04436012	tilt-top table, tip-top table, tip table
+n04436185	timber
+n04436329	timber
+n04436401	timber hitch
+n04436542	timbrel
+n04436832	time bomb, infernal machine
+n04436992	time capsule
+n04437276	time clock
+n04437380	time-delay measuring instrument, time-delay measuring system
+n04437670	time-fuse
+n04437953	timepiece, timekeeper, horologe
+n04438304	timer
+n04438507	timer
+n04438643	time-switch
+n04438897	tin
+n04439505	tinderbox
+n04439585	tine
+n04439712	tinfoil, tin foil
+n04440597	tippet
+n04440963	tire chain, snow chain
+n04441093	tire iron, tire tool
+n04441528	titfer
+n04441662	tithe barn
+n04441790	titrator
+n04442312	toaster
+n04442441	toaster oven
+n04442582	toasting fork
+n04442741	toastrack
+n04443164	tobacco pouch
+n04443257	tobacco shop, tobacconist shop, tobacconist
+n04443433	toboggan
+n04443766	toby, toby jug, toby filneural_compressor jug
+n04444121	tocsin, warning bell
+n04444218	toe
+n04444749	toecap
+n04444953	toehold
+n04445040	toga
+n04445154	toga virilis
+n04445327	toggle
+n04445610	toggle bolt
+n04445782	toggle joint
+n04445952	toggle switch, toggle, on-off switch, on/off switch
+n04446162	togs, threads, duds
+n04446276	toilet, lavatory, lav, can, john, privy, bathroom
+n04446844	toilet bag, sponge bag
+n04447028	toilet bowl
+n04447156	toilet kit, travel kit
+n04447276	toilet powder, bath powder, dusting powder
+n04447443	toiletry, toilet articles
+n04447861	toilet seat
+n04448070	toilet water, eau de toilette
+n04448185	tokamak
+n04448361	token
+n04449290	tollbooth, tolbooth, tollhouse
+n04449449	toll bridge
+n04449550	tollgate, tollbar
+n04449700	toll line
+n04449966	tomahawk, hatchet
+n04450133	Tommy gun, Thompson submachine gun
+n04450243	tomograph
+n04450465	tone arm, pickup, pickup arm
+n04450640	toner
+n04450749	tongs, pair of tongs
+n04450994	tongue
+n04451139	tongue and groove joint
+n04451318	tongue depressor
+n04451636	tonometer
+n04451818	tool
+n04452528	tool bag
+n04452615	toolbox, tool chest, tool cabinet, tool case
+n04452757	toolshed, toolhouse
+n04452848	tooth
+n04453037	tooth
+n04453156	toothbrush
+n04453390	toothpick
+n04453666	top
+n04453910	top, cover
+n04454654	topgallant, topgallant mast
+n04454792	topgallant, topgallant sail
+n04454908	topiary
+n04455048	topknot
+n04455250	topmast
+n04455579	topper
+n04455652	topsail
+n04456011	toque
+n04456115	torch
+n04456472	torpedo
+n04456734	torpedo
+n04457157	torpedo
+n04457326	torpedo boat
+n04457474	torpedo-boat destroyer
+n04457638	torpedo tube
+n04457767	torque converter
+n04457910	torque wrench
+n04458201	torture chamber
+n04458633	totem pole
+n04458843	touch screen, touchscreen
+n04459018	toupee, toupe
+n04459122	touring car, phaeton, tourer
+n04459243	tourist class, third class
+n04459362	towel
+n04459610	toweling, towelling
+n04459773	towel rack, towel horse
+n04459909	towel rail, towel bar
+n04460130	tower
+n04461437	town hall
+n04461570	towpath, towing path
+n04461696	tow truck, tow car, wrecker
+n04461879	toy
+n04462011	toy box, toy chest
+n04462240	toyshop
+n04462576	trace detector
+n04463679	track, rail, rails, runway
+n04464125	track
+n04464615	trackball
+n04464852	tracked vehicle
+n04465050	tract house
+n04465203	tract housing
+n04465358	traction engine
+n04465501	tractor
+n04465666	tractor
+n04466871	trail bike, dirt bike, scrambler
+n04467099	trailer, house trailer
+n04467307	trailer
+n04467506	trailer camp, trailer park
+n04467665	trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi
+n04467899	trailing edge
+n04468005	train, railroad train
+n04469003	tramline, tramway, streetcar track
+n04469251	trammel
+n04469514	trampoline
+n04469684	tramp steamer, tramp
+n04469813	tramway, tram, aerial tramway, cable tramway, ropeway
+n04470741	transdermal patch, skin patch
+n04471148	transept
+n04471315	transformer
+n04471632	transistor, junction transistor, electronic transistor
+n04471912	transit instrument
+n04472243	transmission, transmission system
+n04472563	transmission shaft
+n04472726	transmitter, sender
+n04472961	transom, traverse
+n04473108	transom, transom window, fanlight
+n04473275	transponder
+n04473884	transporter
+n04474035	transporter, car transporter
+n04474187	transport ship
+n04474466	trap
+n04475309	trap door
+n04475411	trapeze
+n04475496	trave, traverse, crossbeam, crosspiece
+n04475631	travel iron
+n04475749	trawl, dragnet, trawl net
+n04475900	trawl, trawl line, spiller, setline, trotline
+n04476116	trawler, dragger
+n04476259	tray
+n04476526	tray cloth
+n04476831	tread
+n04476972	tread
+n04477219	treadmill, treadwheel, tread-wheel
+n04477387	treadmill
+n04477548	treasure chest
+n04477725	treasure ship
+n04478066	treenail, trenail, trunnel
+n04478383	trefoil arch
+n04478512	trellis, treillage
+n04478657	trench
+n04479046	trench coat
+n04479287	trench knife
+n04479405	trepan
+n04479526	trepan, trephine
+n04479694	trestle
+n04479823	trestle
+n04479939	trestle bridge
+n04480033	trestle table
+n04480141	trestlework
+n04480303	trews
+n04480527	trial balloon
+n04480853	triangle
+n04480995	triangle
+n04481524	triclinium
+n04481642	triclinium
+n04482177	tricorn, tricorne
+n04482297	tricot
+n04482393	tricycle, trike, velocipede
+n04482975	trident
+n04483073	trigger
+n04483307	trimaran
+n04483925	trimmer
+n04484024	trimmer arch
+n04484432	triode
+n04485082	tripod
+n04485423	triptych
+n04485586	trip wire
+n04485750	trireme
+n04485884	triskelion, triskele
+n04486054	triumphal arch
+n04486213	trivet
+n04486322	trivet
+n04486616	troika
+n04486934	troll
+n04487081	trolleybus, trolley coach, trackless trolley
+n04487394	trombone
+n04487724	troop carrier, troop transport
+n04487894	troopship
+n04488202	trophy case
+n04488427	trough
+n04488530	trouser
+n04488742	trouser cuff
+n04488857	trouser press, pants presser
+n04489008	trouser, pant
+n04489695	trousseau
+n04489817	trowel
+n04490091	truck, motortruck
+n04491312	trumpet arch
+n04491388	truncheon, nightstick, baton, billy, billystick, billy club
+n04491638	trundle bed, trundle, truckle bed, truckle
+n04491769	trunk
+n04491934	trunk hose
+n04492060	trunk lid
+n04492157	trunk line
+n04492375	truss
+n04492749	truss bridge
+n04493109	try square
+n04493259	T-square
+n04493381	tub, vat
+n04494204	tube, vacuum tube, thermionic vacuum tube, thermionic tube, electron tube, thermionic valve
+n04495051	tuck box
+n04495183	tucker
+n04495310	tucker-bag
+n04495450	tuck shop
+n04495555	Tudor arch, four-centered arch
+n04495698	tudung
+n04495843	tugboat, tug, towboat, tower
+n04496614	tulle
+n04496726	tumble-dryer, tumble drier
+n04496872	tumbler
+n04497249	tumbrel, tumbril
+n04497442	tun
+n04497570	tunic
+n04497801	tuning fork
+n04498275	tupik, tupek, sealskin tent
+n04498389	turban
+n04498523	turbine
+n04498873	turbogenerator
+n04499062	tureen
+n04499300	Turkish bath
+n04499446	Turkish towel, terry towel
+n04499554	Turk's head
+n04499810	turnbuckle
+n04500060	turner, food turner
+n04500390	turnery
+n04501127	turnpike
+n04501281	turnspit
+n04501370	turnstile
+n04501550	turntable
+n04501837	turntable, lazy Susan
+n04501947	turret
+n04502059	turret clock
+n04502197	turtleneck, turtle, polo-neck
+n04502502	tweed
+n04502670	tweeter
+n04502851	twenty-two, .22
+n04502989	twenty-two pistol
+n04503073	twenty-two rifle
+n04503155	twill
+n04503269	twill, twill weave
+n04503413	twin bed
+n04503499	twinjet
+n04503593	twist bit, twist drill
+n04503705	two-by-four
+n04504038	two-man tent
+n04504141	two-piece, two-piece suit, lounge suit
+n04504770	typesetting machine
+n04505036	typewriter
+n04505345	typewriter carriage
+n04505470	typewriter keyboard
+n04505888	tyrolean, tirolean
+n04506289	uke, ukulele
+n04506402	ulster
+n04506506	ultracentrifuge
+n04506688	ultramicroscope, dark-field microscope
+n04506895	Ultrasuede
+n04506994	ultraviolet lamp, ultraviolet source
+n04507155	umbrella
+n04507326	umbrella tent
+n04507453	undercarriage
+n04507689	undercoat, underseal
+n04508163	undergarment, unmentionable
+n04508489	underpants
+n04508949	underwear, underclothes, underclothing
+n04509171	undies
+n04509260	uneven parallel bars, uneven bars
+n04509417	unicycle, monocycle
+n04509592	uniform
+n04510706	universal joint, universal
+n04511002	university
+n04513827	upholstery
+n04513998	upholstery material
+n04514095	upholstery needle
+n04514241	uplift
+n04514648	upper berth, upper
+n04515003	upright, upright piano
+n04515444	upset, swage
+n04515729	upstairs
+n04515890	urceole
+n04516116	urn
+n04516214	urn
+n04516354	used-car, secondhand car
+n04516672	utensil
+n04517211	Uzi
+n04517408	vacation home
+n04517823	vacuum, vacuum cleaner
+n04517999	vacuum chamber
+n04518132	vacuum flask, vacuum bottle
+n04518343	vacuum gauge, vacuum gage
+n04518643	Valenciennes, Valenciennes lace
+n04518764	valise
+n04519153	valve
+n04519536	valve
+n04519728	valve-in-head engine
+n04519887	vambrace, lower cannon
+n04520170	van
+n04520382	van, caravan
+n04520784	vane
+n04520962	vaporizer, vaporiser
+n04521571	variable-pitch propeller
+n04521863	variometer
+n04521987	varnish
+n04522168	vase
+n04523525	vault
+n04523831	vault, bank vault
+n04524142	vaulting horse, long horse, buck
+n04524313	vehicle
+n04524594	Velcro
+n04524716	velocipede
+n04524941	velour, velours
+n04525038	velvet
+n04525191	velveteen
+n04525305	vending machine
+n04525417	veneer, veneering
+n04525584	Venetian blind
+n04525821	Venn diagram, Venn's diagram
+n04526520	ventilation, ventilation system, ventilating system
+n04526800	ventilation shaft
+n04526964	ventilator
+n04527648	veranda, verandah, gallery
+n04528079	verdigris
+n04528968	vernier caliper, vernier micrometer
+n04529108	vernier scale, vernier
+n04529681	vertical file
+n04529962	vertical stabilizer, vertical stabiliser, vertical fin, tail fin, tailfin
+n04530283	vertical tail
+n04530456	Very pistol, Verey pistol
+n04530566	vessel, watercraft
+n04531098	vessel
+n04531873	vest, waistcoat
+n04532022	vestiture
+n04532106	vestment
+n04532398	vest pocket
+n04532504	vestry, sacristy
+n04532670	viaduct
+n04532831	vibraphone, vibraharp, vibes
+n04533042	vibrator
+n04533199	vibrator
+n04533499	Victrola
+n04533594	vicuna
+n04533700	videocassette
+n04533802	videocassette recorder, VCR
+n04533946	videodisk, videodisc, DVD
+n04534127	video recording, video
+n04534359	videotape
+n04534520	videotape
+n04534895	vigil light, vigil candle
+n04535252	villa
+n04535370	villa
+n04535524	villa
+n04536153	viol
+n04536335	viola
+n04536465	viola da braccio
+n04536595	viola da gamba, gamba, bass viol
+n04536765	viola d'amore
+n04536866	violin, fiddle
+n04537436	virginal, pair of virginals
+n04538249	viscometer, viscosimeter
+n04538403	viscose rayon, viscose
+n04538552	vise, bench vise
+n04538878	visor, vizor
+n04539053	visual display unit, VDU
+n04539203	vivarium
+n04539407	Viyella
+n04539794	voile
+n04540053	volleyball
+n04540255	volleyball net
+n04540397	voltage regulator
+n04540761	voltaic cell, galvanic cell, primary cell
+n04541136	voltaic pile, pile, galvanic pile
+n04541320	voltmeter
+n04541662	vomitory
+n04541777	von Neumann machine
+n04541987	voting booth
+n04542095	voting machine
+n04542329	voussoir
+n04542474	vox angelica, voix celeste
+n04542595	vox humana
+n04542715	waders
+n04542858	wading pool
+n04542943	waffle iron
+n04543158	wagon, waggon
+n04543509	wagon, coaster wagon
+n04543636	wagon tire
+n04543772	wagon wheel
+n04543924	wain
+n04543996	wainscot, wainscoting, wainscotting
+n04544325	wainscoting, wainscotting
+n04544450	waist pack, belt bag
+n04545305	walker, baby-walker, go-cart
+n04545471	walker, Zimmer, Zimmer frame
+n04545748	walker
+n04545858	walkie-talkie, walky-talky
+n04545984	walk-in
+n04546081	walking shoe
+n04546194	walking stick
+n04546340	Walkman
+n04546595	walk-up apartment, walk-up
+n04546855	wall
+n04547592	wall
+n04548280	wall clock
+n04548362	wallet, billfold, notecase, pocketbook
+n04549028	wall tent
+n04549122	wall unit
+n04549629	wand
+n04549721	Wankel engine, Wankel rotary engine, epitrochoidal engine
+n04549919	ward, hospital ward
+n04550184	wardrobe, closet, press
+n04550676	wardroom
+n04551055	warehouse, storage warehouse
+n04551833	warming pan
+n04552097	war paint
+n04552348	warplane, military plane
+n04552551	war room
+n04552696	warship, war vessel, combat ship
+n04553389	wash
+n04553561	wash-and-wear
+n04553703	washbasin, handbasin, washbowl, lavabo, wash-hand basin
+n04554211	washboard, splashboard
+n04554406	washboard
+n04554684	washer, automatic washer, washing machine
+n04554871	washer
+n04554998	washhouse
+n04555291	washroom
+n04555400	washstand, wash-hand stand
+n04555600	washtub
+n04555700	wastepaper basket, waste-paper basket, wastebasket, waste basket, circular file
+n04555897	watch, ticker
+n04556408	watch cap
+n04556533	watch case
+n04556664	watch glass
+n04556948	watchtower
+n04557308	water-base paint
+n04557522	water bed
+n04557648	water bottle
+n04557751	water butt
+n04558059	water cart
+n04558199	water chute
+n04558478	water closet, closet, W.C., loo
+n04558804	watercolor, water-color, watercolour, water-colour
+n04559023	water-cooled reactor
+n04559166	water cooler
+n04559451	water faucet, water tap, tap, hydrant
+n04559620	water filter
+n04559730	water gauge, water gage, water glass
+n04559910	water glass
+n04559994	water hazard
+n04560113	water heater, hot-water heater, hot-water tank
+n04560292	watering can, watering pot
+n04560502	watering cart
+n04560619	water jacket
+n04560804	water jug
+n04560882	water jump
+n04561010	water level
+n04561287	water meter
+n04561422	water mill
+n04561734	waterproof
+n04561857	waterproofing
+n04561965	water pump
+n04562122	water scooter, sea scooter, scooter
+n04562262	water ski
+n04562496	waterspout
+n04562935	water tower
+n04563020	water wagon, water waggon
+n04563204	waterwheel, water wheel
+n04563413	waterwheel, water wheel
+n04563560	water wings
+n04563790	waterworks
+n04564278	wattmeter
+n04564581	waxwork, wax figure
+n04565039	ways, shipway, slipway
+n04565375	weapon, arm, weapon system
+n04566257	weaponry, arms, implements of war, weapons system, munition
+n04566561	weapons carrier
+n04566756	weathercock
+n04567098	weatherglass
+n04567593	weather satellite, meteorological satellite
+n04567746	weather ship
+n04568069	weathervane, weather vane, vane, wind vane
+n04568557	web, entanglement
+n04568713	web
+n04568841	webbing
+n04569063	webcam
+n04569520	wedge
+n04569822	wedge
+n04570118	wedgie
+n04570214	Wedgwood
+n04570416	weeder, weed-whacker
+n04570532	weeds, widow's weeds
+n04570815	weekender
+n04570958	weighbridge
+n04571292	weight, free weight, exercising weight
+n04571566	weir
+n04571686	weir
+n04571800	welcome wagon
+n04571958	weld
+n04572121	welder's mask
+n04572235	weldment
+n04572935	well
+n04573045	wellhead
+n04573281	welt
+n04573379	Weston cell, cadmium cell
+n04573513	wet bar
+n04573625	wet-bulb thermometer
+n04573832	wet cell
+n04573937	wet fly
+n04574067	wet suit
+n04574348	whaleboat
+n04574471	whaler, whaling ship
+n04574606	whaling gun
+n04574999	wheel
+n04575723	wheel
+n04575824	wheel and axle
+n04576002	wheelchair
+n04576211	wheeled vehicle
+n04576971	wheelwork
+n04577139	wherry
+n04577293	wherry, Norfolk wherry
+n04577426	whetstone
+n04577567	whiffletree, whippletree, swingletree
+n04577769	whip
+n04578112	whipcord
+n04578329	whipping post
+n04578559	whipstitch, whipping, whipstitching
+n04578708	whirler
+n04578801	whisk, whisk broom
+n04578934	whisk
+n04579056	whiskey bottle
+n04579145	whiskey jug
+n04579230	whispering gallery, whispering dome
+n04579432	whistle
+n04579667	whistle
+n04579986	white
+n04580493	white goods
+n04581102	whitewash
+n04581595	whorehouse, brothel, bordello, bagnio, house of prostitution, house of ill repute, bawdyhouse, cathouse, sporting house
+n04581829	wick, taper
+n04582205	wicker, wickerwork, caning
+n04582349	wicker basket
+n04582771	wicket, hoop
+n04582869	wicket
+n04583022	wickiup, wikiup
+n04583212	wide-angle lens, fisheye lens
+n04583620	widebody aircraft, wide-body aircraft, wide-body, twin-aisle airplane
+n04583888	wide wale
+n04583967	widow's walk
+n04584056	Wiffle, Wiffle Ball
+n04584207	wig
+n04584373	wigwam
+n04585128	Wilton, Wilton carpet
+n04585318	wimple
+n04585456	wincey
+n04585626	winceyette
+n04585745	winch, windlass
+n04585980	Winchester
+n04586072	windbreak, shelterbelt
+n04586581	winder, key
+n04586932	wind instrument, wind
+n04587327	windjammer
+n04587404	windmill, aerogenerator, wind generator
+n04587559	windmill
+n04587648	window
+n04588739	window
+n04589190	window blind
+n04589325	window box
+n04589434	window envelope
+n04589593	window frame
+n04589890	window screen
+n04590021	window seat
+n04590129	window shade
+n04590263	windowsill
+n04590553	windshield, windscreen
+n04590746	windshield wiper, windscreen wiper, wiper, wiper blade
+n04590933	Windsor chair
+n04591056	Windsor knot
+n04591157	Windsor tie
+n04591249	wind tee
+n04591359	wind tunnel
+n04591517	wind turbine
+n04591631	wine bar
+n04591713	wine bottle
+n04591887	wine bucket, wine cooler
+n04592005	wine cask, wine barrel
+n04592099	wineglass
+n04592356	winepress
+n04592465	winery, wine maker
+n04592596	wineskin
+n04592741	wing
+n04593077	wing chair
+n04593185	wing nut, wing-nut, wing screw, butterfly nut, thumbnut
+n04593376	wing tip
+n04593524	wing tip
+n04593629	winker, blinker, blinder
+n04593866	wiper, wiper arm, contact arm
+n04594114	wiper motor
+n04594218	wire
+n04594489	wire, conducting wire
+n04594742	wire cloth
+n04594828	wire cutter
+n04594919	wire gauge, wire gage
+n04595028	wireless local area network, WLAN, wireless fidelity, WiFi
+n04595285	wire matrix printer, wire printer, stylus printer
+n04595501	wire recorder
+n04595611	wire stripper
+n04595762	wirework, grillwork
+n04595855	wiring
+n04596116	wishing cap
+n04596492	witness box, witness stand
+n04596742	wok
+n04596852	woman's clothing
+n04597066	wood
+n04597309	woodcarving
+n04597400	wood chisel
+n04597804	woodenware
+n04597913	wooden spoon
+n04598136	woodscrew
+n04598318	woodshed
+n04598416	wood vise, woodworking vise, shoulder vise
+n04598582	woodwind, woodwind instrument, wood
+n04598965	woof, weft, filling, pick
+n04599124	woofer
+n04599235	wool, woolen, woollen
+n04600312	workbasket, workbox, workbag
+n04600486	workbench, work bench, bench
+n04600912	work-clothing, work-clothes
+n04601041	workhouse
+n04601159	workhouse
+n04601938	workpiece
+n04602762	workroom
+n04602840	works, workings
+n04602956	work-shirt
+n04603399	workstation
+n04603729	worktable, work table
+n04603872	workwear
+n04604276	World Wide Web, WWW, web
+n04604644	worm fence, snake fence, snake-rail fence, Virginia fence
+n04604806	worm gear
+n04605057	worm wheel
+n04605163	worsted
+n04605321	worsted, worsted yarn
+n04605446	wrap, wrapper
+n04605572	wraparound
+n04605726	wrapping, wrap, wrapper
+n04606251	wreck
+n04606574	wrench, spanner
+n04607035	wrestling mat
+n04607242	wringer
+n04607640	wrist pad
+n04607759	wrist pin, gudgeon pin
+n04607869	wristwatch, wrist watch
+n04607982	writing arm
+n04608329	writing desk
+n04608435	writing desk
+n04608567	writing implement
+n04608809	xerographic printer
+n04608923	Xerox, xerographic copier, Xerox machine
+n04609531	X-ray film
+n04609651	X-ray machine
+n04609811	X-ray tube
+n04610013	yacht, racing yacht
+n04610176	yacht chair
+n04610274	yagi, Yagi aerial
+n04610503	yard
+n04610676	yard
+n04611351	yardarm
+n04611795	yard marker
+n04611916	yardstick, yard measure
+n04612026	yarmulke, yarmulka, yarmelke
+n04612159	yashmak, yashmac
+n04612257	yataghan
+n04612373	yawl, dandy
+n04612504	yawl
+n04612840	yoke
+n04613015	yoke
+n04613158	yoke, coupling
+n04613696	yurt
+n04613939	Zamboni
+n04614505	zero
+n04614655	ziggurat, zikkurat, zikurat
+n04614844	zill
+n04615149	zip gun
+n04615226	zither, cither, zithern
+n04615644	zoot suit
+n04682018	shading
+n04950713	grain
+n04950952	wood grain, woodgrain, woodiness
+n04951071	graining, woodgraining
+n04951186	marbleization, marbleisation, marbleizing, marbleising
+n04951373	light, lightness
+n04951716	aura, aureole, halo, nimbus, glory, gloriole
+n04951875	sunniness
+n04953296	glint
+n04953678	opalescence, iridescence
+n04955160	polish, gloss, glossiness, burnish
+n04957356	primary color for pigments, primary colour for pigments
+n04957589	primary color for light, primary colour for light
+n04958634	colorlessness, colourlessness, achromatism, achromaticity
+n04958865	mottle
+n04959061	achromia
+n04959230	shade, tint, tincture, tone
+n04959672	chromatic color, chromatic colour, spectral color, spectral colour
+n04960277	black, blackness, inkiness
+n04960582	coal black, ebony, jet black, pitch black, sable, soot black
+n04961062	alabaster
+n04961331	bone, ivory, pearl, off-white
+n04961691	gray, grayness, grey, greyness
+n04962062	ash grey, ash gray, silver, silver grey, silver gray
+n04962240	charcoal, charcoal grey, charcoal gray, oxford grey, oxford gray
+n04963111	sanguine
+n04963307	Turkey red, alizarine red
+n04963588	crimson, ruby, deep red
+n04963740	dark red
+n04964001	claret
+n04964799	fuschia
+n04964878	maroon
+n04965179	orange, orangeness
+n04965451	reddish orange
+n04965661	yellow, yellowness
+n04966543	gamboge, lemon, lemon yellow, maize
+n04966941	pale yellow, straw, wheat
+n04967191	green, greenness, viridity
+n04967561	greenishness
+n04967674	sea green
+n04967801	sage green
+n04967882	bottle green
+n04968056	emerald
+n04968139	olive green, olive-green
+n04968749	jade green, jade
+n04968895	blue, blueness
+n04969242	azure, cerulean, sapphire, lazuline, sky-blue
+n04969540	steel blue
+n04969798	greenish blue, aqua, aquamarine, turquoise, cobalt blue, peacock blue
+n04969952	purplish blue, royal blue
+n04970059	purple, purpleness
+n04970312	Tyrian purple
+n04970398	indigo
+n04970470	lavender
+n04970631	reddish purple, royal purple
+n04970916	pink
+n04971211	carnation
+n04971313	rose, rosiness
+n04972350	chestnut
+n04972451	chocolate, coffee, deep brown, umber, burnt umber
+n04972801	light brown
+n04973020	tan, topaz
+n04973291	beige, ecru
+n04973386	reddish brown, sepia, burnt sienna, Venetian red, mahogany
+n04973585	brick red
+n04973669	copper, copper color
+n04973816	Indian red
+n04974145	puce
+n04974340	olive
+n04974859	ultramarine
+n04975739	complementary color, complementary
+n04976319	pigmentation
+n04976952	complexion, skin color, skin colour
+n04977412	ruddiness, rosiness
+n04978561	nonsolid color, nonsolid colour, dithered color, dithered colour
+n04979002	aposematic coloration, warning coloration
+n04979307	cryptic coloration
+n04981658	ring
+n05102764	center of curvature, centre of curvature
+n05218119	cadaver, corpse, stiff, clay, remains
+n05233741	mandibular notch
+n05235879	rib
+n05238282	skin, tegument, cutis
+n05239437	skin graft
+n05241218	epidermal cell
+n05241485	melanocyte
+n05241662	prickle cell
+n05242070	columnar cell, columnar epithelial cell
+n05242239	spongioblast
+n05242928	squamous cell
+n05244421	amyloid plaque, amyloid protein plaque
+n05244755	dental plaque, bacterial plaque
+n05244934	macule, macula
+n05245192	freckle, lentigo
+n05257476	bouffant
+n05257967	sausage curl
+n05258051	forelock
+n05258627	spit curl, kiss curl
+n05259914	pigtail
+n05260127	pageboy
+n05260240	pompadour
+n05261310	thatch
+n05262422	soup-strainer, toothbrush
+n05262534	mustachio, moustachio, handle-bars
+n05262698	walrus mustache, walrus moustache
+n05263183	stubble
+n05263316	vandyke beard, vandyke
+n05263448	soul patch, Attilio
+n05265736	esophageal smear
+n05266096	paraduodenal smear, duodenal smear
+n05266879	specimen
+n05278922	punctum
+n05279953	glenoid fossa, glenoid cavity
+n05282652	diastema
+n05285623	marrow, bone marrow
+n05302499	mouth, oral cavity, oral fissure, rima oris
+n05314075	canthus
+n05399034	milk
+n05399243	mother's milk
+n05399356	colostrum, foremilk
+n05418717	vein, vena, venous blood vessel
+n05427346	ganglion cell, gangliocyte
+n05442594	X chromosome
+n05447757	embryonic cell, formative cell
+n05448704	myeloblast
+n05448827	sideroblast
+n05449196	osteocyte
+n05449661	megalocyte, macrocyte
+n05449959	leukocyte, leucocyte, white blood cell, white cell, white blood corpuscle, white corpuscle, WBC
+n05450617	histiocyte
+n05451099	fixed phagocyte
+n05451384	lymphocyte, lymph cell
+n05453412	monoblast
+n05453657	neutrophil, neutrophile
+n05453815	microphage
+n05454833	sickle cell
+n05454978	siderocyte
+n05455113	spherocyte
+n05458173	ootid
+n05458576	oocyte
+n05459101	spermatid
+n05459457	Leydig cell, Leydig's cell
+n05459769	striated muscle cell, striated muscle fiber
+n05460759	smooth muscle cell
+n05464534	Ranvier's nodes, nodes of Ranvier
+n05467054	neuroglia, glia
+n05467758	astrocyte
+n05468098	protoplasmic astrocyte
+n05468739	oligodendrocyte
+n05469664	proprioceptor
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+n05491154	renal cortex
+n05526957	prepuce, foreskin
+n05538625	head, caput
+n05539947	scalp
+n05541509	frontal eminence
+n05542893	suture, sutura, fibrous joint
+n05545879	foramen magnum
+n05571341	esophagogastric junction, oesophagogastric junction
+n05578095	heel
+n05581932	cuticle
+n05584746	hangnail, agnail
+n05586759	exoskeleton
+n05604434	abdominal wall
+n05716342	lemon
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+n06255081	vehicle
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+n06262567	film, cinema, celluloid
+n06262943	silver screen
+n06263202	free press
+n06263369	press, public press
+n06263609	print media
+n06263762	storage medium, data-storage medium
+n06263895	magnetic storage medium, magnetic medium, magnetic storage
+n06266417	journalism, news media
+n06266633	Fleet Street
+n06266710	photojournalism
+n06266878	news photography
+n06266973	rotogravure
+n06267145	newspaper, paper
+n06267564	daily
+n06267655	gazette
+n06267758	school newspaper, school paper
+n06267893	tabloid, rag, sheet
+n06267991	yellow journalism, tabloid, tab
+n06271778	telecommunication, telecom
+n06272290	telephone, telephony
+n06272612	voice mail, voicemail
+n06272803	call, phone call, telephone call
+n06273207	call-back
+n06273294	collect call
+n06273414	call forwarding
+n06273555	call-in
+n06273743	call waiting
+n06273890	crank call
+n06273986	local call
+n06274092	long distance, long-distance call, trunk call
+n06274292	toll call
+n06274546	wake-up call
+n06274760	three-way calling
+n06274921	telegraphy
+n06275095	cable, cablegram, overseas telegram
+n06275353	wireless
+n06275471	radiotelegraph, radiotelegraphy, wireless telegraphy
+n06276501	radiotelephone, radiotelephony, wireless telephone
+n06276697	broadcasting
+n06276902	Rediffusion
+n06277025	multiplex
+n06277135	radio, radiocommunication, wireless
+n06277280	television, telecasting, TV, video
+n06278338	cable television, cable
+n06278475	high-definition television, HDTV
+n06281040	reception
+n06281175	signal detection, detection
+n06340977	Hakham
+n06359193	web site, website, internet site, site
+n06359467	chat room, chatroom
+n06359657	portal site, portal
+n06415688	jotter
+n06417096	breviary
+n06418693	wordbook
+n06419354	desk dictionary, collegiate dictionary
+n06423496	reckoner, ready reckoner
+n06470073	document, written document, papers
+n06591815	album, record album
+n06592078	concept album
+n06592281	rock opera
+n06592421	tribute album, benefit album
+n06595351	magazine, mag
+n06596179	colour supplement
+n06596364	comic book
+n06596474	news magazine
+n06596607	pulp, pulp magazine
+n06596727	slick, slick magazine, glossy
+n06596845	trade magazine
+n06613686	movie, film, picture, moving picture, moving-picture show, motion picture, motion-picture show, picture show, pic, flick
+n06614901	outtake
+n06616216	shoot-'em-up
+n06618653	spaghetti Western
+n06625062	encyclical, encyclical letter
+n06785654	crossword puzzle, crossword
+n06793231	sign
+n06794110	street sign
+n06874185	traffic light, traffic signal, stoplight
+n06883725	swastika, Hakenkreuz
+n06892775	concert
+n06998748	artwork, art, graphics, nontextual matter
+n07005523	lobe
+n07248320	book jacket, dust cover, dust jacket, dust wrapper
+n07273802	cairn
+n07461050	three-day event
+n07556406	comfort food
+n07556637	comestible, edible, eatable, pabulum, victual, victuals
+n07556872	tuck
+n07556970	course
+n07557165	dainty, delicacy, goody, kickshaw, treat
+n07557434	dish
+n07560193	fast food
+n07560331	finger food
+n07560422	ingesta
+n07560542	kosher
+n07560652	fare
+n07560903	diet
+n07561112	diet
+n07561590	dietary
+n07561848	balanced diet
+n07562017	bland diet, ulcer diet
+n07562172	clear liquid diet
+n07562379	diabetic diet
+n07562495	dietary supplement
+n07562651	carbohydrate loading, carbo loading
+n07562881	fad diet
+n07562984	gluten-free diet
+n07563207	high-protein diet
+n07563366	high-vitamin diet, vitamin-deficiency diet
+n07563642	light diet
+n07563800	liquid diet
+n07564008	low-calorie diet
+n07564101	low-fat diet
+n07564292	low-sodium diet, low-salt diet, salt-free diet
+n07564515	macrobiotic diet
+n07564629	reducing diet, obesity diet
+n07564796	soft diet, pap, spoon food
+n07564971	vegetarianism
+n07565083	menu
+n07565161	chow, chuck, eats, grub
+n07565259	board, table
+n07565608	mess
+n07565725	ration
+n07565945	field ration
+n07566092	K ration
+n07566231	C-ration
+n07566340	foodstuff, food product
+n07566863	starches
+n07567039	breadstuff
+n07567139	coloring, colouring, food coloring, food colouring, food color, food colour
+n07567390	concentrate
+n07567611	tomato concentrate
+n07567707	meal
+n07567980	kibble
+n07568095	cornmeal, Indian meal
+n07568241	farina
+n07568389	matzo meal, matzoh meal, matzah meal
+n07568502	oatmeal, rolled oats
+n07568625	pea flour
+n07568818	roughage, fiber
+n07568991	bran
+n07569106	flour
+n07569423	plain flour
+n07569543	wheat flour
+n07569644	whole wheat flour, graham flour, graham, whole meal flour
+n07569873	soybean meal, soybean flour, soy flour
+n07570021	semolina
+n07570530	corn gluten feed
+n07570720	nutriment, nourishment, nutrition, sustenance, aliment, alimentation, victuals
+n07572353	commissariat, provisions, provender, viands, victuals
+n07572616	larder
+n07572858	frozen food, frozen foods
+n07572957	canned food, canned foods, canned goods, tinned goods
+n07573103	canned meat, tinned meat
+n07573347	Spam
+n07573453	dehydrated food, dehydrated foods
+n07573563	square meal
+n07573696	meal, repast
+n07574176	potluck
+n07574426	refection
+n07574504	refreshment
+n07574602	breakfast
+n07574780	continental breakfast, petit dejeuner
+n07574923	brunch
+n07575076	lunch, luncheon, tiffin, dejeuner
+n07575226	business lunch
+n07575392	high tea
+n07575510	tea, afternoon tea, teatime
+n07575726	dinner
+n07575984	supper
+n07576182	buffet
+n07576438	picnic
+n07576577	cookout
+n07576781	barbecue, barbeque
+n07576969	clambake
+n07577144	fish fry
+n07577374	bite, collation, snack
+n07577538	nosh
+n07577657	nosh-up
+n07577772	ploughman's lunch
+n07577918	coffee break, tea break
+n07578093	banquet, feast, spread
+n07579575	entree, main course
+n07579688	piece de resistance
+n07579787	plate
+n07579917	adobo
+n07580053	side dish, side order, entremets
+n07580253	special
+n07580359	casserole
+n07580470	chicken casserole
+n07580592	chicken cacciatore, chicken cacciatora, hunter's chicken
+n07581249	antipasto
+n07581346	appetizer, appetiser, starter
+n07581607	canape
+n07581775	cocktail
+n07581931	fruit cocktail
+n07582027	crab cocktail
+n07582152	shrimp cocktail
+n07582277	hors d'oeuvre
+n07582441	relish
+n07582609	dip
+n07582811	bean dip
+n07582892	cheese dip
+n07582970	clam dip
+n07583066	guacamole
+n07583197	soup
+n07583865	soup du jour
+n07583978	alphabet soup
+n07584110	consomme
+n07584228	madrilene
+n07584332	bisque
+n07584423	borsch, borsh, borscht, borsht, borshch, bortsch
+n07584593	broth
+n07584859	barley water
+n07584938	bouillon
+n07585015	beef broth, beef stock
+n07585107	chicken broth, chicken stock
+n07585208	broth, stock
+n07585474	stock cube
+n07585557	chicken soup
+n07585644	cock-a-leekie, cocky-leeky
+n07585758	gazpacho
+n07585906	gumbo
+n07585997	julienne
+n07586099	marmite
+n07586179	mock turtle soup
+n07586318	mulligatawny
+n07586485	oxtail soup
+n07586604	pea soup
+n07586718	pepper pot, Philadelphia pepper pot
+n07586894	petite marmite, minestrone, vegetable soup
+n07587023	potage, pottage
+n07587111	pottage
+n07587206	turtle soup, green turtle soup
+n07587331	eggdrop soup
+n07587441	chowder
+n07587618	corn chowder
+n07587700	clam chowder
+n07587819	Manhattan clam chowder
+n07587962	New England clam chowder
+n07588111	fish chowder
+n07588193	won ton, wonton, wonton soup
+n07588299	split-pea soup
+n07588419	green pea soup, potage St. Germain
+n07588574	lentil soup
+n07588688	Scotch broth
+n07588817	vichyssoise
+n07588947	stew
+n07589458	bigos
+n07589543	Brunswick stew
+n07589724	burgoo
+n07589872	burgoo
+n07589967	olla podrida, Spanish burgoo
+n07590068	mulligan stew, mulligan, Irish burgoo
+n07590177	purloo, chicken purloo, poilu
+n07590320	goulash, Hungarian goulash, gulyas
+n07590502	hotchpotch
+n07590611	hot pot, hotpot
+n07590752	beef goulash
+n07590841	pork-and-veal goulash
+n07590974	porkholt
+n07591049	Irish stew
+n07591162	oyster stew
+n07591236	lobster stew
+n07591330	lobscouse, lobscuse, scouse
+n07591473	fish stew
+n07591586	bouillabaisse
+n07591813	matelote
+n07591961	paella
+n07592094	fricassee
+n07592317	chicken stew
+n07592400	turkey stew
+n07592481	beef stew
+n07592656	ragout
+n07592768	ratatouille
+n07592922	salmi
+n07593004	pot-au-feu
+n07593107	slumgullion
+n07593199	smorgasbord
+n07593471	viand
+n07593774	ready-mix
+n07593972	brownie mix
+n07594066	cake mix
+n07594155	lemonade mix
+n07594250	self-rising flour, self-raising flour
+n07594737	choice morsel, tidbit, titbit
+n07594840	savory, savoury
+n07595051	calf's-foot jelly
+n07595180	caramel, caramelized sugar
+n07595368	lump sugar
+n07595649	cane sugar
+n07595751	castor sugar, caster sugar
+n07595914	powdered sugar
+n07596046	granulated sugar
+n07596160	icing sugar
+n07596362	corn sugar
+n07596452	brown sugar
+n07596566	demerara, demerara sugar
+n07596684	sweet, confection
+n07596967	confectionery
+n07597145	confiture
+n07597263	sweetmeat
+n07597365	candy, confect
+n07598256	candy bar
+n07598529	carob bar
+n07598622	hardbake
+n07598734	hard candy
+n07598928	barley-sugar, barley candy
+n07599068	brandyball
+n07599161	jawbreaker
+n07599242	lemon drop
+n07599383	sourball
+n07599468	patty
+n07599554	peppermint patty
+n07599649	bonbon
+n07599783	brittle, toffee, toffy
+n07599911	peanut brittle
+n07599998	chewing gum, gum
+n07600177	gum ball
+n07600285	bubble gum
+n07600394	butterscotch
+n07600506	candied fruit, succade, crystallized fruit
+n07600696	candied apple, candy apple, taffy apple, caramel apple, toffee apple
+n07600895	crystallized ginger
+n07601025	grapefruit peel
+n07601175	lemon peel
+n07601290	orange peel
+n07601407	candied citrus peel
+n07601572	candy cane
+n07601686	candy corn
+n07601809	caramel
+n07602650	center, centre
+n07604956	comfit
+n07605040	cotton candy, spun sugar, candyfloss
+n07605198	dragee
+n07605282	dragee
+n07605380	fondant
+n07605474	fudge
+n07605597	chocolate fudge
+n07605693	divinity, divinity fudge
+n07605804	penuche, penoche, panoche, panocha
+n07605944	gumdrop
+n07606058	jujube
+n07606191	honey crisp
+n07606278	mint, mint candy
+n07606419	horehound
+n07606538	peppermint, peppermint candy
+n07606669	jelly bean, jelly egg
+n07606764	kiss, candy kiss
+n07606933	molasses kiss
+n07607027	meringue kiss
+n07607138	chocolate kiss
+n07607361	licorice, liquorice
+n07607492	Life Saver
+n07607605	lollipop, sucker, all-day sucker
+n07607707	lozenge
+n07607832	cachou
+n07607967	cough drop, troche, pastille, pastil
+n07608098	marshmallow
+n07608245	marzipan, marchpane
+n07608339	nougat
+n07608429	nougat bar
+n07608533	nut bar
+n07608641	peanut bar
+n07608721	popcorn ball
+n07608866	praline
+n07608980	rock candy
+n07609083	rock candy, rock
+n07609215	sugar candy
+n07609316	sugarplum
+n07609407	taffy
+n07609549	molasses taffy
+n07609632	truffle, chocolate truffle
+n07609728	Turkish Delight
+n07609840	dessert, sweet, afters
+n07610295	ambrosia, nectar
+n07610502	ambrosia
+n07610620	baked Alaska
+n07610746	blancmange
+n07610890	charlotte
+n07611046	compote, fruit compote
+n07611148	dumpling
+n07611267	flan
+n07611358	frozen dessert
+n07611733	junket
+n07611839	mousse
+n07611991	mousse
+n07612137	pavlova
+n07612273	peach melba
+n07612367	whip
+n07612530	prune whip
+n07612632	pudding
+n07612996	pudding, pud
+n07613158	syllabub, sillabub
+n07613266	tiramisu
+n07613480	trifle
+n07613671	tipsy cake
+n07613815	jello, Jell-O
+n07614103	apple dumpling
+n07614198	ice, frappe
+n07614348	water ice, sorbet
+n07614500	ice cream, icecream
+n07614730	ice-cream cone
+n07614825	chocolate ice cream
+n07615052	Neapolitan ice cream
+n07615190	peach ice cream
+n07615289	sherbert, sherbet
+n07615460	strawberry ice cream
+n07615569	tutti-frutti
+n07615671	vanilla ice cream
+n07615774	ice lolly, lolly, lollipop, popsicle
+n07615954	ice milk
+n07616046	frozen yogurt
+n07616174	snowball
+n07616265	snowball
+n07616386	parfait
+n07616487	ice-cream sundae, sundae
+n07616590	split
+n07616748	banana split
+n07616906	frozen pudding
+n07617051	frozen custard, soft ice cream
+n07617188	pudding
+n07617344	flummery
+n07617447	fish mousse
+n07617526	chicken mousse
+n07617611	chocolate mousse
+n07617708	plum pudding, Christmas pudding
+n07617839	carrot pudding
+n07617932	corn pudding
+n07618029	steamed pudding
+n07618119	duff, plum duff
+n07618281	vanilla pudding
+n07618432	chocolate pudding
+n07618587	brown Betty
+n07618684	Nesselrode, Nesselrode pudding
+n07618871	pease pudding
+n07619004	custard
+n07619208	creme caramel
+n07619301	creme anglais
+n07619409	creme brulee
+n07619508	fruit custard
+n07619881	tapioca
+n07620047	tapioca pudding
+n07620145	roly-poly, roly-poly pudding
+n07620327	suet pudding
+n07620597	Bavarian cream
+n07620689	maraschino, maraschino cherry
+n07621264	nonpareil
+n07621497	zabaglione, sabayon
+n07621618	garnish
+n07623136	pastry, pastry dough
+n07624466	turnover
+n07624666	apple turnover
+n07624757	knish
+n07624924	pirogi, piroshki, pirozhki
+n07625061	samosa
+n07625324	timbale
+n07627931	puff paste, pate feuillete
+n07628068	phyllo
+n07628181	puff batter, pouf paste, pate a choux
+n07631926	ice-cream cake, icebox cake
+n07639069	doughnut, donut, sinker
+n07641928	fish cake, fish ball
+n07642361	fish stick, fish finger
+n07642471	conserve, preserve, conserves, preserves
+n07642742	apple butter
+n07642833	chowchow
+n07642933	jam
+n07643026	lemon curd, lemon cheese
+n07643200	strawberry jam, strawberry preserves
+n07643306	jelly
+n07643474	apple jelly
+n07643577	crabapple jelly
+n07643679	grape jelly
+n07643764	marmalade
+n07643891	orange marmalade
+n07643981	gelatin, jelly
+n07644244	gelatin dessert
+n07648913	buffalo wing
+n07648997	barbecued wing
+n07650792	mess
+n07650903	mince
+n07651025	puree
+n07654148	barbecue, barbeque
+n07654298	biryani, biriani
+n07655067	escalope de veau Orloff
+n07655263	saute
+n07663899	patty, cake
+n07665438	veal parmesan, veal parmigiana
+n07666176	veal cordon bleu
+n07672914	margarine, margarin, oleo, oleomargarine, marge
+n07678586	mincemeat
+n07678729	stuffing, dressing
+n07678953	turkey stuffing
+n07679034	oyster stuffing, oyster dressing
+n07679140	forcemeat, farce
+n07679356	bread, breadstuff, staff of life
+n07680168	anadama bread
+n07680313	bap
+n07680416	barmbrack
+n07680517	breadstick, bread-stick
+n07680655	grissino
+n07680761	brown bread, Boston brown bread
+n07680932	bun, roll
+n07681264	tea bread
+n07681355	caraway seed bread
+n07681450	challah, hallah
+n07681691	cinnamon bread
+n07681805	cracked-wheat bread
+n07681926	cracker
+n07682197	crouton
+n07682316	dark bread, whole wheat bread, whole meal bread, brown bread
+n07682477	English muffin
+n07682624	flatbread
+n07682808	garlic bread
+n07682952	gluten bread
+n07683039	graham bread
+n07683138	Host
+n07683265	flatbrod
+n07683360	bannock
+n07683490	chapatti, chapati
+n07683617	pita, pocket bread
+n07683786	loaf of bread, loaf
+n07684084	French loaf
+n07684164	matzo, matzoh, matzah, unleavened bread
+n07684289	nan, naan
+n07684422	onion bread
+n07684517	raisin bread
+n07684600	quick bread
+n07684938	banana bread
+n07685031	date bread
+n07685118	date-nut bread
+n07685218	nut bread
+n07685303	oatcake
+n07685399	Irish soda bread
+n07685546	skillet bread, fry bread
+n07685730	rye bread
+n07685918	black bread, pumpernickel
+n07686021	Jewish rye bread, Jewish rye
+n07686202	limpa
+n07686299	Swedish rye bread, Swedish rye
+n07686461	salt-rising bread
+n07686634	simnel
+n07686720	sour bread, sourdough bread
+n07686873	toast
+n07687053	wafer
+n07687211	white bread, light bread
+n07687381	baguet, baguette
+n07687469	French bread
+n07687626	Italian bread
+n07687789	cornbread
+n07688021	corn cake
+n07688130	skillet corn bread
+n07688265	ashcake, ash cake, corn tash
+n07688412	hoecake
+n07688624	cornpone, pone
+n07688757	corn dab, corn dodger, dodger
+n07688898	hush puppy, hushpuppy
+n07689003	johnnycake, johnny cake, journey cake
+n07689217	Shawnee cake
+n07689313	spoon bread, batter bread
+n07689490	cinnamon toast
+n07689624	orange toast
+n07689757	Melba toast
+n07689842	zwieback, rusk, Brussels biscuit, twice-baked bread
+n07690019	frankfurter bun, hotdog bun
+n07690152	hamburger bun, hamburger roll
+n07690273	muffin, gem
+n07690431	bran muffin
+n07690511	corn muffin
+n07690585	Yorkshire pudding
+n07690739	popover
+n07690892	scone
+n07691091	drop scone, griddlecake, Scotch pancake
+n07691237	cross bun, hot cross bun
+n07691539	brioche
+n07691650	crescent roll, croissant
+n07691758	hard roll, Vienna roll
+n07691863	soft roll
+n07691954	kaiser roll
+n07692114	Parker House roll
+n07692248	clover-leaf roll
+n07692405	onion roll
+n07692517	bialy, bialystoker
+n07692614	sweet roll, coffee roll
+n07692887	bear claw, bear paw
+n07693048	cinnamon roll, cinnamon bun, cinnamon snail
+n07693223	honey bun, sticky bun, caramel bun, schnecken
+n07693439	pinwheel roll
+n07693590	danish, danish pastry
+n07693725	bagel, beigel
+n07693889	onion bagel
+n07693972	biscuit
+n07694169	rolled biscuit
+n07694403	baking-powder biscuit
+n07694516	buttermilk biscuit, soda biscuit
+n07694659	shortcake
+n07694839	hardtack, pilot biscuit, pilot bread, sea biscuit, ship biscuit
+n07695187	saltine
+n07695284	soda cracker
+n07695410	oyster cracker
+n07695504	water biscuit
+n07695652	graham cracker
+n07695742	pretzel
+n07695878	soft pretzel
+n07695965	sandwich
+n07696403	sandwich plate
+n07696527	butty
+n07696625	ham sandwich
+n07696728	chicken sandwich
+n07696839	club sandwich, three-decker, triple-decker
+n07696977	open-face sandwich, open sandwich
+n07697100	hamburger, beefburger, burger
+n07697313	cheeseburger
+n07697408	tunaburger
+n07697537	hotdog, hot dog, red hot
+n07697699	Sloppy Joe
+n07697825	bomber, grinder, hero, hero sandwich, hoagie, hoagy, Cuban sandwich, Italian sandwich, poor boy, sub, submarine, submarine sandwich, torpedo, wedge, zep
+n07698250	gyro
+n07698401	bacon-lettuce-tomato sandwich, BLT
+n07698543	Reuben
+n07698672	western, western sandwich
+n07698782	wrap
+n07700003	spaghetti
+n07703889	hasty pudding
+n07704054	gruel
+n07704205	congee, jook
+n07704305	skilly
+n07705931	edible fruit
+n07707451	vegetable, veggie, veg
+n07708124	julienne, julienne vegetable
+n07708398	raw vegetable, rabbit food
+n07708512	crudites
+n07708685	celery stick
+n07708798	legume
+n07709046	pulse
+n07709172	potherb
+n07709333	greens, green, leafy vegetable
+n07709701	chop-suey greens
+n07709881	bean curd, tofu
+n07710007	solanaceous vegetable
+n07710283	root vegetable
+n07710616	potato, white potato, Irish potato, murphy, spud, tater
+n07710952	baked potato
+n07711080	french fries, french-fried potatoes, fries, chips
+n07711232	home fries, home-fried potatoes
+n07711371	jacket potato
+n07711569	mashed potato
+n07711683	potato skin, potato peel, potato peelings
+n07711799	Uruguay potato
+n07711907	yam
+n07712063	sweet potato
+n07712267	yam
+n07712382	snack food
+n07712559	chip, crisp, potato chip, Saratoga chip
+n07712748	corn chip
+n07712856	tortilla chip
+n07712959	nacho
+n07713074	eggplant, aubergine, mad apple
+n07713267	pieplant, rhubarb
+n07713395	cruciferous vegetable
+n07713763	mustard, mustard greens, leaf mustard, Indian mustard
+n07713895	cabbage, chou
+n07714078	kale, kail, cole
+n07714188	collards, collard greens
+n07714287	Chinese cabbage, celery cabbage, Chinese celery
+n07714448	bok choy, bok choi
+n07714571	head cabbage
+n07714802	red cabbage
+n07714895	savoy cabbage, savoy
+n07714990	broccoli
+n07715103	cauliflower
+n07715221	brussels sprouts
+n07715407	broccoli rabe, broccoli raab
+n07715561	squash
+n07715721	summer squash
+n07716034	yellow squash
+n07716203	crookneck, crookneck squash, summer crookneck
+n07716358	zucchini, courgette
+n07716504	marrow, vegetable marrow
+n07716649	cocozelle
+n07716750	pattypan squash
+n07716906	spaghetti squash
+n07717070	winter squash
+n07717410	acorn squash
+n07717556	butternut squash
+n07717714	hubbard squash
+n07717858	turban squash
+n07718068	buttercup squash
+n07718195	cushaw
+n07718329	winter crookneck squash
+n07718472	cucumber, cuke
+n07718671	gherkin
+n07718747	artichoke, globe artichoke
+n07718920	artichoke heart
+n07719058	Jerusalem artichoke, sunchoke
+n07719213	asparagus
+n07719330	bamboo shoot
+n07719437	sprout
+n07719616	bean sprout
+n07719756	alfalfa sprout
+n07719839	beet, beetroot
+n07719980	beet green
+n07720084	sugar beet
+n07720185	mangel-wurzel
+n07720277	chard, Swiss chard, spinach beet, leaf beet
+n07720442	pepper
+n07720615	sweet pepper
+n07720875	bell pepper
+n07721018	green pepper
+n07721118	globe pepper
+n07721195	pimento, pimiento
+n07721325	hot pepper
+n07721456	chili, chili pepper, chilli, chilly, chile
+n07721678	jalapeno, jalapeno pepper
+n07721833	chipotle
+n07721942	cayenne, cayenne pepper
+n07722052	tabasco, red pepper
+n07722217	onion
+n07722390	Bermuda onion
+n07722485	green onion, spring onion, scallion
+n07722666	Vidalia onion
+n07722763	Spanish onion
+n07722888	purple onion, red onion
+n07723039	leek
+n07723177	shallot
+n07723330	salad green, salad greens
+n07723559	lettuce
+n07723753	butterhead lettuce
+n07723968	buttercrunch
+n07724078	Bibb lettuce
+n07724173	Boston lettuce
+n07724269	crisphead lettuce, iceberg lettuce, iceberg
+n07724492	cos, cos lettuce, romaine, romaine lettuce
+n07724654	leaf lettuce, loose-leaf lettuce
+n07724819	celtuce
+n07724943	bean, edible bean
+n07725158	goa bean
+n07725255	lentil
+n07725376	pea
+n07725531	green pea, garden pea
+n07725663	marrowfat pea
+n07725789	snow pea, sugar pea
+n07725888	sugar snap pea
+n07726009	split-pea
+n07726095	chickpea, garbanzo
+n07726230	cajan pea, pigeon pea, dahl
+n07726386	field pea
+n07726525	mushy peas
+n07726672	black-eyed pea, cowpea
+n07726796	common bean
+n07727048	kidney bean
+n07727140	navy bean, pea bean, white bean
+n07727252	pinto bean
+n07727377	frijole
+n07727458	black bean, turtle bean
+n07727578	fresh bean
+n07727741	flageolet, haricot
+n07727868	green bean
+n07728053	snap bean, snap
+n07728181	string bean
+n07728284	Kentucky wonder, Kentucky wonder bean
+n07728391	scarlet runner, scarlet runner bean, runner bean, English runner bean
+n07728585	haricot vert, haricots verts, French bean
+n07728708	wax bean, yellow bean
+n07728804	shell bean
+n07729000	lima bean
+n07729142	Fordhooks
+n07729225	sieva bean, butter bean, butterbean, civet bean
+n07729384	fava bean, broad bean
+n07729485	soy, soybean, soya, soya bean
+n07729828	green soybean
+n07729926	field soybean
+n07730033	cardoon
+n07730207	carrot
+n07730320	carrot stick
+n07730406	celery
+n07730562	pascal celery, Paschal celery
+n07730708	celeriac, celery root
+n07730855	chicory, curly endive
+n07731006	radicchio
+n07731122	coffee substitute
+n07731284	chicory, chicory root
+n07731436	Postum
+n07731587	chicory escarole, endive, escarole
+n07731767	Belgian endive, French endive, witloof
+n07731952	corn, edible corn
+n07732168	sweet corn, green corn
+n07732302	hominy
+n07732433	lye hominy
+n07732525	pearl hominy
+n07732636	popcorn
+n07732747	cress
+n07732904	watercress
+n07733005	garden cress
+n07733124	winter cress
+n07733217	dandelion green
+n07733394	gumbo, okra
+n07733567	kohlrabi, turnip cabbage
+n07733712	lamb's-quarter, pigweed, wild spinach
+n07733847	wild spinach
+n07734017	tomato
+n07734183	beefsteak tomato
+n07734292	cherry tomato
+n07734417	plum tomato
+n07734555	tomatillo, husk tomato, Mexican husk tomato
+n07734744	mushroom
+n07734879	stuffed mushroom
+n07735052	salsify
+n07735179	oyster plant, vegetable oyster
+n07735294	scorzonera, black salsify
+n07735404	parsnip
+n07735510	pumpkin
+n07735687	radish
+n07735803	turnip
+n07735981	white turnip
+n07736087	rutabaga, swede, swedish turnip, yellow turnip
+n07736256	turnip greens
+n07736371	sorrel, common sorrel
+n07736527	French sorrel
+n07736692	spinach
+n07736813	taro, taro root, cocoyam, dasheen, edda
+n07736971	truffle, earthnut
+n07737081	edible nut
+n07737594	bunya bunya
+n07737745	peanut, earthnut, goober, goober pea, groundnut, monkey nut
+n07738105	freestone
+n07738224	cling, clingstone
+n07739035	windfall
+n07739125	apple
+n07739344	crab apple, crabapple
+n07739506	eating apple, dessert apple
+n07739923	Baldwin
+n07740033	Cortland
+n07740115	Cox's Orange Pippin
+n07740220	Delicious
+n07740342	Golden Delicious, Yellow Delicious
+n07740461	Red Delicious
+n07740597	Empire
+n07740744	Grimes' golden
+n07740855	Jonathan
+n07740954	McIntosh
+n07741138	Macoun
+n07741235	Northern Spy
+n07741357	Pearmain
+n07741461	Pippin
+n07741623	Prima
+n07741706	Stayman
+n07741804	Winesap
+n07741888	Stayman Winesap
+n07742012	cooking apple
+n07742224	Bramley's Seedling
+n07742313	Granny Smith
+n07742415	Lane's Prince Albert
+n07742513	Newtown Wonder
+n07742605	Rome Beauty
+n07742704	berry
+n07743224	bilberry, whortleberry, European blueberry
+n07743384	huckleberry
+n07743544	blueberry
+n07743723	wintergreen, boxberry, checkerberry, teaberry, spiceberry
+n07743902	cranberry
+n07744057	lingonberry, mountain cranberry, cowberry, lowbush cranberry
+n07744246	currant
+n07744430	gooseberry
+n07744559	black currant
+n07744682	red currant
+n07744811	blackberry
+n07745046	boysenberry
+n07745197	dewberry
+n07745357	loganberry
+n07745466	raspberry
+n07745661	saskatoon, serviceberry, shadberry, juneberry
+n07745940	strawberry
+n07746038	sugarberry, hackberry
+n07746186	persimmon
+n07746334	acerola, barbados cherry, surinam cherry, West Indian cherry
+n07746551	carambola, star fruit
+n07746749	ceriman, monstera
+n07746910	carissa plum, natal plum
+n07747055	citrus, citrus fruit, citrous fruit
+n07747607	orange
+n07747811	temple orange
+n07747951	mandarin, mandarin orange
+n07748157	clementine
+n07748276	satsuma
+n07748416	tangerine
+n07748574	tangelo, ugli, ugli fruit
+n07748753	bitter orange, Seville orange, sour orange
+n07748912	sweet orange
+n07749095	Jaffa orange
+n07749192	navel orange
+n07749312	Valencia orange
+n07749446	kumquat
+n07749582	lemon
+n07749731	lime
+n07749870	key lime
+n07749969	grapefruit
+n07750146	pomelo, shaddock
+n07750299	citrange
+n07750449	citron
+n07750586	almond
+n07750736	Jordan almond
+n07750872	apricot
+n07751004	peach
+n07751148	nectarine
+n07751280	pitahaya
+n07751451	plum
+n07751737	damson, damson plum
+n07751858	greengage, greengage plum
+n07751977	beach plum
+n07752109	sloe
+n07752264	Victoria plum
+n07752377	dried fruit
+n07752514	dried apricot
+n07752602	prune
+n07752664	raisin
+n07752782	seedless raisin, sultana
+n07752874	seeded raisin
+n07752966	currant
+n07753113	fig
+n07753275	pineapple, ananas
+n07753448	anchovy pear, river pear
+n07753592	banana
+n07753743	passion fruit
+n07753980	granadilla
+n07754155	sweet calabash
+n07754279	bell apple, sweet cup, water lemon, yellow granadilla
+n07754451	breadfruit
+n07754684	jackfruit, jak, jack
+n07754894	cacao bean, cocoa bean
+n07755089	cocoa
+n07755262	canistel, eggfruit
+n07755411	melon
+n07755619	melon ball
+n07755707	muskmelon, sweet melon
+n07755929	cantaloup, cantaloupe
+n07756096	winter melon
+n07756325	honeydew, honeydew melon
+n07756499	Persian melon
+n07756641	net melon, netted melon, nutmeg melon
+n07756838	casaba, casaba melon
+n07756951	watermelon
+n07757132	cherry
+n07757312	sweet cherry, black cherry
+n07757511	bing cherry
+n07757602	heart cherry, oxheart, oxheart cherry
+n07757753	blackheart, blackheart cherry
+n07757874	capulin, Mexican black cherry
+n07757990	sour cherry
+n07758125	amarelle
+n07758260	morello
+n07758407	cocoa plum, coco plum, icaco
+n07758582	gherkin
+n07758680	grape
+n07758950	fox grape
+n07759194	Concord grape
+n07759324	Catawba
+n07759424	muscadine, bullace grape
+n07759576	scuppernong
+n07759691	slipskin grape
+n07759816	vinifera grape
+n07760070	emperor
+n07760153	muscat, muscatel, muscat grape
+n07760297	ribier
+n07760395	sultana
+n07760501	Tokay
+n07760673	flame tokay
+n07760755	Thompson Seedless
+n07760859	custard apple
+n07761141	cherimoya, cherimolla
+n07761309	soursop, guanabana
+n07761611	sweetsop, annon, sugar apple
+n07761777	ilama
+n07761954	pond apple
+n07762114	papaw, pawpaw
+n07762244	papaya
+n07762373	kai apple
+n07762534	ketembilla, kitembilla, kitambilla
+n07762740	ackee, akee
+n07762913	durian
+n07763107	feijoa, pineapple guava
+n07763290	genip, Spanish lime
+n07763483	genipap, genipap fruit
+n07763629	kiwi, kiwi fruit, Chinese gooseberry
+n07763792	loquat, Japanese plum
+n07763987	mangosteen
+n07764155	mango
+n07764315	sapodilla, sapodilla plum, sapota
+n07764486	sapote, mammee, marmalade plum
+n07764630	tamarind, tamarindo
+n07764847	avocado, alligator pear, avocado pear, aguacate
+n07765073	date
+n07765208	elderberry
+n07765361	guava
+n07765517	mombin
+n07765612	hog plum, yellow mombin
+n07765728	hog plum, wild plum
+n07765862	jaboticaba
+n07765999	jujube, Chinese date, Chinese jujube
+n07766173	litchi, litchi nut, litchee, lichi, leechee, lichee, lychee
+n07766409	longanberry, dragon's eye
+n07766530	mamey, mammee, mammee apple
+n07766723	marang
+n07766891	medlar
+n07767002	medlar
+n07767171	mulberry
+n07767344	olive
+n07767549	black olive, ripe olive
+n07767709	green olive
+n07767847	pear
+n07768068	bosc
+n07768139	anjou
+n07768230	bartlett, bartlett pear
+n07768318	seckel, seckel pear
+n07768423	plantain
+n07768590	plumcot
+n07768694	pomegranate
+n07768858	prickly pear
+n07769102	Barbados gooseberry, blade apple
+n07769306	quandong, quandang, quantong, native peach
+n07769465	quandong nut
+n07769584	quince
+n07769731	rambutan, rambotan
+n07769886	pulasan, pulassan
+n07770034	rose apple
+n07770180	sorb, sorb apple
+n07770439	sour gourd, monkey bread
+n07770571	edible seed
+n07770763	pumpkin seed
+n07770869	betel nut, areca nut
+n07771082	beechnut
+n07771212	walnut
+n07771405	black walnut
+n07771539	English walnut
+n07771731	brazil nut, brazil
+n07771891	butternut
+n07772026	souari nut
+n07772147	cashew, cashew nut
+n07772274	chestnut
+n07772413	chincapin, chinkapin, chinquapin
+n07772788	hazelnut, filbert, cobnut, cob
+n07772935	coconut, cocoanut
+n07773428	coconut milk, coconut water
+n07774182	grugru nut
+n07774295	hickory nut
+n07774479	cola extract
+n07774596	macadamia nut
+n07774719	pecan
+n07774842	pine nut, pignolia, pinon nut
+n07775050	pistachio, pistachio nut
+n07775197	sunflower seed
+n07783827	anchovy paste
+n07785487	rollmops
+n07800091	feed, provender
+n07800487	cattle cake
+n07800636	creep feed
+n07800740	fodder
+n07801007	feed grain
+n07801091	eatage, forage, pasture, pasturage, grass
+n07801342	silage, ensilage
+n07801508	oil cake
+n07801709	oil meal
+n07801779	alfalfa
+n07801892	broad bean, horse bean
+n07802026	hay
+n07802152	timothy
+n07802246	stover
+n07802417	grain, food grain, cereal
+n07802767	grist
+n07802863	groats
+n07802963	millet
+n07803093	barley, barleycorn
+n07803213	pearl barley
+n07803310	buckwheat
+n07803408	bulgur, bulghur, bulgur wheat
+n07803545	wheat, wheat berry
+n07803779	cracked wheat
+n07803895	stodge
+n07803992	wheat germ
+n07804152	oat
+n07804323	rice
+n07804543	brown rice
+n07804657	white rice, polished rice
+n07804771	wild rice, Indian rice
+n07804900	paddy
+n07805006	slop, slops, swill, pigswill, pigwash
+n07805254	mash
+n07805389	chicken feed, scratch
+n07805478	cud, rechewed food
+n07805594	bird feed, bird food, birdseed
+n07805731	petfood, pet-food, pet food
+n07805966	dog food
+n07806043	cat food
+n07806120	canary seed
+n07806221	salad
+n07806633	tossed salad
+n07806774	green salad
+n07806879	Caesar salad
+n07807002	salmagundi
+n07807171	salad nicoise
+n07807317	combination salad
+n07807472	chef's salad
+n07807594	potato salad
+n07807710	pasta salad
+n07807834	macaroni salad
+n07807922	fruit salad
+n07808022	Waldorf salad
+n07808166	crab Louis
+n07808268	herring salad
+n07808352	tuna fish salad, tuna salad
+n07808479	chicken salad
+n07808587	coleslaw, slaw
+n07808675	aspic
+n07808806	molded salad
+n07808904	tabbouleh, tabooli
+n07809096	ingredient, fixings
+n07809368	flavorer, flavourer, flavoring, flavouring, seasoner, seasoning
+n07810531	bouillon cube
+n07810907	condiment
+n07811416	herb
+n07812046	fines herbes
+n07812184	spice
+n07812662	spearmint oil
+n07812790	lemon oil
+n07812913	wintergreen oil, oil of wintergreen
+n07813107	salt, table salt, common salt
+n07813324	celery salt
+n07813495	onion salt
+n07813579	seasoned salt
+n07813717	sour salt
+n07813833	five spice powder
+n07814007	allspice
+n07814203	cinnamon
+n07814390	stick cinnamon
+n07814487	clove
+n07814634	cumin, cumin seed
+n07814790	fennel
+n07814925	ginger, gingerroot
+n07815163	ginger, powdered ginger
+n07815294	mace
+n07815424	nutmeg
+n07815588	pepper, peppercorn
+n07815839	black pepper
+n07815956	white pepper
+n07816052	sassafras
+n07816164	basil, sweet basil
+n07816296	bay leaf
+n07816398	borage
+n07816575	hyssop
+n07816726	caraway
+n07816839	chervil
+n07817024	chives
+n07817160	comfrey, healing herb
+n07817315	coriander, Chinese parsley, cilantro
+n07817465	coriander, coriander seed
+n07817599	costmary
+n07817758	fennel, common fennel
+n07817871	fennel, Florence fennel, finocchio
+n07818029	fennel seed
+n07818133	fenugreek, fenugreek seed
+n07818277	garlic, ail
+n07818422	clove, garlic clove
+n07818572	garlic chive
+n07818689	lemon balm
+n07818825	lovage
+n07818995	marjoram, oregano
+n07819166	mint
+n07819303	mustard seed
+n07819480	mustard, table mustard
+n07819682	Chinese mustard
+n07819769	nasturtium
+n07819896	parsley
+n07820036	salad burnet
+n07820145	rosemary
+n07820297	rue
+n07820497	sage
+n07820683	clary sage
+n07820814	savory, savoury
+n07820960	summer savory, summer savoury
+n07821107	winter savory, winter savoury
+n07821260	sweet woodruff, waldmeister
+n07821404	sweet cicely
+n07821610	tarragon, estragon
+n07821758	thyme
+n07821919	turmeric
+n07822053	caper
+n07822197	catsup, ketchup, cetchup, tomato ketchup
+n07822323	cardamom, cardamon, cardamum
+n07822518	cayenne, cayenne pepper, red pepper
+n07822687	chili powder
+n07822845	chili sauce
+n07823105	chutney, Indian relish
+n07823280	steak sauce
+n07823369	taco sauce
+n07823460	salsa
+n07823591	mint sauce
+n07823698	cranberry sauce
+n07823814	curry powder
+n07823951	curry
+n07824191	lamb curry
+n07824268	duck sauce, hoisin sauce
+n07824383	horseradish
+n07824502	marinade
+n07824702	paprika
+n07824863	Spanish paprika
+n07824988	pickle
+n07825194	dill pickle
+n07825399	bread and butter pickle
+n07825496	pickle relish
+n07825597	piccalilli
+n07825717	sweet pickle
+n07825850	applesauce, apple sauce
+n07825972	soy sauce, soy
+n07826091	Tabasco, Tabasco sauce
+n07826250	tomato paste
+n07826340	angelica
+n07826453	angelica
+n07826544	almond extract
+n07826653	anise, aniseed, anise seed
+n07826930	Chinese anise, star anise, star aniseed
+n07827130	juniper berries
+n07827284	saffron
+n07827410	sesame seed, benniseed
+n07827554	caraway seed
+n07827750	poppy seed
+n07827896	dill, dill weed
+n07828041	dill seed
+n07828156	celery seed
+n07828275	lemon extract
+n07828378	monosodium glutamate, MSG
+n07828642	vanilla bean
+n07828987	vinegar, acetum
+n07829248	cider vinegar
+n07829331	wine vinegar
+n07829412	sauce
+n07830493	anchovy sauce
+n07830593	hot sauce
+n07830690	hard sauce
+n07830841	horseradish sauce, sauce Albert
+n07830986	bolognese pasta sauce
+n07831146	carbonara
+n07831267	tomato sauce
+n07831450	tartare sauce, tartar sauce
+n07831663	wine sauce
+n07831821	marchand de vin, mushroom wine sauce
+n07831955	bread sauce
+n07832099	plum sauce
+n07832202	peach sauce
+n07832307	apricot sauce
+n07832416	pesto
+n07832592	ravigote, ravigotte
+n07832741	remoulade sauce
+n07832902	dressing, salad dressing
+n07833333	sauce Louis
+n07833535	bleu cheese dressing, blue cheese dressing
+n07833672	blue cheese dressing, Roquefort dressing
+n07833816	French dressing, vinaigrette, sauce vinaigrette
+n07833951	Lorenzo dressing
+n07834065	anchovy dressing
+n07834160	Italian dressing
+n07834286	half-and-half dressing
+n07834507	mayonnaise, mayo
+n07834618	green mayonnaise, sauce verte
+n07834774	aioli, aioli sauce, garlic sauce
+n07834872	Russian dressing, Russian mayonnaise
+n07835051	salad cream
+n07835173	Thousand Island dressing
+n07835331	barbecue sauce
+n07835457	hollandaise
+n07835547	bearnaise
+n07835701	Bercy, Bercy butter
+n07835823	bordelaise
+n07835921	bourguignon, bourguignon sauce, Burgundy sauce
+n07836077	brown sauce, sauce Espagnole
+n07836269	Espagnole, sauce Espagnole
+n07836456	Chinese brown sauce, brown sauce
+n07836600	blanc
+n07836731	cheese sauce
+n07836838	chocolate sauce, chocolate syrup
+n07837002	hot-fudge sauce, fudge sauce
+n07837110	cocktail sauce, seafood sauce
+n07837234	Colbert, Colbert butter
+n07837362	white sauce, bechamel sauce, bechamel
+n07837545	cream sauce
+n07837630	Mornay sauce
+n07837755	demiglace, demi-glaze
+n07837912	gravy, pan gravy
+n07838073	gravy
+n07838233	spaghetti sauce, pasta sauce
+n07838441	marinara
+n07838551	mole
+n07838659	hunter's sauce, sauce chausseur
+n07838811	mushroom sauce
+n07838905	mustard sauce
+n07839055	Nantua, shrimp sauce
+n07839172	Hungarian sauce, paprika sauce
+n07839312	pepper sauce, Poivrade
+n07839478	roux
+n07839593	Smitane
+n07839730	Soubise, white onion sauce
+n07839864	Lyonnaise sauce, brown onion sauce
+n07840027	veloute
+n07840124	allemande, allemande sauce
+n07840219	caper sauce
+n07840304	poulette
+n07840395	curry sauce
+n07840520	Worcester sauce, Worcestershire, Worcestershire sauce
+n07840672	coconut milk, coconut cream
+n07840804	egg, eggs
+n07841037	egg white, white, albumen, ovalbumin
+n07841345	egg yolk, yolk
+n07841495	boiled egg, coddled egg
+n07841639	hard-boiled egg, hard-cooked egg
+n07841800	Easter egg
+n07841907	Easter egg
+n07842044	chocolate egg
+n07842130	candy egg
+n07842202	poached egg, dropped egg
+n07842308	scrambled eggs
+n07842433	deviled egg, stuffed egg
+n07842605	shirred egg, baked egg, egg en cocotte
+n07842753	omelet, omelette
+n07842972	firm omelet
+n07843117	French omelet
+n07843220	fluffy omelet
+n07843348	western omelet
+n07843464	souffle
+n07843636	fried egg
+n07843775	dairy product
+n07844042	milk
+n07844604	milk
+n07844786	sour milk
+n07844867	soya milk, soybean milk, soymilk
+n07845087	formula
+n07845166	pasteurized milk
+n07845335	cows' milk
+n07845421	yak's milk
+n07845495	goats' milk
+n07845571	acidophilus milk
+n07845702	raw milk
+n07845775	scalded milk
+n07845863	homogenized milk
+n07846014	certified milk
+n07846143	powdered milk, dry milk, dried milk, milk powder
+n07846274	nonfat dry milk
+n07846359	evaporated milk
+n07846471	condensed milk
+n07846557	skim milk, skimmed milk
+n07846688	semi-skimmed milk
+n07846802	whole milk
+n07846938	low-fat milk
+n07847047	buttermilk
+n07847198	cream
+n07847453	clotted cream, Devonshire cream
+n07847585	double creme, heavy whipping cream
+n07847706	half-and-half
+n07847827	heavy cream
+n07847917	light cream, coffee cream, single cream
+n07848093	sour cream, soured cream
+n07848196	whipping cream, light whipping cream
+n07848338	butter
+n07848771	clarified butter, drawn butter
+n07848936	ghee
+n07849026	brown butter, beurre noisette
+n07849186	Meuniere butter, lemon butter
+n07849336	yogurt, yoghurt, yoghourt
+n07849506	blueberry yogurt
+n07849619	raita
+n07849733	whey
+n07849912	curd
+n07850083	curd
+n07850219	clabber
+n07850329	cheese
+n07851054	paring
+n07851298	cream cheese
+n07851443	double cream
+n07851554	mascarpone
+n07851641	triple cream, triple creme
+n07851767	cottage cheese, pot cheese, farm cheese, farmer's cheese
+n07851926	process cheese, processed cheese
+n07852045	bleu, blue cheese
+n07852229	Stilton
+n07852302	Roquefort
+n07852376	gorgonzola
+n07852452	Danish blue
+n07852532	Bavarian blue
+n07852614	Brie
+n07852712	brick cheese
+n07852833	Camembert
+n07852919	cheddar, cheddar cheese, Armerican cheddar, American cheese
+n07853125	rat cheese, store cheese
+n07853232	Cheshire cheese
+n07853345	double Gloucester
+n07853445	Edam
+n07853560	goat cheese, chevre
+n07853648	Gouda, Gouda cheese
+n07853762	grated cheese
+n07853852	hand cheese
+n07853946	Liederkranz
+n07854066	Limburger
+n07854184	mozzarella
+n07854266	Muenster
+n07854348	Parmesan
+n07854455	quark cheese, quark
+n07854614	ricotta
+n07854707	string cheese
+n07854813	Swiss cheese
+n07854982	Emmenthal, Emmental, Emmenthaler, Emmentaler
+n07855105	Gruyere
+n07855188	sapsago
+n07855317	Velveeta
+n07855413	nut butter
+n07855510	peanut butter
+n07855603	marshmallow fluff
+n07855721	onion butter
+n07855812	pimento butter
+n07855907	shrimp butter
+n07856045	lobster butter
+n07856186	yak butter
+n07856270	spread, paste
+n07856756	cheese spread
+n07856895	anchovy butter
+n07856992	fishpaste
+n07857076	garlic butter
+n07857170	miso
+n07857356	wasabi
+n07857598	snail butter
+n07857731	hummus, humus, hommos, hoummos, humous
+n07857959	pate
+n07858114	duck pate
+n07858197	foie gras, pate de foie gras
+n07858336	tapenade
+n07858484	tahini
+n07858595	sweetening, sweetener
+n07858841	aspartame
+n07858978	honey
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+n07859284	sugar, refined sugar
+n07859583	syrup, sirup
+n07859796	sugar syrup
+n07859951	molasses
+n07860103	sorghum, sorghum molasses
+n07860208	treacle, golden syrup
+n07860331	grenadine
+n07860447	maple syrup
+n07860548	corn syrup
+n07860629	miraculous food, manna, manna from heaven
+n07860805	batter
+n07860988	dough
+n07861158	bread dough
+n07861247	pancake batter
+n07861334	fritter batter
+n07861557	coq au vin
+n07861681	chicken provencale
+n07861813	chicken and rice
+n07861983	moo goo gai pan
+n07862095	arroz con pollo
+n07862244	bacon and eggs
+n07862348	barbecued spareribs, spareribs
+n07862461	beef Bourguignonne, boeuf Bourguignonne
+n07862611	beef Wellington, filet de boeuf en croute
+n07862770	bitok
+n07862946	boiled dinner, New England boiled dinner
+n07863107	Boston baked beans
+n07863229	bubble and squeak
+n07863374	pasta
+n07863547	cannelloni
+n07863644	carbonnade flamande, Belgian beef stew
+n07863802	cheese souffle
+n07863935	chicken Marengo
+n07864065	chicken cordon bleu
+n07864198	Maryland chicken
+n07864317	chicken paprika, chicken paprikash
+n07864475	chicken Tetrazzini
+n07864638	Tetrazzini
+n07864756	chicken Kiev
+n07864934	chili, chili con carne
+n07865105	chili dog
+n07865196	chop suey
+n07865484	chow mein
+n07865575	codfish ball, codfish cake
+n07865700	coquille
+n07865788	coquilles Saint-Jacques
+n07866015	croquette
+n07866151	cottage pie
+n07866277	rissole
+n07866409	dolmas, stuffed grape leaves
+n07866571	egg foo yong, egg fu yung
+n07866723	egg roll, spring roll
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+n07867324	fish and chips
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+n07867616	cheese fondue
+n07867751	chocolate fondue
+n07867883	fondue, fondu
+n07868045	beef fondue, boeuf fondu bourguignon
+n07868200	French toast
+n07868340	fried rice, Chinese fried rice
+n07868508	frittata
+n07868684	frog legs
+n07868830	galantine
+n07868955	gefilte fish, fish ball
+n07869111	haggis
+n07869291	ham and eggs
+n07869391	hash
+n07869522	corned beef hash
+n07869611	jambalaya
+n07869775	kabob, kebab, shish kebab
+n07869937	kedgeree
+n07870069	souvlaki, souvlakia
+n07870167	lasagna, lasagne
+n07870313	seafood Newburg
+n07870478	lobster Newburg, lobster a la Newburg
+n07870620	shrimp Newburg
+n07870734	Newburg sauce
+n07870894	lobster thermidor
+n07871065	lutefisk, lutfisk
+n07871234	macaroni and cheese
+n07871335	macedoine
+n07871436	meatball
+n07871588	porcupine ball, porcupines
+n07871720	Swedish meatball
+n07871810	meat loaf, meatloaf
+n07872593	moussaka
+n07872748	osso buco
+n07873057	marrow, bone marrow
+n07873198	pheasant under glass
+n07873348	pigs in blankets
+n07873464	pilaf, pilaff, pilau, pilaw
+n07873679	bulgur pilaf
+n07873807	pizza, pizza pie
+n07874063	sausage pizza
+n07874159	pepperoni pizza
+n07874259	cheese pizza
+n07874343	anchovy pizza
+n07874441	Sicilian pizza
+n07874531	poi
+n07874674	pork and beans
+n07874780	porridge
+n07874995	oatmeal, burgoo
+n07875086	loblolly
+n07875152	potpie
+n07875267	rijsttaffel, rijstaffel, rijstafel
+n07875436	risotto, Italian rice
+n07875560	roulade
+n07875693	fish loaf
+n07875835	salmon loaf
+n07875926	Salisbury steak
+n07876026	sauerbraten
+n07876189	sauerkraut
+n07876281	scallopine, scallopini
+n07876460	veal scallopini
+n07876550	scampi
+n07876651	Scotch egg
+n07876775	Scotch woodcock
+n07876893	scrapple
+n07877187	spaghetti and meatballs
+n07877299	Spanish rice
+n07877675	steak tartare, tartar steak, cannibal mound
+n07877849	pepper steak
+n07877961	steak au poivre, peppered steak, pepper steak
+n07878145	beef Stroganoff
+n07878283	stuffed cabbage
+n07878479	kishke, stuffed derma
+n07878647	stuffed peppers
+n07878785	stuffed tomato, hot stuffed tomato
+n07878926	stuffed tomato, cold stuffed tomato
+n07879072	succotash
+n07879174	sukiyaki
+n07879350	sashimi
+n07879450	sushi
+n07879560	Swiss steak
+n07879659	tamale
+n07879821	tamale pie
+n07879953	tempura
+n07880080	teriyaki
+n07880213	terrine
+n07880325	Welsh rarebit, Welsh rabbit, rarebit
+n07880458	schnitzel, Wiener schnitzel
+n07880751	taco
+n07880880	chicken taco
+n07880968	burrito
+n07881117	beef burrito
+n07881205	quesadilla
+n07881404	tostada
+n07881525	bean tostada
+n07881625	refried beans, frijoles refritos
+n07881800	beverage, drink, drinkable, potable
+n07882420	wish-wash
+n07882497	concoction, mixture, intermixture
+n07882886	mix, premix
+n07883031	filling
+n07883156	lekvar
+n07883251	potion
+n07883384	elixir
+n07883510	elixir of life
+n07883661	philter, philtre, love-potion, love-philter, love-philtre
+n07884567	alcohol, alcoholic drink, alcoholic beverage, intoxicant, inebriant
+n07885705	proof spirit
+n07886057	home brew, homebrew
+n07886176	hooch, hootch
+n07886317	kava, kavakava
+n07886463	aperitif
+n07886572	brew, brewage
+n07886849	beer
+n07887099	draft beer, draught beer
+n07887192	suds
+n07887304	Munich beer, Munchener
+n07887461	bock, bock beer
+n07887634	lager, lager beer
+n07887967	light beer
+n07888058	Oktoberfest, Octoberfest
+n07888229	Pilsner, Pilsener
+n07888378	shebeen
+n07888465	Weissbier, white beer, wheat beer
+n07888816	Weizenbock
+n07888909	malt
+n07889193	wort
+n07889274	malt, malt liquor
+n07889510	ale
+n07889814	bitter
+n07889990	Burton
+n07890068	pale ale
+n07890226	porter, porter's beer
+n07890352	stout
+n07890540	Guinness
+n07890617	kvass
+n07890750	mead
+n07890890	metheglin
+n07890970	hydromel
+n07891095	oenomel
+n07891189	near beer
+n07891309	ginger beer
+n07891433	sake, saki, rice beer
+n07891726	wine, vino
+n07892418	vintage
+n07892512	red wine
+n07892813	white wine
+n07893253	blush wine, pink wine, rose, rose wine
+n07893425	altar wine, sacramental wine
+n07893528	sparkling wine
+n07893642	champagne, bubbly
+n07893792	cold duck
+n07893891	Burgundy, Burgundy wine
+n07894102	Beaujolais
+n07894298	Medoc
+n07894451	Canary wine
+n07894551	Chablis, white Burgundy
+n07894703	Montrachet
+n07894799	Chardonnay, Pinot Chardonnay
+n07894965	Pinot noir
+n07895100	Pinot blanc
+n07895237	Bordeaux, Bordeaux wine
+n07895435	claret, red Bordeaux
+n07895595	Chianti
+n07895710	Cabernet, Cabernet Sauvignon
+n07895839	Merlot
+n07895962	Sauvignon blanc
+n07896060	California wine
+n07896165	Cotes de Provence
+n07896287	dessert wine
+n07896422	Dubonnet
+n07896560	jug wine
+n07896661	macon, maconnais
+n07896765	Moselle
+n07896893	Muscadet
+n07896994	plonk
+n07897116	retsina
+n07897200	Rhine wine, Rhenish, hock
+n07897438	Riesling
+n07897600	liebfraumilch
+n07897750	Rhone wine
+n07897865	Rioja
+n07897975	sack
+n07898117	Saint Emilion
+n07898247	Soave
+n07898333	zinfandel
+n07898443	Sauterne, Sauternes
+n07898617	straw wine
+n07898745	table wine
+n07898895	Tokay
+n07899003	vin ordinaire
+n07899108	vermouth
+n07899292	sweet vermouth, Italian vermouth
+n07899434	dry vermouth, French vermouth
+n07899533	Chenin blanc
+n07899660	Verdicchio
+n07899769	Vouvray
+n07899899	Yquem
+n07899976	generic, generic wine
+n07900225	varietal, varietal wine
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+n07900734	malmsey
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+n07900958	sherry
+n07901355	Marsala
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+n07901587	liquor, spirits, booze, hard drink, hard liquor, John Barleycorn, strong drink
+n07902121	neutral spirits, ethyl alcohol
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+n07902443	eau de vie
+n07902520	moonshine, bootleg, corn liquor
+n07902698	bathtub gin
+n07902799	aquavit, akvavit
+n07902937	arrack, arak
+n07903101	bitters
+n07903208	brandy
+n07903543	applejack
+n07903643	Calvados
+n07903731	Armagnac
+n07903841	Cognac
+n07903962	grappa
+n07904072	kirsch
+n07904293	slivovitz
+n07904395	gin
+n07904637	sloe gin
+n07904760	geneva, Holland gin, Hollands
+n07904865	grog
+n07904934	ouzo
+n07905038	rum
+n07905296	demerara, demerara rum
+n07905386	Jamaica rum
+n07905474	schnapps, schnaps
+n07905618	pulque
+n07905770	mescal
+n07905979	tequila
+n07906111	vodka
+n07906284	whiskey, whisky
+n07906572	blended whiskey, blended whisky
+n07906718	bourbon
+n07906877	corn whiskey, corn whisky, corn
+n07907037	firewater
+n07907161	Irish, Irish whiskey, Irish whisky
+n07907342	poteen
+n07907429	rye, rye whiskey, rye whisky
+n07907548	Scotch, Scotch whiskey, Scotch whisky, malt whiskey, malt whisky, Scotch malt whiskey, Scotch malt whisky
+n07907831	sour mash, sour mash whiskey
+n07907943	liqueur, cordial
+n07908411	absinth, absinthe
+n07908567	amaretto
+n07908647	anisette, anisette de Bordeaux
+n07908812	benedictine
+n07908923	Chartreuse
+n07909129	coffee liqueur
+n07909231	creme de cacao
+n07909362	creme de menthe
+n07909504	creme de fraise
+n07909593	Drambuie
+n07909714	Galliano
+n07909811	orange liqueur
+n07909954	curacao, curacoa
+n07910048	triple sec
+n07910152	Grand Marnier
+n07910245	kummel
+n07910379	maraschino, maraschino liqueur
+n07910538	pastis
+n07910656	Pernod
+n07910799	pousse-cafe
+n07910970	Kahlua
+n07911061	ratafia, ratafee
+n07911249	sambuca
+n07911371	mixed drink
+n07911677	cocktail
+n07912093	Dom Pedro
+n07912211	highball
+n07913180	mixer
+n07913300	bishop
+n07913393	Bloody Mary
+n07913537	Virgin Mary, bloody shame
+n07913644	bullshot
+n07913774	cobbler
+n07913882	collins, Tom Collins
+n07914006	cooler
+n07914128	refresher
+n07914271	smoothie
+n07914413	daiquiri, rum cocktail
+n07914586	strawberry daiquiri
+n07914686	NADA daiquiri
+n07914777	spritzer
+n07914887	flip
+n07914995	gimlet
+n07915094	gin and tonic
+n07915213	grasshopper
+n07915366	Harvey Wallbanger
+n07915491	julep, mint julep
+n07915618	manhattan
+n07915800	Rob Roy
+n07915918	margarita
+n07916041	martini
+n07916183	gin and it
+n07916319	vodka martini
+n07916437	old fashioned
+n07916582	pink lady
+n07917133	Sazerac
+n07917272	screwdriver
+n07917392	sidecar
+n07917507	Scotch and soda
+n07917618	sling
+n07917791	brandy sling
+n07917874	gin sling
+n07917951	rum sling
+n07918028	sour
+n07918193	whiskey sour, whisky sour
+n07918309	stinger
+n07918706	swizzle
+n07918879	hot toddy, toddy
+n07919165	zombie, zombi
+n07919310	fizz
+n07919441	Irish coffee
+n07919572	cafe au lait
+n07919665	cafe noir, demitasse
+n07919787	decaffeinated coffee, decaf
+n07919894	drip coffee
+n07920052	espresso
+n07920222	caffe latte, latte
+n07920349	cappuccino, cappuccino coffee, coffee cappuccino
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+n07920663	instant coffee
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+n07920989	mocha
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+n07921239	Turkish coffee
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+n07921615	hard cider
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+n07921948	sweet cider
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+n07922512	rotgut
+n07922607	slug
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+n07924366	apple juice
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+n07924655	must
+n07924747	grapefruit juice
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+n07925116	pineapple juice
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+n07925708	V-8 juice
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+n07925966	fruit drink, ade
+n07926250	lemonade
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+n07926442	orangeade
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+n07926785	mate
+n07926920	mulled wine
+n07927070	negus
+n07927197	soft drink
+n07927512	pop, soda, soda pop, soda water, tonic
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+n07927836	bitter lemon
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+n07928163	cream soda
+n07928264	egg cream
+n07928367	ginger ale, ginger pop
+n07928488	orange soda
+n07928578	phosphate
+n07928696	Coca Cola, Coke
+n07928790	Pepsi, Pepsi Cola
+n07928887	root beer
+n07928998	sarsaparilla
+n07929172	tonic, tonic water, quinine water
+n07929351	coffee bean, coffee berry, coffee
+n07929519	coffee, java
+n07929940	cafe royale, coffee royal
+n07930062	fruit punch
+n07930205	milk punch
+n07930315	mimosa, buck's fizz
+n07930433	pina colada
+n07930554	punch
+n07930864	cup
+n07931001	champagne cup
+n07931096	claret cup
+n07931280	wassail
+n07931452	planter's punch
+n07931612	White Russian
+n07931733	fish house punch
+n07931870	May wine
+n07932039	eggnog
+n07932323	cassiri
+n07932454	spruce beer
+n07932614	rickey
+n07932762	gin rickey
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+n07933154	tea bag
+n07933274	tea
+n07933530	tea-like drink
+n07933652	cambric tea
+n07933799	cuppa, cupper
+n07933891	herb tea, herbal tea, herbal
+n07934032	tisane
+n07934152	camomile tea
+n07934282	ice tea, iced tea
+n07934373	sun tea
+n07934530	black tea
+n07934678	congou, congo, congou tea, English breakfast tea
+n07934800	Darjeeling
+n07934908	orange pekoe, pekoe
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+n07935152	green tea
+n07935288	hyson
+n07935379	oolong
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+n07935737	bottled water
+n07935878	branch water
+n07936015	spring water
+n07936093	sugar water
+n07936263	drinking water
+n07936459	ice water
+n07936548	soda water, carbonated water, club soda, seltzer, sparkling water
+n07936745	mineral water
+n07936979	seltzer
+n07937069	Vichy water
+n07937344	perishable, spoilable
+n07937461	couscous
+n07937621	ramekin, ramequin
+n07938007	multivitamin, multivitamin pill
+n07938149	vitamin pill
+n07938313	soul food
+n07938594	mold, mould
+n07942152	people
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+n07954211	book, rule book
+n07977870	library
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+n08242223	core, nucleus, core group
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+n08253141	dance
+n08256735	wedding, wedding party
+n08376250	chain, concatenation
+n08385989	power breakfast
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+n08492461	agora
+n08494231	amusement park, funfair, pleasure ground
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+n08496334	apron
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+n08500989	interstellar space
+n08501887	intergalactic space
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+n08517010	bridgehead
+n08517676	bus stop
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+n08519299	detention basin
+n08521623	cemetery, graveyard, burial site, burial ground, burying ground, memorial park, necropolis
+n08523340	trichion, crinion
+n08524735	city, metropolis, urban center
+n08539072	business district, downtown
+n08539276	outskirts
+n08540532	borough
+n08547468	cow pasture
+n08547544	crest
+n08551296	eparchy, exarchate
+n08554440	suburb, suburbia, suburban area
+n08555333	stockbroker belt
+n08555710	crawlspace, crawl space
+n08558770	sheikdom, sheikhdom
+n08558963	residence, abode
+n08559155	domicile, legal residence
+n08560295	dude ranch
+n08569482	farmland, farming area
+n08571275	midfield
+n08571642	firebreak, fireguard
+n08571898	flea market
+n08573674	battlefront, front, front line
+n08573842	garbage heap, junk heap, rubbish heap, scrapheap, trash heap, junk pile, trash pile, refuse heap
+n08578517	benthos, benthic division, benthonic zone
+n08579266	goldfield
+n08579352	grainfield, grain field
+n08580944	half-mast, half-staff
+n08583292	hemline
+n08583455	heronry
+n08583554	hipline
+n08583682	hipline
+n08584914	hole-in-the-wall
+n08586978	junkyard
+n08589670	isoclinic line, isoclinal
+n08596076	littoral, litoral, littoral zone, sands
+n08597579	magnetic pole
+n08598301	grassland
+n08598568	mecca
+n08599174	observer's meridian
+n08599292	prime meridian
+n08611339	nombril
+n08611421	no-parking zone
+n08613733	outdoors, out-of-doors, open air, open
+n08614632	fairground
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+n08619112	periselene, perilune
+n08623676	locus of infection
+n08628141	kasbah, casbah
+n08633683	waterfront
+n08640531	resort, resort hotel, holiday resort
+n08640739	resort area, playground, vacation spot
+n08640962	rough
+n08643267	ashram
+n08644045	harborage, harbourage
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+n08645318	wold
+n08647264	schoolyard
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+n08649711	bedside
+n08651104	sideline, out of bounds
+n08652376	ski resort
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+n08658918	geological horizon
+n08659242	coal seam
+n08659331	coalface
+n08659446	field
+n08659861	oilfield
+n08661878	Temperate Zone
+n08662427	terreplein
+n08663051	three-mile limit
+n08663703	desktop
+n08663860	top
+n08673039	kampong, campong
+n08674344	subtropics, semitropics
+n08676253	barrio
+n08677424	veld, veldt
+n08677801	vertex, peak, apex, acme
+n08678783	waterline, water line, water level
+n08679167	high-water mark
+n08679269	low-water mark
+n08679562	continental divide
+n08685188	zodiac
+n08782627	Aegean island
+n08896327	sultanate
+n09032191	Swiss canton
+n09186592	abyssal zone
+n09189157	aerie, aery, eyrie, eyry
+n09191635	air bubble
+n09193551	alluvial flat, alluvial plain
+n09193705	alp
+n09194227	Alpine glacier, Alpine type of glacier
+n09199101	anthill, formicary
+n09201998	aquifer
+n09203827	archipelago
+n09205509	arete
+n09206896	arroyo
+n09206985	ascent, acclivity, rise, raise, climb, upgrade
+n09208496	asterism
+n09209025	asthenosphere
+n09210862	atoll
+n09213434	bank
+n09213565	bank
+n09214060	bar
+n09214269	barbecue pit
+n09214916	barrier reef
+n09215023	baryon, heavy particle
+n09215437	basin
+n09217230	beach
+n09218315	honeycomb
+n09218494	belay
+n09218641	ben
+n09219233	berm
+n09223487	bladder stone, cystolith
+n09224725	bluff
+n09226869	borrow pit
+n09228055	brae
+n09229709	bubble
+n09230041	burrow, tunnel
+n09230202	butte
+n09231117	caldera
+n09233446	canyon, canon
+n09233603	canyonside
+n09238926	cave
+n09239302	cavern
+n09242389	chasm
+n09245515	cirque, corrie, cwm
+n09246464	cliff, drop, drop-off
+n09247410	cloud
+n09248153	coast
+n09248399	coastland
+n09249034	col, gap
+n09249155	collector
+n09251407	comet
+n09255070	continental glacier
+n09256479	coral reef
+n09257843	cove
+n09259025	crag
+n09259219	crater
+n09260907	cultivated land, farmland, plowland, ploughland, tilled land, tillage, tilth
+n09262690	dale
+n09263912	defile, gorge
+n09264803	delta
+n09265620	descent, declivity, fall, decline, declination, declension, downslope
+n09266604	diapir
+n09267854	divot
+n09268007	divot
+n09269341	down
+n09269472	downhill
+n09269882	draw
+n09270160	drey
+n09270657	drumlin
+n09270735	dune, sand dune
+n09274152	escarpment, scarp
+n09274305	esker
+n09279986	fireball
+n09281252	flare star
+n09282208	floor
+n09283193	fomite, vehicle
+n09283405	foothill
+n09283514	footwall
+n09283767	foreland
+n09283866	foreshore
+n09287415	gauge boson
+n09287968	geological formation, formation
+n09288635	geyser
+n09289331	glacier
+n09289596	glen
+n09290350	gopher hole
+n09290444	gorge
+n09294877	grotto, grot
+n09295210	growler
+n09295946	gulch, flume
+n09300306	gully
+n09300905	hail
+n09302616	highland, upland
+n09303008	hill
+n09303528	hillside
+n09304750	hole, hollow
+n09305031	hollow, holler
+n09305898	hot spring, thermal spring
+n09308572	iceberg, berg
+n09308743	icecap, ice cap
+n09309046	ice field
+n09309168	ice floe, floe
+n09309292	ice mass
+n09310616	inclined fault
+n09315159	ion
+n09319604	isthmus
+n09325824	kidney stone, urinary calculus, nephrolith, renal calculus
+n09326662	knoll, mound, hillock, hummock, hammock
+n09327077	kopje, koppie
+n09327538	Kuiper belt, Edgeworth-Kuiper belt
+n09330378	lake bed, lake bottom
+n09331251	lakefront
+n09332890	lakeside, lakeshore
+n09335693	landfall
+n09335809	landfill
+n09336555	lather
+n09337048	leak
+n09337253	ledge, shelf
+n09338013	lepton
+n09339810	lithosphere, geosphere
+n09344198	lowland
+n09344324	lunar crater
+n09344724	maar
+n09348460	massif
+n09349648	meander
+n09351905	mesa, table
+n09352849	meteorite
+n09353815	microfossil
+n09354511	midstream
+n09357346	molehill
+n09357447	monocline
+n09359803	mountain, mount
+n09361517	mountainside, versant
+n09362316	mouth
+n09362945	mull
+n09366017	natural depression, depression
+n09366317	natural elevation, elevation
+n09375606	nullah
+n09376198	ocean
+n09376526	ocean floor, sea floor, ocean bottom, seabed, sea bottom, Davy Jones's locker, Davy Jones
+n09376786	oceanfront
+n09381242	outcrop, outcropping, rock outcrop
+n09382099	oxbow
+n09384106	pallasite
+n09389867	perforation
+n09391386	photosphere
+n09391644	piedmont
+n09391774	Piedmont glacier, Piedmont type of glacier
+n09392402	pinetum
+n09393524	plage
+n09393605	plain, field, champaign
+n09396465	point
+n09396608	polar glacier
+n09398076	pothole, chuckhole
+n09398677	precipice
+n09399592	promontory, headland, head, foreland
+n09400584	ptyalith
+n09400987	pulsar
+n09402944	quicksand
+n09403086	rabbit burrow, rabbit hole
+n09403211	radiator
+n09403427	rainbow
+n09403734	range, mountain range, range of mountains, chain, mountain chain, chain of mountains
+n09405078	rangeland
+n09405787	ravine
+n09406793	reef
+n09409512	ridge
+n09409752	ridge, ridgeline
+n09410224	rift valley
+n09411189	riparian forest
+n09411295	ripple mark
+n09415584	riverbank, riverside
+n09415671	riverbed, river bottom
+n09416076	rock, stone
+n09416890	roof
+n09421031	saltpan
+n09421799	sandbank
+n09421951	sandbar, sand bar
+n09422190	sandpit
+n09422631	sanitary landfill
+n09425019	sawpit
+n09425344	scablands
+n09428293	seashore, coast, seacoast, sea-coast
+n09428628	seaside, seaboard
+n09429630	seif dune
+n09432283	shell
+n09432990	shiner
+n09433312	shoal
+n09433442	shore
+n09433839	shoreline
+n09435739	sinkhole, sink, swallow hole
+n09436444	ski slope
+n09436708	sky
+n09437454	slope, incline, side
+n09438844	snowcap
+n09438940	snowdrift
+n09439032	snowfield
+n09439213	soapsuds, suds, lather
+n09442595	spit, tongue
+n09443281	spoor
+n09443641	spume
+n09444783	star
+n09445008	steep
+n09445289	steppe
+n09447666	strand
+n09448690	streambed, creek bed
+n09450163	sun, Sun
+n09451237	supernova
+n09452291	swale
+n09452395	swamp, swampland
+n09452760	swell
+n09453008	tableland, plateau
+n09454153	talus, scree
+n09454412	tangle
+n09454744	tar pit
+n09456207	terrace, bench
+n09457979	tidal basin
+n09458269	tideland
+n09459979	tor
+n09460046	tor
+n09461069	Trapezium
+n09462600	troposphere
+n09463226	tundra
+n09464486	twinkler
+n09466678	uphill
+n09467696	urolith
+n09468604	valley, vale
+n09470027	vehicle-borne transmission
+n09470222	vein, mineral vein
+n09472413	volcanic crater, crater
+n09472597	volcano
+n09474010	wadi
+n09474412	wall
+n09474765	warren, rabbit warren
+n09475044	wasp's nest, wasps' nest, hornet's nest, hornets' nest
+n09475179	watercourse
+n09475925	waterside
+n09476123	water table, water level, groundwater level
+n09478210	whinstone, whin
+n09480959	wormcast
+n09481120	xenolith
+n09493983	Circe
+n09495962	gryphon, griffin, griffon
+n09505153	spiritual leader
+n09537660	messiah, christ
+n09556121	Rhea Silvia, Rea Silvia
+n09605110	number one
+n09606009	adventurer, venturer
+n09606527	anomaly, unusual person
+n09607630	appointee, appointment
+n09607782	argonaut
+n09607903	Ashkenazi
+n09608709	benefactor, helper
+n09610255	color-blind person
+n09610405	commoner, common man, common person
+n09611722	conservator
+n09612700	contrarian
+n09613118	contadino
+n09613191	contestant
+n09613690	cosigner, cosignatory
+n09615336	discussant
+n09616573	enologist, oenologist, fermentologist
+n09616922	entertainer
+n09617161	eulogist, panegyrist
+n09617435	ex-gambler
+n09617577	experimenter
+n09617696	experimenter
+n09618760	exponent
+n09618880	ex-president
+n09618957	face
+n09619168	female, female person
+n09619452	finisher
+n09620078	inhabitant, habitant, dweller, denizen, indweller
+n09620794	native, indigen, indigene, aborigine, aboriginal
+n09621232	native
+n09622049	juvenile, juvenile person
+n09622302	lover
+n09624168	male, male person
+n09624559	mediator, go-between, intermediator, intermediary, intercessor
+n09624899	mediatrix
+n09625401	national, subject
+n09626238	peer, equal, match, compeer
+n09627807	prize winner, lottery winner
+n09627906	recipient, receiver
+n09629065	religionist
+n09629246	sensualist
+n09629752	traveler, traveller
+n09631129	unwelcome person, persona non grata
+n09632274	unskilled person
+n09632518	worker
+n09633969	wrongdoer, offender
+n09635534	Black African
+n09635635	Afrikaner, Afrikander, Boer
+n09635973	Aryan
+n09636339	Black, Black person, blackamoor, Negro, Negroid
+n09637339	Black woman
+n09638454	mulatto
+n09638875	White, White person, Caucasian
+n09639382	Circassian
+n09639919	Semite
+n09640327	Chaldean, Chaldaean, Chaldee
+n09640715	Elamite
+n09641002	white man
+n09641578	WASP, white Anglo-Saxon Protestant
+n09643799	gook, slant-eye
+n09644152	Mongol, Mongolian
+n09644657	Tatar, Tartar, Mongol Tatar
+n09648743	Nahuatl
+n09648911	Aztec
+n09649067	Olmec
+n09650729	Biloxi
+n09650839	Blackfoot
+n09650989	Brule
+n09651123	Caddo
+n09651968	Cheyenne
+n09652149	Chickasaw
+n09653144	Cocopa, Cocopah
+n09653438	Comanche
+n09654079	Creek
+n09654518	Delaware
+n09654898	Diegueno
+n09655213	Esselen
+n09655466	Eyeish
+n09656077	Havasupai
+n09657206	Hunkpapa
+n09657748	Iowa, Ioway
+n09658254	Kalapooia, Kalapuya, Calapooya, Calapuya
+n09658398	Kamia
+n09658815	Kekchi
+n09658921	Kichai
+n09659039	Kickapoo
+n09659188	Kiliwa, Kiliwi
+n09660010	Malecite
+n09660240	Maricopa
+n09661873	Mohican, Mahican
+n09662038	Muskhogean, Muskogean
+n09662661	Navaho, Navajo
+n09662951	Nootka
+n09663248	Oglala, Ogalala
+n09663786	Osage
+n09663999	Oneida
+n09664556	Paiute, Piute
+n09664908	Passamaquody
+n09665367	Penobscot
+n09665545	Penutian
+n09666349	Potawatomi
+n09666476	Powhatan
+n09666883	kachina
+n09667358	Salish
+n09668199	Shahaptian, Sahaptin, Sahaptino
+n09668437	Shasta
+n09668562	Shawnee
+n09668988	Sihasapa
+n09669631	Teton, Lakota, Teton Sioux, Teton Dakota
+n09670280	Taracahitian
+n09670521	Tarahumara
+n09670909	Tuscarora
+n09671089	Tutelo
+n09672590	Yana
+n09672725	Yavapai
+n09672840	Yokuts
+n09673091	Yuma
+n09674412	Gadaba
+n09674786	Kolam
+n09675045	Kui
+n09675673	Toda
+n09675799	Tulu
+n09675922	Gujarati, Gujerati
+n09676021	Kashmiri
+n09676247	Punjabi, Panjabi
+n09676884	Slav
+n09677427	Anabaptist
+n09678747	Adventist, Second Adventist
+n09679028	gentile, non-Jew, goy
+n09679170	gentile
+n09679925	Catholic
+n09680908	Old Catholic
+n09681107	Uniat, Uniate, Uniate Christian
+n09681234	Copt
+n09681973	Jewess
+n09683180	Jihadist
+n09683757	Buddhist
+n09683924	Zen Buddhist
+n09684082	Mahayanist
+n09684901	swami
+n09685233	Hare Krishna
+n09685806	Shintoist
+n09686262	Eurafrican
+n09686401	Eurasian
+n09688233	Gael
+n09688804	Frank
+n09689435	Afghan, Afghanistani
+n09689958	Albanian
+n09690083	Algerian
+n09690208	Altaic
+n09690496	Andorran
+n09690621	Angolan
+n09690864	Anguillan
+n09691604	Austrian
+n09691729	Bahamian
+n09691858	Bahraini, Bahreini
+n09692125	Basotho
+n09692915	Herero
+n09693244	Luba, Chiluba
+n09693982	Barbadian
+n09694664	Bolivian
+n09694771	Bornean
+n09695019	Carioca
+n09695132	Tupi
+n09695514	Bruneian
+n09695620	Bulgarian
+n09695979	Byelorussian, Belorussian, White Russian
+n09696456	Cameroonian
+n09696585	Canadian
+n09696763	French Canadian
+n09697401	Central American
+n09697986	Chilean
+n09698644	Congolese
+n09699020	Cypriot, Cypriote, Cyprian
+n09699642	Dane
+n09700125	Djiboutian
+n09700964	Britisher, Briton, Brit
+n09701148	English person
+n09701833	Englishwoman
+n09702134	Anglo-Saxon
+n09702673	Angle
+n09703101	West Saxon
+n09703344	Lombard, Langobard
+n09703485	limey, John Bull
+n09703708	Cantabrigian
+n09703809	Cornishman
+n09703932	Cornishwoman
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+n09704157	Lancastrian
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+n09705784	Eritrean
+n09706029	Finn
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+n09707289	Lithuanian
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+n09708889	Parisienne
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+n09709673	Creole
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+n09710886	Dorian
+n09711132	Athenian
+n09711435	Laconian
+n09712324	Guyanese
+n09712448	Haitian
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+n09712967	Moro
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+n09714120	Icelander
+n09714694	Iraqi, Iraki
+n09715165	Irishman
+n09715303	Irishwoman
+n09715427	Dubliner
+n09716047	Italian
+n09716933	Roman
+n09717233	Sabine
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+n09718811	Jordanian
+n09718936	Korean
+n09719309	Kenyan
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+n09720033	Lapp, Lapplander, Sami, Saami, Same, Saame
+n09720256	Latin American, Latino
+n09720595	Lebanese
+n09720702	Levantine
+n09720842	Liberian
+n09721244	Luxemburger, Luxembourger
+n09721444	Macedonian
+n09722064	Sabahan
+n09722658	Mexican
+n09722817	Chicano
+n09723067	Mexican-American, Mexicano
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+n09724234	Gurkha
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+n09724656	Nicaraguan
+n09724785	Nigerian
+n09725000	Hausa, Haussa
+n09725229	North American
+n09725546	Nova Scotian, bluenose
+n09725653	Omani
+n09725772	Pakistani
+n09725935	Brahui
+n09726621	South American Indian
+n09726811	Carib, Carib Indian
+n09727440	Filipino
+n09727826	Polynesian
+n09728137	Qatari, Katari
+n09728285	Romanian, Rumanian
+n09729062	Muscovite
+n09729156	Georgian
+n09730077	Sarawakian
+n09730204	Scandinavian, Norse, Northman
+n09730824	Senegalese
+n09731343	Slovene
+n09731436	South African
+n09731571	South American
+n09732170	Sudanese
+n09733459	Syrian
+n09733793	Tahitian
+n09734185	Tanzanian
+n09734450	Tibetan
+n09734535	Togolese
+n09734639	Tuareg
+n09735258	Turki
+n09735654	Chuvash
+n09736485	Turkoman, Turkmen, Turcoman
+n09736798	Uzbek, Uzbeg, Uzbak, Usbek, Usbeg
+n09736945	Ugandan
+n09737050	Ukranian
+n09737161	Yakut
+n09737453	Tungus, Evenk
+n09738121	Igbo
+n09738400	American
+n09740724	Anglo-American
+n09741074	Alaska Native, Alaskan Native, Native Alaskan
+n09741331	Arkansan, Arkansawyer
+n09741722	Carolinian
+n09741816	Coloradan
+n09741904	Connecticuter
+n09741999	Delawarean, Delawarian
+n09742101	Floridian
+n09742315	German American
+n09742927	Illinoisan
+n09743487	Mainer, Down Easter
+n09743601	Marylander
+n09743792	Minnesotan, Gopher
+n09744161	Nebraskan, Cornhusker
+n09744346	New Hampshirite, Granite Stater
+n09744462	New Jerseyan, New Jerseyite, Garden Stater
+n09744679	New Yorker
+n09744834	North Carolinian, Tarheel
+n09745229	Oregonian, Beaver
+n09745324	Pennsylvanian, Keystone Stater
+n09745834	Texan
+n09745933	Utahan
+n09746936	Uruguayan
+n09747191	Vietnamese, Annamese
+n09747495	Gambian
+n09748101	East German
+n09748408	Berliner
+n09748648	Prussian
+n09748889	Ghanian
+n09749386	Guinean
+n09750282	Papuan
+n09750641	Walloon
+n09750770	Yemeni
+n09750891	Yugoslav, Jugoslav, Yugoslavian, Jugoslavian
+n09751076	Serbian, Serb
+n09751496	Xhosa
+n09751622	Zairese, Zairean
+n09751895	Zimbabwean
+n09752023	Zulu
+n09752519	Gemini, Twin
+n09753348	Sagittarius, Archer
+n09753792	Pisces, Fish
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+n09754217	abbess, mother superior, prioress
+n09754633	abnegator
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+n09761753	account executive, account representative, registered representative, customer's broker, customer's man
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+n09762385	accuser
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+n09764201	acquirer
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+n09765118	active citizen
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+n09774783	advocate, advocator, proponent, exponent
+n09775907	aeronautical engineer
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+n09776807	aficionado
+n09777870	buck sergeant
+n09778266	agent-in-place
+n09778537	aggravator, annoyance
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+n09779124	agnostic, doubter
+n09779280	agonist
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+n09780395	air attache
+n09780828	air force officer, commander
+n09780984	airhead
+n09781398	air traveler, air traveller
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+n09781650	albino
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+n09782397	alderman
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+n09783776	alienor
+n09783884	aliterate, aliterate person
+n09784043	algebraist
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+n09784564	alliterator
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+n09785891	altar boy
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+n09787534	ambassador, embassador
+n09787765	ambassador
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+n09790047	analphabet, analphabetic
+n09790482	analyst
+n09791014	industry analyst
+n09791419	market strategist
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+n09792555	ancestor, ascendant, ascendent, antecedent, root
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+n09793141	ancient
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+n09793946	angler, troller
+n09794550	animator
+n09794668	animist
+n09795010	annotator
+n09795124	announcer
+n09795334	announcer
+n09796809	anti
+n09796974	anti-American
+n09797742	anti-Semite, Jew-baiter
+n09797873	Anzac
+n09797998	ape-man
+n09798096	aphakic
+n09800469	appellant, plaintiff in error
+n09800964	appointee
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+n09801275	April fool
+n09801533	aspirant, aspirer, hopeful, wannabe, wannabee
+n09802445	appreciator
+n09802641	appropriator
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+n09805475	architect, designer
+n09806944	archivist
+n09807075	archpriest, hierarch, high priest, prelate, primate
+n09808080	Aristotelian, Aristotelean, Peripatetic
+n09808591	armiger
+n09809279	army attache
+n09809538	army engineer, military engineer
+n09809749	army officer
+n09809925	arranger, adapter, transcriber
+n09810166	arrival, arriver, comer
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+n09811712	articulator
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+n09813219	artist's model, sitter
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+n09814488	assemblywoman
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+n09814660	asserter, declarer, affirmer, asseverator, avower
+n09815455	assignee
+n09815790	assistant, helper, help, supporter
+n09816654	assistant professor
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+n09817174	associate
+n09817386	associate professor
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+n09819477	cosmographer, cosmographist
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+n09820263	athlete, jock
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+n09822830	attorney general
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+n09823502	aunt, auntie, aunty
+n09823832	au pair girl
+n09824135	authoritarian, dictator
+n09824609	authority
+n09825096	authorizer, authoriser
+n09825750	automobile mechanic, auto-mechanic, car-mechanic, mechanic, grease monkey
+n09826204	aviator, aeronaut, airman, flier, flyer
+n09826605	aviatrix, airwoman, aviatress
+n09826821	ayah
+n09827246	babu, baboo
+n09827363	baby, babe, sister
+n09828216	baby
+n09828403	baby boomer, boomer
+n09828988	baby farmer
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+n09830629	backpacker, packer
+n09830759	backroom boy, brain truster
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+n09832633	bag lady
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+n09833111	bailiff
+n09833275	bailor
+n09833441	bairn
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+n09833751	balancer
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+n09834378	ball carrier, runner
+n09834699	ballet dancer
+n09834885	ballet master
+n09835017	ballet mistress
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+n09835230	ball hawk
+n09835348	balloonist
+n09835506	ballplayer, baseball player
+n09836160	bullfighter, toreador
+n09836343	banderillero
+n09836519	matador
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+n09838295	bank robber
+n09838370	bankrupt, insolvent
+n09838621	bantamweight
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+n09840217	baron, big businessman, business leader, king, magnate, mogul, power, top executive, tycoon
+n09840435	baron
+n09840520	baron
+n09841188	bartender, barman, barkeep, barkeeper, mixologist
+n09841515	baseball coach, baseball manager
+n09841696	base runner, runner
+n09842047	basketball player, basketeer, cager
+n09842288	basketweaver, basketmaker
+n09842395	Basket Maker
+n09842528	bass, basso
+n09842823	bastard, by-blow, love child, illegitimate child, illegitimate, whoreson
+n09843443	bat boy
+n09843602	bather
+n09843716	batman
+n09843824	baton twirler, twirler
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+n09845401	beard
+n09845849	beatnik, beat
+n09846142	beauty consultant
+n09846469	Bedouin, Beduin
+n09846586	bedwetter, bed wetter, wetter
+n09846755	beekeeper, apiarist, apiculturist
+n09846894	beer drinker, ale drinker
+n09847267	beggarman
+n09847344	beggarwoman
+n09847543	beldam, beldame
+n09848110	theist
+n09848489	believer, truster
+n09849167	bell founder
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+n09850760	berserker, berserk
+n09850974	besieger
+n09851165	best, topper
+n09851575	betrothed
+n09853541	Big Brother
+n09853645	bigot
+n09853881	big shot, big gun, big wheel, big cheese, big deal, big enchilada, big fish, head honcho
+n09854218	big sister
+n09854421	billiard player
+n09854915	biochemist
+n09855433	biographer
+n09856401	bird fancier
+n09856671	birth
+n09856827	birth-control campaigner, birth-control reformer
+n09857007	bisexual, bisexual person
+n09858165	black belt
+n09858299	blackmailer, extortioner, extortionist
+n09858733	Black Muslim
+n09859152	blacksmith
+n09859285	blade
+n09859975	blind date
+n09861287	bluecoat
+n09861599	bluestocking, bas bleu
+n09861863	boatbuilder
+n09861946	boatman, boater, waterman
+n09862183	boatswain, bos'n, bo's'n, bosun, bo'sun
+n09862621	bobby
+n09863031	bodyguard, escort
+n09863339	boffin
+n09863749	Bolshevik, Marxist, red, bolshie, bolshy
+n09863936	Bolshevik, Bolshevist
+n09864632	bombshell
+n09864968	bondman, bondsman
+n09865068	bondwoman, bondswoman, bondmaid
+n09865162	bondwoman, bondswoman, bondmaid
+n09865398	bond servant
+n09865672	book agent
+n09865744	bookbinder
+n09866115	bookkeeper
+n09866354	bookmaker
+n09866559	bookworm
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+n09866817	bootblack, shoeblack
+n09866922	bootlegger, moonshiner
+n09867069	bootmaker, boot maker
+n09867154	borderer
+n09867311	border patrolman
+n09868270	botanist, phytologist, plant scientist
+n09868782	bottom feeder
+n09868899	boulevardier
+n09869317	bounty hunter
+n09869447	bounty hunter
+n09869578	Bourbon
+n09870096	bowler
+n09871095	slugger, slogger
+n09871229	cub, lad, laddie, sonny, sonny boy
+n09871681	Boy Scout
+n09871867	boy scout
+n09871952	boy wonder
+n09872066	bragger, braggart, boaster, blowhard, line-shooter, vaunter
+n09872557	brahman, brahmin
+n09873348	brawler
+n09873473	breadwinner
+n09873769	breaststroker
+n09873899	breeder, stock breeder
+n09874428	brick
+n09874725	bride
+n09874862	bridesmaid, maid of honor
+n09875025	bridge agent
+n09875979	broadcast journalist
+n09876701	Brother
+n09877288	brother-in-law
+n09877587	browser
+n09877750	Brummie, Brummy
+n09877951	buddy, brother, chum, crony, pal, sidekick
+n09878921	bull
+n09879552	bully
+n09880189	bunny, bunny girl
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+n09881265	bursar
+n09881358	busboy, waiter's assistant
+n09881895	business editor
+n09883047	business traveler
+n09883452	buster
+n09883807	busybody, nosy-parker, nosey-parker, quidnunc
+n09885059	buttinsky
+n09885866	cabinetmaker, furniture maker
+n09886403	caddie, golf caddie
+n09886540	cadet, plebe
+n09888635	caller, caller-out
+n09889065	call girl
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+n09889691	campaigner, candidate, nominee
+n09889941	camper
+n09890192	camp follower
+n09890749	candidate, prospect
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+n09892262	capitalist
+n09892513	captain, headwaiter, maitre d'hotel, maitre d'
+n09892693	captain, senior pilot
+n09893191	captain
+n09893344	captain, chieftain
+n09893502	captive
+n09893600	captive
+n09894143	cardinal
+n09894445	cardiologist, heart specialist, heart surgeon
+n09894654	card player
+n09894909	cardsharp, card sharp, cardsharper, card sharper, sharper, sharpie, sharpy, card shark
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+n09895480	career man
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+n09896401	caroler, caroller
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+n09896826	carper, niggler
+n09898020	Cartesian
+n09899289	cashier
+n09899671	casualty, injured party
+n09899782	casualty
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+n09901337	catechist
+n09901502	catechumen, neophyte
+n09901642	caterer
+n09901786	Catholicos
+n09901921	cat fancier
+n09902128	Cavalier, Royalist
+n09902353	cavalryman, trooper
+n09902731	caveman, cave man, cave dweller, troglodyte
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+n09902954	celebrant, celebrator, celebrater
+n09903153	celebrity, famous person
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+n09903639	censor
+n09903936	censor
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+n09904837	centrist, middle of the roader, moderate, moderationist
+n09905050	centurion
+n09905185	certified public accountant, CPA
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+n09906293	chambermaid, fille de chambre
+n09906449	chameleon
+n09906704	champion, champ, title-holder
+n09907804	chandler
+n09908769	prison chaplain
+n09909660	charcoal burner
+n09909929	charge d'affaires
+n09910222	charioteer
+n09910374	charmer, beguiler
+n09910556	chartered accountant
+n09910840	chartist, technical analyst
+n09911226	charwoman, char, cleaning woman, cleaning lady, woman
+n09912431	male chauvinist, sexist
+n09912681	cheapskate, tightwad
+n09912907	Chechen
+n09912995	checker
+n09913329	cheerer
+n09913455	cheerleader
+n09913593	cheerleader
+n09915434	Cheops, Khufu
+n09915651	chess master
+n09916348	chief executive officer, CEO, chief operating officer
+n09917214	chief of staff
+n09917345	chief petty officer
+n09917481	Chief Secretary
+n09917593	child, kid, youngster, minor, shaver, nipper, small fry, tiddler, tike, tyke, fry, nestling
+n09918248	child, kid
+n09918554	child, baby
+n09918867	child prodigy, infant prodigy, wonder child
+n09919061	chimneysweeper, chimneysweep, sweep
+n09919200	chiropractor
+n09919451	chit
+n09919899	choker
+n09920106	choragus
+n09920283	choreographer
+n09920901	chorus girl, showgirl, chorine
+n09921034	chosen
+n09923003	cicerone
+n09923186	cigar smoker
+n09923418	cipher, cypher, nobody, nonentity
+n09923561	circus acrobat
+n09923673	citizen
+n09923996	city editor
+n09924106	city father
+n09924195	city man
+n09924313	city slicker, city boy
+n09924437	civic leader, civil leader
+n09924996	civil rights leader, civil rights worker, civil rights activist
+n09927089	cleaner
+n09927451	clergyman, reverend, man of the cloth
+n09928136	cleric, churchman, divine, ecclesiastic
+n09928451	clerk
+n09928845	clever Dick, clever clogs
+n09929202	climatologist
+n09929298	climber
+n09929577	clinician
+n09930257	closer, finisher
+n09930628	closet queen
+n09930876	clown, buffoon, goof, goofball, merry andrew
+n09931165	clown, buffoon
+n09931418	coach, private instructor, tutor
+n09931640	coach, manager, handler
+n09932098	pitching coach
+n09932336	coachman
+n09932508	coal miner, collier, pitman
+n09932788	coastguardsman
+n09933020	cobber
+n09933098	cobbler, shoemaker
+n09933842	codger, old codger
+n09933972	co-beneficiary
+n09934337	cog
+n09934488	cognitive neuroscientist
+n09934774	coiffeur
+n09935107	coiner
+n09935434	collaborator, cooperator, partner, pardner
+n09936825	colleen
+n09936892	college student, university student
+n09937056	collegian, college man, college boy
+n09937688	colonial
+n09937802	colonialist
+n09937903	colonizer, coloniser
+n09938080	coloratura, coloratura soprano
+n09938449	color guard
+n09938991	colossus, behemoth, giant, heavyweight, titan
+n09940725	comedian
+n09940818	comedienne
+n09941089	comer
+n09941571	commander
+n09941787	commander in chief, generalissimo
+n09941964	commanding officer, commandant, commander
+n09942697	commissar, political commissar
+n09942970	commissioned officer
+n09943239	commissioned military officer
+n09943811	commissioner
+n09944022	commissioner
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+n09945319	communist, commie
+n09945603	Communist
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+n09946814	compere
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+n09950457	compulsive
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+n09951070	computer scientist
+n09951274	computer user
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+n09952163	conciliator, make-peace, pacifier, peacemaker, reconciler
+n09953052	conductor
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+n09954639	confidant, intimate
+n09955406	Confucian, Confucianist
+n09955944	rep
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+n09958133	Nonconformist, chapelgoer
+n09958292	Anglican
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+n09958569	consigner, consignor
+n09959142	constable
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+n09961331	contributor
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+n09967967	co-star
+n09968259	costumier, costumer, costume designer
+n09968652	cotter, cottier
+n09968741	cotter, cottar
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+n09970088	counterterrorist
+n09970192	counterspy, mole
+n09970402	countess
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+n09971273	countrywoman
+n09971385	county agent, agricultural agent, extension agent
+n09971839	courtier
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+n09972458	cover girl, pin-up, lovely
+n09972587	cow
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+n09975425	craftsman, crafter
+n09976024	crapshooter
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+n09976429	creature, wight
+n09976728	creditor
+n09976917	creep, weirdo, weirdie, weirdy, spook
+n09978442	criminologist
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+n09979913	Croesus
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+n09981092	crown prince
+n09981278	crown princess
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+n09981939	Cub Scout
+n09982152	cuckold
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+n09983572	curate, minister of religion, minister, parson, pastor, rector
+n09983889	curator, conservator
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+n09985978	cyborg, bionic man, bionic woman
+n09986450	cymbalist
+n09986700	Cynic
+n09986904	cytogeneticist
+n09987045	cytologist
+n09987161	czar
+n09987239	czar, tsar, tzar
+n09988063	dad, dada, daddy, pa, papa, pappa, pop
+n09988311	dairyman
+n09988493	Dalai Lama, Grand Lama
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+n09989502	dancer, professional dancer, terpsichorean
+n09990415	dancer, social dancer
+n09990690	clog dancer
+n09990777	dancing-master, dance master
+n09991740	dark horse
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+n09992538	date, escort
+n09992837	daughter, girl
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+n09993651	day boarder
+n09994400	day laborer, day labourer
+n09994673	deacon, Protestant deacon
+n09994808	deaconess
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+n09996039	dropout
+n09996304	deadhead
+n09996481	deaf person
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+n09998788	deckhand, roustabout
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+n10000787	delegate
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+n10001764	demigod, superman, Ubermensch
+n10002257	demographer, demographist, population scientist
+n10002760	demonstrator, protester
+n10003476	den mother
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+n10005006	depositor
+n10005934	deputy
+n10006177	dermatologist, skin doctor
+n10006748	descender
+n10007684	designated hitter
+n10007809	designer, intriguer
+n10007995	desk clerk, hotel desk clerk, hotel clerk
+n10008123	desk officer
+n10008254	desk sergeant, deskman, station keeper
+n10009162	detainee, political detainee
+n10009276	detective, investigator, tec, police detective
+n10009484	detective
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+n10010062	developer
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+n10010632	devisee
+n10010767	devisor
+n10010864	devourer
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+n10012484	dietician, dietitian, nutritionist
+n10013811	diocesan
+n10015215	director, theater director, theatre director
+n10015485	director
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+n10017272	disk jockey, disc jockey, dj
+n10017422	dispatcher
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+n10018861	distributor, distributer
+n10019072	district attorney, DA
+n10019187	district manager
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+n10020366	divorcee, grass widow
+n10020533	ex-wife, ex
+n10020670	divorce lawyer
+n10020807	docent
+n10020890	doctor, doc, physician, MD, Dr., medico
+n10022908	dodo, fogy, fogey, fossil
+n10023264	doge
+n10023506	dog in the manger
+n10023656	dogmatist, doctrinaire
+n10024025	dolichocephalic
+n10024362	domestic partner, significant other, spousal equivalent, spouse equivalent
+n10024937	Dominican
+n10025060	dominus, dominie, domine, dominee
+n10025295	don, father
+n10025391	Donatist
+n10025635	donna
+n10026976	dosser, street person
+n10027246	double, image, look-alike
+n10027590	double-crosser, double-dealer, two-timer, betrayer, traitor
+n10028402	down-and-out
+n10028541	doyenne
+n10029068	draftsman, drawer
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+n10033572	dressmaker's model
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+n10033888	dribbler
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+n10034614	drinker
+n10035952	drug addict, junkie, junky
+n10036266	drug user, substance abuser, user
+n10036444	Druid
+n10036692	drum majorette, majorette
+n10036929	drummer
+n10037080	drunk
+n10037385	drunkard, drunk, rummy, sot, inebriate, wino
+n10037588	Druze, Druse
+n10037922	dry, prohibitionist
+n10038119	dry nurse
+n10038409	duchess
+n10038620	duke
+n10039271	duffer
+n10039946	dunker
+n10040240	Dutch uncle
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+n10040945	eager beaver, busy bee, live wire, sharpie, sharpy
+n10041373	earl
+n10041887	earner, wage earner
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+n10043024	eclectic, eclecticist
+n10043491	econometrician, econometrist
+n10043643	economist, economic expert
+n10044682	ectomorph
+n10044879	editor, editor in chief
+n10047199	egocentric, egoist
+n10047459	egotist, egoist, swellhead
+n10048117	ejaculator
+n10048367	elder
+n10048612	elder statesman
+n10048836	elected official
+n10049363	electrician, lineman, linesman
+n10050043	elegist
+n10050880	elocutionist
+n10051026	emancipator, manumitter
+n10051761	embryologist
+n10051861	emeritus
+n10051975	emigrant, emigre, emigree, outgoer
+n10052694	emissary, envoy
+n10053439	empress
+n10053808	employee
+n10054657	employer
+n10055297	enchantress, witch
+n10055410	enchantress, temptress, siren, Delilah, femme fatale
+n10055566	encyclopedist, encyclopaedist
+n10055730	endomorph
+n10055847	enemy, foe, foeman, opposition
+n10056103	energizer, energiser, vitalizer, vitaliser, animator
+n10056611	end man
+n10056719	end man, corner man
+n10057271	endorser, indorser
+n10058411	enjoyer
+n10058962	enlisted woman
+n10059067	enophile, oenophile
+n10060075	entrant
+n10060175	entrant
+n10060352	entrepreneur, enterpriser
+n10061043	envoy, envoy extraordinary, minister plenipotentiary
+n10061195	enzymologist
+n10061431	eparch
+n10061882	epidemiologist
+n10062042	epigone, epigon
+n10062176	epileptic
+n10062275	Episcopalian
+n10062492	equerry
+n10062594	equerry
+n10062716	erotic
+n10062905	escapee
+n10062996	escapist, dreamer, wishful thinker
+n10063635	Eskimo, Esquimau, Inuit
+n10063919	espionage agent
+n10064831	esthetician, aesthetician
+n10064977	etcher
+n10065758	ethnologist
+n10066206	Etonian
+n10066314	etymologist
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+n10067305	Evangelist
+n10067600	event planner
+n10067968	examiner, inspector
+n10068234	examiner, tester, quizzer
+n10068425	exarch
+n10069296	executant
+n10069981	executive secretary
+n10070108	executive vice president
+n10070377	executrix
+n10070449	exegete
+n10070563	exhibitor, exhibitioner, shower
+n10070711	exhibitionist, show-off
+n10071332	exile, expatriate, expat
+n10071557	existentialist, existentialist philosopher, existential philosopher
+n10072054	exorcist, exorciser
+n10074249	ex-spouse
+n10074578	extern, medical extern
+n10074735	extremist
+n10074841	extrovert, extravert
+n10075299	eyewitness
+n10075693	facilitator
+n10076224	fairy godmother
+n10076483	falangist, phalangist
+n10076604	falconer, hawker
+n10076957	falsifier
+n10077106	familiar
+n10077593	fan, buff, devotee, lover
+n10077879	fanatic, fiend
+n10078131	fancier, enthusiast
+n10078719	farm boy
+n10078806	farmer, husbandman, granger, sodbuster
+n10079399	farmhand, fieldhand, field hand, farm worker
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+n10080117	fascista
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+n10080869	father, male parent, begetter
+n10081204	Father, Padre
+n10081842	father-figure
+n10082043	father-in-law
+n10082299	Fauntleroy, Little Lord Fauntleroy
+n10082423	Fauve, fauvist
+n10082562	favorite son
+n10082687	featherweight
+n10082997	federalist
+n10083677	fellow traveler, fellow traveller
+n10083823	female aristocrat
+n10084043	female offspring
+n10084295	female child, girl, little girl
+n10085101	fence
+n10085869	fiance, groom-to-be
+n10086383	fielder, fieldsman
+n10086744	field judge
+n10087434	fighter pilot
+n10087736	filer
+n10088200	film director, director
+n10090745	finder
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+n10091564	fire-eater, hothead
+n10091651	fireman, firefighter, fire fighter, fire-eater
+n10091861	fire marshall
+n10091997	fire walker
+n10092488	first baseman, first sacker
+n10092643	firstborn, eldest
+n10092794	first lady
+n10092978	first lieutenant, 1st lieutenant
+n10093167	first offender
+n10093475	first sergeant, sergeant first class
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+n10094320	flagellant
+n10094584	flag officer
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+n10095265	flanker back, flanker
+n10095420	flapper
+n10095769	flatmate
+n10095869	flatterer, adulator
+n10096126	flibbertigibbet, foolish woman
+n10096508	flight surgeon
+n10097262	floorwalker, shopwalker
+n10097477	flop, dud, washout
+n10097590	Florentine
+n10097842	flower girl
+n10097995	flower girl
+n10098245	flutist, flautist, flute player
+n10098388	fly-by-night
+n10098517	flyweight
+n10098624	flyweight
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+n10098862	folk dancer
+n10099002	folk poet
+n10099375	follower
+n10101308	football hero
+n10101634	football player, footballer
+n10101981	footman
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+n10103155	foremother
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+n10103921	foreigner, outsider
+n10104064	boss
+n10104487	foreman
+n10104756	forester, tree farmer, arboriculturist
+n10104888	forewoman
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+n10105733	forward
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+n10106387	foster-father, foster father
+n10106509	foster-mother, foster mother
+n10106995	foster-sister, foster sister
+n10107173	foster-son, foster son
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+n10108018	foundress
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+n10108464	framer
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+n10109826	free agent
+n10110093	freedom rider
+n10110731	free-liver
+n10110893	freeloader
+n10111358	free trader
+n10111779	Freudian
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+n10112129	monk, monastic
+n10113249	frontierswoman
+n10113583	front man, front, figurehead, nominal head, straw man, strawman
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+n10114550	fucker
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+n10115430	fullback
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+n10116478	fundraiser
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+n10117267	gagman, gagster, gagwriter
+n10117415	gagman, standup comedian
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+n10117851	gal
+n10118301	galoot
+n10118743	gambist
+n10118844	gambler
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+n10120330	garbage man, garbageman, garbage collector, garbage carter, garbage hauler, refuse collector, dustman
+n10120671	gardener
+n10121026	garment cutter
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+n10121714	gasman
+n10121800	gastroenterologist
+n10122300	gatherer
+n10122531	gawker
+n10123122	gendarme
+n10123844	general, full general
+n10126177	generator, source, author
+n10126424	geneticist
+n10126708	genitor
+n10127186	gent
+n10127689	geologist
+n10128519	geophysicist
+n10128748	ghostwriter, ghost
+n10129338	Gibson girl
+n10129825	girl, miss, missy, young lady, young woman, fille
+n10130686	girlfriend, girl, lady friend
+n10130877	girlfriend
+n10131151	girl wonder
+n10131268	Girondist, Girondin
+n10131590	gitano
+n10131815	gladiator
+n10132035	glassblower
+n10132502	gleaner
+n10134178	goat herder, goatherd
+n10134396	godchild
+n10134760	godfather
+n10134982	godparent
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+n10135297	goffer, gopher
+n10136615	goldsmith, goldworker, gold-worker
+n10136959	golfer, golf player, linksman
+n10137825	gondolier, gondoliere
+n10138369	good guy
+n10138472	good old boy, good ole boy, good ol' boy
+n10139077	good Samaritan
+n10139651	gossip columnist
+n10140051	gouger
+n10140597	governor general
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+n10140783	grader
+n10140929	graduate nurse, trained nurse
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+n10141732	granddaughter
+n10142166	grande dame
+n10142391	grandfather, gramps, granddad, grandad, granddaddy, grandpa
+n10142537	Grand Inquisitor
+n10142747	grandma, grandmother, granny, grannie, gran, nan, nanna
+n10142946	grandmaster
+n10143172	grandparent
+n10143595	grantee
+n10143725	granter
+n10144338	grass widower, divorced man
+n10145239	great-aunt, grandaunt
+n10145340	great grandchild
+n10145480	great granddaughter
+n10145590	great grandmother
+n10145774	great grandparent
+n10145902	great grandson
+n10146002	great-nephew, grandnephew
+n10146104	great-niece, grandniece
+n10146416	Green Beret
+n10146816	grenadier, grenade thrower
+n10146927	greeter, saluter, welcomer
+n10147121	gringo
+n10147262	grinner
+n10147710	grocer
+n10147935	groom, bridegroom
+n10148035	groom, bridegroom
+n10148305	grouch, grump, crank, churl, crosspatch
+n10148825	group captain
+n10149436	grunter
+n10149867	prison guard, jailer, jailor, gaoler, screw, turnkey
+n10150071	guard
+n10150794	guesser
+n10150940	guest, invitee
+n10151133	guest
+n10151261	guest of honor
+n10151367	guest worker, guestworker
+n10151570	guide
+n10151760	guitarist, guitar player
+n10152306	gunnery sergeant
+n10152616	guru
+n10152763	guru
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+n10153414	guy, cat, hombre, bozo
+n10153594	gymnast
+n10153865	gym rat
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+n10154186	Gypsy, Gipsy, Romany, Rommany, Romani, Roma, Bohemian
+n10154601	hack, drudge, hacker
+n10155222	hacker, cyber-terrorist, cyberpunk
+n10155600	haggler
+n10155849	hairdresser, hairstylist, stylist, styler
+n10156629	hakim, hakeem
+n10156831	Hakka
+n10157016	halberdier
+n10157128	halfback
+n10157271	half blood
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+n10159045	animal trainer, handler
+n10159289	handyman, jack of all trades, odd-job man
+n10159533	hang glider
+n10160188	hardliner
+n10160280	harlequin
+n10160412	harmonizer, harmoniser
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+n10162194	hater
+n10162354	hatmaker, hatter, milliner, modiste
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+n10164233	headmaster, schoolmaster, master
+n10164492	head nurse
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+n10166189	heartbreaker
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+n10167152	heavyweight
+n10167361	heavy
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+n10168012	hedger, equivocator, tergiversator
+n10168183	hedonist, pagan, pleasure seeker
+n10168584	heir, inheritor, heritor
+n10168837	heir apparent
+n10169147	heiress, inheritress, inheritrix
+n10169241	heir presumptive
+n10169419	hellion, heller, devil
+n10169796	helmsman, steersman, steerer
+n10170060	hire
+n10170681	hematologist, haematologist
+n10170866	hemiplegic
+n10171219	herald, trumpeter
+n10171456	herbalist, herb doctor
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+n10173410	heroine
+n10173579	heroin addict
+n10173665	hero worshiper, hero worshipper
+n10173771	Herr
+n10174253	highbinder
+n10174330	highbrow
+n10174445	high commissioner
+n10174589	highflier, highflyer
+n10174695	Highlander, Scottish Highlander, Highland Scot
+n10174971	high-muck-a-muck, pooh-bah
+n10175248	high priest
+n10175725	highjacker, hijacker
+n10176913	hireling, pensionary
+n10177150	historian, historiographer
+n10178077	hitchhiker
+n10178216	hitter, striker
+n10179069	hobbyist
+n10180580	holdout
+n10180791	holdover, hangover
+n10180923	holdup man, stickup man
+n10181445	homeboy
+n10181547	homeboy
+n10181799	home buyer
+n10181878	homegirl
+n10182190	homeless, homeless person
+n10182402	homeopath, homoeopath
+n10183347	honest woman
+n10183931	honor guard, guard of honor
+n10184505	hooker
+n10185148	hoper
+n10185483	hornist
+n10185793	horseman, equestrian, horseback rider
+n10186068	horse trader
+n10186143	horsewoman
+n10186216	horse wrangler, wrangler
+n10186350	horticulturist, plantsman
+n10186686	hospital chaplain
+n10186774	host, innkeeper, boniface
+n10187130	host
+n10187491	hostess
+n10187990	hotelier, hotelkeeper, hotel manager, hotelman, hosteller
+n10188715	housekeeper
+n10188856	housemaster
+n10188957	housemate
+n10189278	house physician, resident, resident physician
+n10189597	house sitter
+n10190122	housing commissioner
+n10190516	huckster, cheap-jack
+n10191001	hugger
+n10191388	humanist, humanitarian
+n10191613	humanitarian, do-gooder, improver
+n10192839	hunk
+n10193650	huntress
+n10194231	ex-husband, ex
+n10194775	hydrologist
+n10195056	hyperope
+n10195155	hypertensive
+n10195261	hypnotist, hypnotizer, hypnotiser, mesmerist, mesmerizer
+n10195593	hypocrite, dissembler, dissimulator, phony, phoney, pretender
+n10196404	iceman
+n10196725	iconoclast
+n10197392	ideologist, ideologue
+n10198437	idol, matinee idol
+n10198832	idolizer, idoliser
+n10199251	imam, imaum
+n10200246	imperialist
+n10200781	important person, influential person, personage
+n10202225	inamorato
+n10202624	incumbent, officeholder
+n10202763	incurable
+n10203949	inductee
+n10204177	industrialist
+n10204833	infanticide
+n10205231	inferior
+n10205344	infernal
+n10205457	infielder
+n10205714	infiltrator
+n10206173	informer, betrayer, rat, squealer, blabber
+n10206506	ingenue
+n10206629	ingenue
+n10207077	polymath
+n10207169	in-law, relative-in-law
+n10208189	inquiry agent
+n10208847	inspector
+n10208950	inspector general
+n10209082	instigator, initiator
+n10209731	insurance broker, insurance agent, general agent, underwriter
+n10210137	insurgent, insurrectionist, freedom fighter, rebel
+n10210512	intelligence analyst
+n10210648	interior designer, designer, interior decorator, house decorator, room decorator, decorator
+n10210911	interlocutor, conversational partner
+n10211036	interlocutor, middleman
+n10211666	International Grandmaster
+n10211830	internationalist
+n10212231	internist
+n10212501	interpreter, translator
+n10212780	interpreter
+n10213034	intervenor
+n10213429	introvert
+n10214062	invader, encroacher
+n10214390	invalidator, voider, nullifier
+n10215623	investigator
+n10216106	investor
+n10216403	invigilator
+n10217208	irreligionist
+n10218043	Ivy Leaguer
+n10218164	Jack of all trades
+n10218292	Jacksonian
+n10219240	Jane Doe
+n10219453	janissary
+n10219879	Jat
+n10220080	Javanese, Javan
+n10220924	Jekyll and Hyde
+n10221312	jester, fool, motley fool
+n10221520	Jesuit
+n10222170	jezebel
+n10222259	jilt
+n10222497	jobber, middleman, wholesaler
+n10222716	job candidate
+n10223069	Job's comforter
+n10223177	jockey
+n10223606	John Doe
+n10224578	journalist
+n10225219	judge, justice, jurist
+n10225931	judge advocate
+n10226413	juggler
+n10227166	Jungian
+n10227266	junior
+n10227393	junior
+n10227490	Junior, Jr, Jnr
+n10227698	junior lightweight
+n10227793	junior middleweight
+n10227985	jurist, legal expert
+n10228278	juror, juryman, jurywoman
+n10228468	justice of the peace
+n10228592	justiciar, justiciary
+n10228712	kachina
+n10229883	keyboardist
+n10230216	Khedive
+n10233248	kingmaker
+n10235024	king, queen, world-beater
+n10235269	King's Counsel
+n10235385	Counsel to the Crown
+n10236304	kin, kinsperson, family
+n10236521	enate, matrikin, matrilineal kin, matrisib, matrilineal sib
+n10236842	kink
+n10237069	kinswoman
+n10237196	kisser, osculator
+n10237464	kitchen help
+n10237556	kitchen police, KP
+n10237676	Klansman, Ku Kluxer, Kluxer
+n10237799	kleptomaniac
+n10238272	kneeler
+n10238375	knight
+n10239928	knocker
+n10240082	knower, apprehender
+n10240235	know-it-all, know-all
+n10240417	kolkhoznik
+n10240821	Kshatriya
+n10241024	labor coach, birthing coach, doula, monitrice
+n10241300	laborer, manual laborer, labourer, jack
+n10242328	Labourite
+n10243137	lady
+n10243273	lady-in-waiting
+n10243483	lady's maid
+n10243664	lama
+n10243872	lamb, dear
+n10244108	lame duck
+n10244359	lamplighter
+n10244913	land agent
+n10245029	landgrave
+n10245341	landlubber, lubber, landsman
+n10245507	landlubber, landsman, landman
+n10245639	landowner, landholder, property owner
+n10245863	landscape architect, landscape gardener, landscaper, landscapist
+n10246317	langlaufer
+n10246395	languisher
+n10246703	lapidary, lapidarist
+n10247358	lass, lassie, young girl, jeune fille
+n10247880	Latin
+n10248008	Latin
+n10248198	latitudinarian
+n10248377	Jehovah's Witness
+n10249191	law agent
+n10249270	lawgiver, lawmaker
+n10249459	lawman, law officer, peace officer
+n10249869	law student
+n10249950	lawyer, attorney
+n10250712	lay reader
+n10251329	lazybones
+n10251612	leaker
+n10252075	leaseholder, lessee
+n10252222	lector, lecturer, reader
+n10252354	lector, reader
+n10252547	lecturer
+n10253122	left-hander, lefty, southpaw
+n10253296	legal representative
+n10253479	legate, official emissary
+n10253611	legatee
+n10253703	legionnaire, legionary
+n10255459	letterman
+n10257221	liberator
+n10258602	licenser
+n10258786	licentiate
+n10259348	lieutenant
+n10259780	lieutenant colonel, light colonel
+n10259997	lieutenant commander
+n10260473	lieutenant junior grade, lieutenant JG
+n10260706	life
+n10260800	lifeguard, lifesaver
+n10261211	life tenant
+n10261511	light flyweight
+n10261624	light heavyweight, cruiserweight
+n10261862	light heavyweight
+n10262343	light-o'-love, light-of-love
+n10262445	lightweight
+n10262561	lightweight
+n10262655	lightweight
+n10262880	lilliputian
+n10263146	limnologist
+n10263411	lineman
+n10263790	line officer
+n10265281	lion-hunter
+n10265801	lisper
+n10265891	lister
+n10266016	literary critic
+n10266328	literate, literate person
+n10266848	litigant, litigator
+n10267166	litterer, litterbug, litter lout
+n10267311	little brother
+n10267865	little sister
+n10268629	lobbyist
+n10269199	locksmith
+n10269289	locum tenens, locum
+n10271677	Lord, noble, nobleman
+n10272782	loser
+n10272913	loser, also-ran
+n10273064	failure, loser, nonstarter, unsuccessful person
+n10274173	Lothario
+n10274318	loudmouth, blusterer
+n10274815	lowerclassman, underclassman
+n10275249	Lowlander, Scottish Lowlander, Lowland Scot
+n10275395	loyalist, stalwart
+n10275848	Luddite
+n10276045	lumberman, lumberjack, logger, feller, faller
+n10276477	lumper
+n10276942	bedlamite
+n10277027	pyromaniac
+n10277638	lutist, lutanist, lutenist
+n10277815	Lutheran
+n10277912	lyricist, lyrist
+n10278456	macebearer, mace, macer
+n10279018	machinist, mechanic, shop mechanic
+n10279778	madame
+n10280034	maenad
+n10280130	maestro, master
+n10280598	magdalen
+n10280674	magician, prestidigitator, conjurer, conjuror, illusionist
+n10281546	magus
+n10281770	maharani, maharanee
+n10281896	mahatma
+n10282482	maid, maiden
+n10282672	maid, maidservant, housemaid, amah
+n10283170	major
+n10283366	major
+n10283546	major-domo, seneschal
+n10284064	maker, shaper
+n10284871	malahini
+n10284965	malcontent
+n10286282	malik
+n10286539	malingerer, skulker, shammer
+n10286749	Malthusian
+n10288964	adonis
+n10289039	man
+n10289176	man
+n10289462	manageress
+n10289766	mandarin
+n10290422	maneuverer, manoeuvrer
+n10290541	maniac
+n10290813	Manichaean, Manichean, Manichee
+n10290919	manicurist
+n10291110	manipulator
+n10291469	man-at-arms
+n10291822	man of action, man of deeds
+n10291942	man of letters
+n10292316	manufacturer, producer
+n10293332	marcher, parader
+n10293590	marchioness, marquise
+n10293861	margrave
+n10294020	margrave
+n10294139	Marine, devil dog, leatherneck, shipboard soldier
+n10295371	marquess
+n10295479	marquis, marquess
+n10296176	marshal, marshall
+n10296444	martinet, disciplinarian, moralist
+n10297234	mascot
+n10297367	masochist
+n10297531	mason, stonemason
+n10297841	masquerader, masker, masquer
+n10298202	masseur
+n10298271	masseuse
+n10298647	master
+n10298912	master, captain, sea captain, skipper
+n10299125	master-at-arms
+n10299250	master of ceremonies, emcee, host
+n10299700	masturbator, onanist
+n10299875	matchmaker, matcher, marriage broker
+n10300041	mate, first mate
+n10300154	mate
+n10300303	mate
+n10300500	mater
+n10300654	material
+n10300829	materialist
+n10302576	matriarch, materfamilias
+n10302700	matriarch
+n10302905	matriculate
+n10303037	matron
+n10303814	mayor, city manager
+n10304086	mayoress
+n10304650	mechanical engineer
+n10304914	medalist, medallist, medal winner
+n10305635	medical officer, medic
+n10305802	medical practitioner, medical man
+n10306004	medical scientist
+n10306279	medium, spiritualist, sensitive
+n10306496	megalomaniac
+n10306595	melancholic, melancholiac
+n10306890	Melkite, Melchite
+n10307114	melter
+n10308066	nonmember
+n10308168	board member
+n10308275	clansman, clanswoman, clan member
+n10308504	memorizer, memoriser
+n10308653	Mendelian
+n10308732	mender, repairer, fixer
+n10310783	Mesoamerican
+n10311506	messmate
+n10311661	mestiza
+n10312287	meteorologist
+n10312491	meter maid
+n10312600	Methodist
+n10313000	Metis
+n10313239	metropolitan
+n10313441	mezzo-soprano, mezzo
+n10313724	microeconomist, microeconomic expert
+n10314054	middle-aged man
+n10314182	middlebrow
+n10314517	middleweight
+n10314836	midwife, accoucheuse
+n10315217	mikado, tenno
+n10315456	Milanese
+n10315561	miler
+n10315730	miles gloriosus
+n10316360	military attache
+n10316527	military chaplain, padre, Holy Joe, sky pilot
+n10316862	military leader
+n10317007	military officer, officer
+n10317500	military policeman, MP
+n10317963	mill agent
+n10318293	mill-hand, factory worker
+n10318607	millionairess
+n10318686	millwright
+n10319313	minder
+n10320484	mining engineer
+n10320863	minister, government minister
+n10321126	ministrant
+n10321340	minor leaguer, bush leaguer
+n10321632	Minuteman
+n10321882	misanthrope, misanthropist
+n10322238	misfit
+n10323634	mistress
+n10323752	mistress, kept woman, fancy woman
+n10323999	mixed-blood
+n10324560	model, poser
+n10325549	class act
+n10325774	modeler, modeller
+n10326776	modifier
+n10327143	molecular biologist
+n10327987	Monegasque, Monacan
+n10328123	monetarist
+n10328328	moneygrubber
+n10328437	moneymaker
+n10328696	Mongoloid
+n10328941	monolingual
+n10329035	monologist
+n10330593	moonlighter
+n10330931	moralist
+n10331098	morosoph
+n10331167	morris dancer
+n10331258	mortal enemy
+n10331347	mortgagee, mortgage holder
+n10331841	mortician, undertaker, funeral undertaker, funeral director
+n10332110	moss-trooper
+n10332385	mother, female parent
+n10332861	mother
+n10332953	mother
+n10333044	mother figure
+n10333165	mother hen
+n10333317	mother-in-law
+n10333439	mother's boy, mamma's boy, mama's boy
+n10333601	mother's daughter
+n10333838	motorcycle cop, motorcycle policeman, speed cop
+n10334009	motorcyclist
+n10334461	Mound Builder
+n10334782	mountebank, charlatan
+n10335246	mourner, griever, sorrower, lamenter
+n10335801	mouthpiece, mouth
+n10335931	mover
+n10336411	moviegoer, motion-picture fan
+n10336904	muffin man
+n10337488	mugwump, independent, fencesitter
+n10338231	Mullah, Mollah, Mulla
+n10338391	muncher
+n10339179	murderess
+n10339251	murder suspect
+n10339717	musher
+n10340312	musician, instrumentalist, player
+n10341243	musicologist
+n10341343	music teacher
+n10341446	musketeer
+n10341573	Muslimah
+n10341955	mutilator, maimer, mangler
+n10342180	mutineer
+n10342367	mute, deaf-mute, deaf-and-dumb person
+n10342543	mutterer, mumbler, murmurer
+n10342893	muzzler
+n10342992	Mycenaen
+n10343088	mycologist
+n10343355	myope
+n10343449	myrmidon
+n10343554	mystic, religious mystic
+n10343869	mythologist
+n10344121	naif
+n10344203	nailer
+n10344319	namby-pamby
+n10344656	name dropper
+n10344774	namer
+n10345015	nan
+n10345100	nanny, nursemaid, nurse
+n10345302	narc, nark, narcotics agent
+n10345422	narcissist, narcist
+n10345659	nark, copper's nark
+n10346015	nationalist
+n10347204	nautch girl
+n10347446	naval commander
+n10348526	Navy SEAL, SEAL
+n10349243	obstructionist, obstructor, obstructer, resister, thwarter
+n10349750	Nazarene
+n10349836	Nazarene, Ebionite
+n10350220	Nazi, German Nazi
+n10350774	nebbish, nebbech
+n10351064	necker
+n10353016	neonate, newborn, newborn infant, newborn baby
+n10353355	nephew
+n10353928	neurobiologist
+n10354265	neurologist, brain doctor
+n10354754	neurosurgeon, brain surgeon
+n10355142	neutral
+n10355306	neutralist
+n10355449	newcomer, fledgling, fledgeling, starter, neophyte, freshman, newbie, entrant
+n10355688	newcomer
+n10355806	New Dealer
+n10356450	newspaper editor
+n10356877	newsreader, news reader
+n10357012	Newtonian
+n10357613	niece
+n10357737	niggard, skinflint, scrooge, churl
+n10358032	night porter
+n10358124	night rider, nightrider
+n10358575	NIMBY
+n10359117	niqaabi
+n10359422	nitpicker
+n10359546	Nobelist, Nobel Laureate
+n10359659	NOC
+n10360366	noncandidate
+n10360747	noncommissioned officer, noncom, enlisted officer
+n10361060	nondescript
+n10361194	nondriver
+n10361296	nonparticipant
+n10361525	nonperson, unperson
+n10362003	nonresident
+n10362319	nonsmoker
+n10362557	Northern Baptist
+n10363445	noticer
+n10363573	novelist
+n10364198	novitiate, novice
+n10364502	nuclear chemist, radiochemist
+n10365514	nudger
+n10366145	nullipara
+n10366276	number theorist
+n10366966	nurse
+n10368291	nursling, nurseling, suckling
+n10368528	nymph, houri
+n10368624	nymphet
+n10368711	nympholept
+n10368798	nymphomaniac, nympho
+n10369095	oarswoman
+n10369317	oboist
+n10369417	obscurantist
+n10369528	observer, commentator
+n10369699	obstetrician, accoucheur
+n10369955	occupier
+n10370381	occultist
+n10370955	wine lover
+n10371052	offerer, offeror
+n10371221	office-bearer
+n10371330	office boy
+n10371450	officeholder, officer
+n10373390	officiant
+n10373525	Federal, Fed, federal official
+n10374541	oilman
+n10374849	oil tycoon
+n10374943	old-age pensioner
+n10375052	old boy
+n10375314	old lady
+n10375402	old man
+n10376523	oldster, old person, senior citizen, golden ager
+n10376890	old-timer, oldtimer, gaffer, old geezer, antique
+n10377021	old woman
+n10377185	oligarch
+n10377291	Olympian
+n10377542	omnivore
+n10377633	oncologist
+n10378026	onlooker, looker-on
+n10378113	onomancer
+n10378780	operator
+n10379376	opportunist, self-seeker
+n10380126	optimist
+n10380499	Orangeman
+n10380672	orator, speechmaker, rhetorician, public speaker, speechifier
+n10381804	orderly, hospital attendant
+n10381981	orderly
+n10382157	orderly sergeant
+n10382302	ordinand
+n10382480	ordinary
+n10382710	organ-grinder
+n10382825	organist
+n10383094	organization man
+n10383237	organizer, organiser, arranger
+n10383505	organizer, organiser, labor organizer
+n10383816	originator, conceiver, mastermind
+n10384214	ornithologist, bird watcher
+n10384392	orphan
+n10384496	orphan
+n10385566	osteopath, osteopathist
+n10386196	out-and-outer
+n10386754	outdoorswoman
+n10386874	outfielder
+n10386984	outfielder
+n10387196	right fielder
+n10387324	right-handed pitcher, right-hander
+n10387836	outlier
+n10389865	owner-occupier
+n10389976	oyabun
+n10390600	packrat
+n10390698	padrone
+n10390807	padrone
+n10391416	page, pageboy
+n10393909	painter
+n10394434	Paleo-American, Paleo-Amerind, Paleo-Indian
+n10394786	paleontologist, palaeontologist, fossilist
+n10395073	pallbearer, bearer
+n10395209	palmist, palmister, chiromancer
+n10395390	pamperer, spoiler, coddler, mollycoddler
+n10395828	Panchen Lama
+n10396106	panelist, panellist
+n10396337	panhandler
+n10396727	paparazzo
+n10396908	paperboy
+n10397001	paperhanger, paperer
+n10397142	paperhanger
+n10397392	papoose, pappoose
+n10399130	pardoner
+n10400003	paretic
+n10400108	parishioner
+n10400205	park commissioner
+n10400437	Parliamentarian, Member of Parliament
+n10400618	parliamentary agent
+n10400998	parodist, lampooner
+n10401204	parricide
+n10401331	parrot
+n10401639	partaker, sharer
+n10402709	part-timer
+n10402824	party
+n10403633	party man, party liner
+n10403876	passenger, rider
+n10404426	passer
+n10404998	paster
+n10405540	pater
+n10405694	patient
+n10406266	patriarch
+n10406391	patriarch
+n10406765	patriarch, paterfamilias
+n10407310	patriot, nationalist
+n10407954	patron, sponsor, supporter
+n10408809	patternmaker
+n10409459	pawnbroker
+n10409752	payer, remunerator
+n10410246	peacekeeper
+n10410996	peasant
+n10411356	pedant, bookworm, scholastic
+n10411551	peddler, pedlar, packman, hawker, pitchman
+n10411867	pederast, paederast, child molester
+n10414239	penologist
+n10414768	pentathlete
+n10414865	Pentecostal, Pentecostalist
+n10415037	percussionist
+n10416567	periodontist
+n10417288	peshmerga
+n10417424	personality
+n10417551	personal representative
+n10417682	personage
+n10417843	persona grata
+n10417969	persona non grata
+n10418101	personification
+n10418735	perspirer, sweater
+n10419047	pervert, deviant, deviate, degenerate
+n10419472	pessimist
+n10419630	pest, blighter, cuss, pesterer, gadfly
+n10419785	Peter Pan
+n10420031	petitioner, suppliant, supplicant, requester
+n10420277	petit juror, petty juror
+n10420507	pet sitter, critter sitter
+n10420649	petter, fondler
+n10421016	Pharaoh, Pharaoh of Egypt
+n10421470	pharmacist, druggist, chemist, apothecary, pill pusher, pill roller
+n10421956	philanthropist, altruist
+n10422405	philatelist, stamp collector
+n10425946	philosopher
+n10426454	phonetician
+n10426630	phonologist
+n10427223	photojournalist
+n10427359	photometrist, photometrician
+n10427764	physical therapist, physiotherapist
+n10428004	physicist
+n10431122	piano maker
+n10431625	picker, chooser, selector
+n10432189	picnicker, picknicker
+n10432441	pilgrim
+n10432875	pill
+n10432957	pillar, mainstay
+n10433077	pill head
+n10433452	pilot
+n10433610	Piltdown man, Piltdown hoax
+n10433737	pimp, procurer, panderer, pander, pandar, fancy man, ponce
+n10435169	pipe smoker
+n10435251	pip-squeak, squirt, small fry
+n10435716	pisser, urinator
+n10435988	pitcher, hurler, twirler
+n10436334	pitchman
+n10437014	placeman, placeseeker
+n10437137	placer miner
+n10437262	plagiarist, plagiarizer, plagiariser, literary pirate, pirate
+n10437698	plainsman
+n10438172	planner, contriver, deviser
+n10438619	planter, plantation owner
+n10438842	plasterer
+n10439373	platinum blond, platinum blonde
+n10439523	platitudinarian
+n10439727	playboy, man-about-town, Corinthian
+n10439851	player, participant
+n10441037	playmate, playfellow
+n10441124	pleaser
+n10441694	pledger
+n10441962	plenipotentiary
+n10442093	plier, plyer
+n10442232	plodder, slowpoke, stick-in-the-mud, slowcoach
+n10442417	plodder, slogger
+n10442573	plotter, mapper
+n10443032	plumber, pipe fitter
+n10443659	pluralist
+n10443830	pluralist
+n10444194	poet
+n10448322	pointsman
+n10448455	point woman
+n10449664	policyholder
+n10450038	political prisoner
+n10450161	political scientist
+n10450303	politician, politico, pol, political leader
+n10451450	politician
+n10451590	pollster, poll taker, headcounter, canvasser
+n10451858	polluter, defiler
+n10453184	pool player
+n10455619	portraitist, portrait painter, portrayer, limner
+n10456070	poseuse
+n10456138	positivist, rationalist
+n10456696	postdoc, post doc
+n10457214	poster girl
+n10457444	postulator
+n10457903	private citizen
+n10458111	problem solver, solver, convergent thinker
+n10458356	pro-lifer
+n10458596	prosthetist
+n10459882	postulant
+n10460033	potboy, potman
+n10461060	poultryman, poulterer
+n10462588	power user
+n10462751	power worker, power-station worker
+n10462860	practitioner, practician
+n10464052	prayer, supplicant
+n10464542	preceptor, don
+n10464711	predecessor
+n10464870	preemptor, pre-emptor
+n10465002	preemptor, pre-emptor
+n10465451	premature baby, preterm baby, premature infant, preterm infant, preemie, premie
+n10465831	presbyter
+n10466198	presenter, sponsor
+n10466564	presentist
+n10466918	preserver
+n10467179	president
+n10467395	President of the United States, United States President, President, Chief Executive
+n10468750	president, prexy
+n10469611	press agent, publicity man, public relations man, PR man
+n10469874	press photographer
+n10470779	priest
+n10471640	prima ballerina
+n10471732	prima donna, diva
+n10471859	prima donna
+n10472129	primigravida, gravida I
+n10472447	primordial dwarf, hypoplastic dwarf, true dwarf, normal dwarf
+n10473453	prince charming
+n10473562	prince consort
+n10473789	princeling
+n10473917	Prince of Wales
+n10474064	princess
+n10474343	princess royal
+n10474446	principal, dealer
+n10474645	principal, school principal, head teacher, head
+n10475835	print seller
+n10475940	prior
+n10476467	private, buck private, common soldier
+n10477713	probationer, student nurse
+n10477955	processor
+n10478118	process-server
+n10478293	proconsul
+n10478462	proconsul
+n10478827	proctologist
+n10478960	proctor, monitor
+n10479135	procurator
+n10479328	procurer, securer
+n10481167	profit taker
+n10481268	programmer, computer programmer, coder, software engineer
+n10482054	promiser, promisor
+n10482220	promoter, booster, plugger
+n10482587	promulgator
+n10482921	propagandist
+n10483138	propagator, disseminator
+n10483395	property man, propman, property master
+n10483799	prophetess
+n10483890	prophet
+n10484858	prosecutor, public prosecutor, prosecuting officer, prosecuting attorney
+n10485298	prospector
+n10485883	protectionist
+n10486166	protegee
+n10486236	protozoologist
+n10486561	provost marshal
+n10487182	pruner, trimmer
+n10487363	psalmist
+n10487592	psephologist
+n10488016	psychiatrist, head-shrinker, shrink
+n10488309	psychic
+n10488656	psycholinguist
+n10489426	psychophysicist
+n10490421	publican, tavern keeper
+n10491998	pudge
+n10492086	puerpera
+n10492727	punching bag
+n10493199	punter
+n10493419	punter
+n10493685	puppeteer
+n10493835	puppy, pup
+n10493922	purchasing agent
+n10494195	puritan
+n10494373	Puritan
+n10495167	pursuer
+n10495421	pusher, shover
+n10495555	pusher, drug peddler, peddler, drug dealer, drug trafficker
+n10495756	pusher, thruster
+n10496393	putz
+n10496489	Pygmy, Pigmy
+n10497135	qadi
+n10497534	quadriplegic
+n10497645	quadruplet, quad
+n10498046	quaker, trembler
+n10498699	quarter
+n10498816	quarterback, signal caller, field general
+n10498986	quartermaster
+n10499110	quartermaster general
+n10499232	Quebecois
+n10499355	queen, queen regnant, female monarch
+n10499631	Queen of England
+n10499857	queen
+n10500217	queen
+n10500419	queen consort
+n10500603	queen mother
+n10500824	Queen's Counsel
+n10500942	question master, quizmaster
+n10501453	quick study, sponge
+n10501635	quietist
+n10502046	quitter
+n10502329	rabbi
+n10502950	racist, racialist
+n10503818	radiobiologist
+n10504090	radiologic technologist
+n10504206	radiologist, radiotherapist
+n10505347	rainmaker
+n10505613	raiser
+n10505732	raja, rajah
+n10505942	rake, rakehell, profligate, rip, blood, roue
+n10506336	ramrod
+n10506544	ranch hand
+n10506915	ranker
+n10507070	ranter, raver
+n10507380	rape suspect
+n10507482	rapper
+n10507565	rapporteur
+n10507692	rare bird, rara avis
+n10508141	ratepayer
+n10508379	raw recruit
+n10508710	reader
+n10509063	reading teacher
+n10509161	realist
+n10509810	real estate broker, real estate agent, estate agent, land agent, house agent
+n10510245	rear admiral
+n10510974	receiver
+n10511771	reciter
+n10512201	recruit, enlistee
+n10512372	recruit, military recruit
+n10512708	recruiter
+n10512859	recruiting-sergeant
+n10513509	redcap
+n10513823	redhead, redheader, red-header, carrottop
+n10513938	redneck, cracker
+n10514051	reeler
+n10514121	reenactor
+n10514255	referral
+n10514429	referee, ref
+n10514784	refiner
+n10515863	Reform Jew
+n10516527	registered nurse, RN
+n10517137	registrar
+n10517283	Regius professor
+n10518349	reliever, allayer, comforter
+n10519126	anchorite, hermit
+n10519494	religious leader
+n10519984	remover
+n10520286	Renaissance man, generalist
+n10520544	renegade
+n10520964	rentier
+n10521100	repairman, maintenance man, service man
+n10521662	reporter, newsman, newsperson
+n10521853	newswoman
+n10522035	representative
+n10522324	reprobate, miscreant
+n10522759	rescuer, recoverer, saver
+n10523341	reservist
+n10524076	resident commissioner
+n10524223	respecter
+n10524869	restaurateur, restauranter
+n10525134	restrainer, controller
+n10525436	retailer, retail merchant
+n10525617	retiree, retired person
+n10525878	returning officer
+n10526534	revenant
+n10527147	revisionist
+n10527334	revolutionist, revolutionary, subversive, subverter
+n10528023	rheumatologist
+n10528148	Rhodesian man, Homo rhodesiensis
+n10528493	rhymer, rhymester, versifier, poetizer, poetiser
+n10529231	rich person, wealthy person, have
+n10530150	rider
+n10530383	riding master
+n10530571	rifleman
+n10530959	right-hander, right hander, righthander
+n10531109	right-hand man, chief assistant, man Friday
+n10531445	ringer
+n10531838	ringleader
+n10533874	roadman, road mender
+n10533983	roarer, bawler, bellower, screamer, screecher, shouter, yeller
+n10536134	rocket engineer, rocket scientist
+n10536274	rocket scientist
+n10536416	rock star
+n10537708	Romanov, Romanoff
+n10537906	romanticist, romantic
+n10538629	ropemaker, rope-maker, roper
+n10538733	roper
+n10538853	roper
+n10539015	ropewalker, ropedancer
+n10539160	rosebud
+n10539278	Rosicrucian
+n10540114	Mountie
+n10540252	Rough Rider
+n10540656	roundhead
+n10541833	civil authority, civil officer
+n10542608	runner
+n10542761	runner
+n10542888	runner
+n10543161	running back
+n10543937	rusher
+n10544232	rustic
+n10544748	saboteur, wrecker, diversionist
+n10545792	sadist
+n10546428	sailing master, navigator
+n10546633	sailor, crewman
+n10548419	salesgirl, saleswoman, saleslady
+n10548537	salesman
+n10548681	salesperson, sales representative, sales rep
+n10549510	salvager, salvor
+n10550252	sandwichman
+n10550369	sangoma
+n10550468	sannup
+n10551576	sapper
+n10552393	Sassenach
+n10553140	satrap
+n10553235	saunterer, stroller, ambler
+n10554024	Savoyard
+n10554141	sawyer
+n10554846	scalper
+n10555059	scandalmonger
+n10555430	scapegrace, black sheep
+n10556033	scene painter
+n10556518	schemer, plotter
+n10556704	schizophrenic
+n10556825	schlemiel, shlemiel
+n10557246	schlockmeister, shlockmeister
+n10557854	scholar, scholarly person, bookman, student
+n10559009	scholiast
+n10559288	schoolchild, school-age child, pupil
+n10559508	schoolfriend
+n10559683	Schoolman, medieval Schoolman
+n10559996	schoolmaster
+n10560106	schoolmate, classmate, schoolfellow, class fellow
+n10560637	scientist
+n10561222	scion
+n10561320	scoffer, flouter, mocker, jeerer
+n10561736	scofflaw
+n10562135	scorekeeper, scorer
+n10562283	scorer
+n10562509	scourer
+n10562968	scout, talent scout
+n10563314	scoutmaster
+n10563403	scrambler
+n10563711	scratcher
+n10564098	screen actor, movie actor
+n10565502	scrutineer, canvasser
+n10565667	scuba diver
+n10566072	sculptor, sculpturer, carver, statue maker
+n10567613	Sea Scout
+n10567722	seasonal worker, seasonal
+n10567848	seasoner
+n10568200	second baseman, second sacker
+n10568358	second cousin
+n10568443	seconder
+n10568608	second fiddle, second banana
+n10568915	second-in-command
+n10569011	second lieutenant, 2nd lieutenant
+n10569179	second-rater, mediocrity
+n10570019	secretary
+n10570704	Secretary of Agriculture, Agriculture Secretary
+n10571907	Secretary of Health and Human Services
+n10572706	Secretary of State
+n10572889	Secretary of the Interior, Interior Secretary
+n10573957	sectarian, sectary, sectarist
+n10574311	section hand
+n10574538	secularist
+n10574840	security consultant
+n10575463	seeded player, seed
+n10575594	seeder, cloud seeder
+n10575787	seeker, searcher, quester
+n10576223	segregate
+n10576316	segregator, segregationist
+n10576676	selectman
+n10576818	selectwoman
+n10576962	selfish person
+n10577182	self-starter
+n10577284	seller, marketer, vender, vendor, trafficker
+n10577710	selling agent
+n10577820	semanticist, semiotician
+n10578021	semifinalist
+n10578162	seminarian, seminarist
+n10578471	senator
+n10578656	sendee
+n10579062	senior
+n10579549	senior vice president
+n10580030	separatist, separationist
+n10580437	septuagenarian
+n10580535	serf, helot, villein
+n10581648	spree killer
+n10581890	serjeant-at-law, serjeant, sergeant-at-law, sergeant
+n10582604	server
+n10582746	serviceman, military man, man, military personnel
+n10583387	settler, colonist
+n10583790	settler
+n10585077	sex symbol
+n10585217	sexton, sacristan
+n10585628	shaheed
+n10586166	Shakespearian, Shakespearean
+n10586265	shanghaier, seizer
+n10586444	sharecropper, cropper, sharecrop farmer
+n10586903	shaver
+n10586998	Shavian
+n10588074	sheep
+n10588357	sheik, tribal sheik, sheikh, tribal sheikh, Arab chief
+n10588724	shelver
+n10588965	shepherd
+n10589666	ship-breaker
+n10590146	shipmate
+n10590239	shipowner
+n10590452	shipping agent
+n10590903	shirtmaker
+n10591072	shogun
+n10591811	shopaholic
+n10592049	shop girl
+n10592811	shop steward, steward
+n10593521	shot putter
+n10594147	shrew, termagant
+n10594523	shuffler
+n10594857	shyster, pettifogger
+n10595164	sibling, sib
+n10595647	sick person, diseased person, sufferer
+n10596517	sightreader
+n10596899	signaler, signaller
+n10597505	signer
+n10597745	signor, signior
+n10597889	signora
+n10598013	signore
+n10598181	signorina
+n10598459	silent partner, sleeping partner
+n10598904	addle-head, addlehead, loon, birdbrain
+n10599215	simperer
+n10599806	singer, vocalist, vocalizer, vocaliser
+n10601234	Sinologist
+n10601362	sipper
+n10602119	sirrah
+n10602470	Sister
+n10602985	sister, sis
+n10603528	waverer, vacillator, hesitator, hesitater
+n10603851	sitar player
+n10604275	sixth-former
+n10604380	skateboarder
+n10604634	skeptic, sceptic, doubter
+n10604880	sketcher
+n10604979	skidder
+n10605253	skier
+n10605737	skinny-dipper
+n10607291	skin-diver, aquanaut
+n10607478	skinhead
+n10609092	slasher
+n10609198	slattern, slut, slovenly woman, trollop
+n10610465	sleeper, slumberer
+n10610850	sleeper
+n10611267	sleeping beauty
+n10611613	sleuth, sleuthhound
+n10612210	slob, sloven, pig, slovenly person
+n10612373	sloganeer
+n10612518	slopseller, slop-seller
+n10613996	smasher, stunner, knockout, beauty, ravisher, sweetheart, peach, lulu, looker, mantrap, dish
+n10614507	smirker
+n10614629	smith, metalworker
+n10615179	smoothie, smoothy, sweet talker, charmer
+n10615334	smuggler, runner, contrabandist, moon curser, moon-curser
+n10616578	sneezer
+n10617024	snob, prig, snot, snoot
+n10617193	snoop, snooper
+n10617397	snorer
+n10618234	sob sister
+n10618342	soccer player
+n10618465	social anthropologist, cultural anthropologist
+n10618685	social climber, climber
+n10618848	socialist
+n10619492	socializer, socialiser
+n10619642	social scientist
+n10619888	social secretary
+n10620212	Socinian
+n10620586	sociolinguist
+n10620758	sociologist
+n10621294	soda jerk, soda jerker
+n10621400	sodalist
+n10621514	sodomite, sodomist, sod, bugger
+n10622053	soldier
+n10624074	son, boy
+n10624310	songster
+n10624437	songstress
+n10624540	songwriter, songster, ballad maker
+n10625860	sorcerer, magician, wizard, necromancer, thaumaturge, thaumaturgist
+n10626630	sorehead
+n10627252	soul mate
+n10628097	Southern Baptist
+n10628644	sovereign, crowned head, monarch
+n10629329	spacewalker
+n10629647	Spanish American, Hispanic American, Hispanic
+n10629939	sparring partner, sparring mate
+n10630093	spastic
+n10630188	speaker, talker, utterer, verbalizer, verbaliser
+n10631131	native speaker
+n10631309	Speaker
+n10631654	speechwriter
+n10632576	specialist, medical specialist
+n10633298	specifier
+n10633450	spectator, witness, viewer, watcher, looker
+n10634464	speech therapist
+n10634849	speedskater, speed skater
+n10634990	spellbinder
+n10635788	sphinx
+n10636488	spinster, old maid
+n10637483	split end
+n10638922	sport, sportsman, sportswoman
+n10639238	sport, summercater
+n10639359	sporting man, outdoor man
+n10639637	sports announcer, sportscaster, sports commentator
+n10639817	sports editor
+n10641223	sprog
+n10642596	square dancer
+n10642705	square shooter, straight shooter, straight arrow
+n10643095	squatter
+n10643837	squire
+n10643937	squire
+n10644598	staff member, staffer
+n10645017	staff sergeant
+n10645223	stage director
+n10646032	stainer
+n10646140	stakeholder
+n10646433	stalker
+n10646641	stalking-horse
+n10646780	stammerer, stutterer
+n10646942	stamper, stomper, tramper, trampler
+n10647745	standee
+n10648237	stand-in, substitute, relief, reliever, backup, backup man, fill-in
+n10648696	star, principal, lead
+n10649197	starlet
+n10649308	starter, dispatcher
+n10650162	statesman, solon, national leader
+n10652605	state treasurer
+n10652703	stationer, stationery seller
+n10654015	stenographer, amanuensis, shorthand typist
+n10654211	stentor
+n10654321	stepbrother, half-brother, half brother
+n10654827	stepmother
+n10654932	stepparent
+n10655169	stevedore, loader, longshoreman, docker, dockhand, dock worker, dockworker, dock-walloper, lumper
+n10655442	steward
+n10655594	steward, flight attendant
+n10655730	steward
+n10655986	stickler
+n10656120	stiff
+n10656223	stifler, smotherer
+n10656969	stipendiary, stipendiary magistrate
+n10657306	stitcher
+n10657556	stockjobber
+n10657835	stock trader
+n10658304	stockist
+n10659042	stoker, fireman
+n10659762	stooper
+n10660128	store detective
+n10660621	strafer
+n10660883	straight man, second banana
+n10661002	stranger, alien, unknown
+n10661216	stranger
+n10661563	strategist, strategian
+n10661732	straw boss, assistant foreman
+n10663315	streetwalker, street girl, hooker, hustler, floozy, floozie, slattern
+n10663549	stretcher-bearer, litter-bearer
+n10665302	struggler
+n10665587	stud, he-man, macho-man
+n10665698	student, pupil, educatee
+n10666752	stumblebum, palooka
+n10667477	stylist
+n10667709	subaltern
+n10667863	subcontractor
+n10668450	subduer, surmounter, overcomer
+n10668666	subject, case, guinea pig
+n10669991	subordinate, subsidiary, underling, foot soldier
+n10671042	substitute, reserve, second-stringer
+n10671613	successor, heir
+n10671736	successor, replacement
+n10671898	succorer, succourer
+n10672371	Sufi
+n10672540	suffragan, suffragan bishop
+n10672662	suffragette
+n10673296	sugar daddy
+n10673776	suicide bomber
+n10674130	suitor, suer, wooer
+n10674713	sumo wrestler
+n10675010	sunbather
+n10675142	sundowner
+n10675609	super heavyweight
+n10676018	superior, higher-up, superordinate
+n10676434	supermom
+n10676569	supernumerary, spear carrier, extra
+n10678937	supremo
+n10679174	surgeon, operating surgeon, sawbones
+n10679503	Surgeon General
+n10679610	Surgeon General
+n10679723	surpriser
+n10680609	surveyor
+n10680796	surveyor
+n10681194	survivor, subsister
+n10681557	sutler, victualer, victualler, provisioner
+n10682713	sweeper
+n10682953	sweetheart, sweetie, steady, truelove
+n10683675	swinger, tramp
+n10684146	switcher, whipper
+n10684630	swot, grind, nerd, wonk, dweeb
+n10684827	sycophant, toady, crawler, lackey, ass-kisser
+n10685398	sylph
+n10686073	sympathizer, sympathiser, well-wisher
+n10686517	symphonist
+n10686694	syncopator
+n10686885	syndic
+n10688356	tactician
+n10688811	tagger
+n10689306	tailback
+n10690268	tallyman, tally clerk
+n10690421	tallyman
+n10690648	tanker, tank driver
+n10691318	tapper, wiretapper, phone tapper
+n10691937	Tartuffe, Tartufe
+n10692090	Tarzan
+n10692482	taster, taste tester, taste-tester, sampler
+n10692883	tax assessor, assessor
+n10693235	taxer
+n10693334	taxi dancer
+n10693824	taxonomist, taxonomer, systematist
+n10694258	teacher, instructor
+n10694939	teaching fellow
+n10695450	tearaway
+n10696101	technical sergeant
+n10696508	technician
+n10697135	Ted, Teddy boy
+n10697282	teetotaler, teetotaller, teetotalist
+n10698368	television reporter, television newscaster, TV reporter, TV newsman
+n10699558	temporizer, temporiser
+n10699752	tempter
+n10699981	term infant
+n10700105	toiler
+n10700201	tenant, renter
+n10700640	tenant
+n10700963	tenderfoot
+n10701180	tennis player
+n10701644	tennis pro, professional tennis player
+n10701962	tenor saxophonist, tenorist
+n10702167	termer
+n10702615	terror, scourge, threat
+n10703221	tertigravida, gravida III
+n10703336	testator, testate
+n10703480	testatrix
+n10703692	testee, examinee
+n10704238	test-tube baby
+n10704712	Texas Ranger, Ranger
+n10704886	thane
+n10705448	theatrical producer
+n10705615	theologian, theologist, theologizer, theologiser
+n10706812	theorist, theoretician, theorizer, theoriser, idealogue
+n10707134	theosophist
+n10707233	therapist, healer
+n10707707	Thessalonian
+n10708292	thinker, creative thinker, mind
+n10708454	thinker
+n10709529	thrower
+n10710171	thurifer
+n10710259	ticket collector, ticket taker
+n10710778	tight end
+n10710913	tiler
+n10711483	timekeeper, timer
+n10711766	Timorese
+n10712229	tinkerer, fiddler
+n10712374	tinsmith, tinner
+n10712474	tinter
+n10712690	tippler, social drinker
+n10712835	tipster, tout
+n10713254	T-man
+n10713686	toastmaster, symposiarch
+n10713843	toast mistress
+n10714195	tobogganist
+n10715030	tomboy, romp, hoyden
+n10715347	toolmaker
+n10715789	torchbearer
+n10716576	Tory
+n10716864	Tory
+n10717055	tosser
+n10717196	tosser, jerk-off, wanker
+n10717337	totalitarian
+n10718131	tourist, tourer, holidaymaker
+n10718349	tout, touter
+n10718509	tout, ticket tout
+n10718665	tovarich, tovarisch
+n10718952	towhead
+n10719036	town clerk
+n10719132	town crier, crier
+n10719267	townsman, towner
+n10719807	toxicologist
+n10720197	track star
+n10720453	trader, bargainer, dealer, monger
+n10720964	trade unionist, unionist, union member
+n10721124	traditionalist, diehard
+n10721321	traffic cop
+n10721612	tragedian
+n10721708	tragedian
+n10721819	tragedienne
+n10722029	trail boss
+n10722575	trainer
+n10722965	traitor, treasonist
+n10723230	traitress
+n10723597	transactor
+n10724132	transcriber
+n10724372	transfer, transferee
+n10724570	transferee
+n10725280	translator, transcriber
+n10726031	transvestite, cross-dresser
+n10726786	traveling salesman, travelling salesman, commercial traveler, commercial traveller, roadman, bagman
+n10727016	traverser
+n10727171	trawler
+n10727458	Treasury, First Lord of the Treasury
+n10728117	trencher
+n10728233	trend-setter, taste-maker, fashion arbiter
+n10728624	tribesman
+n10728998	trier, attempter, essayer
+n10729330	trifler
+n10730542	trooper
+n10730728	trooper, state trooper
+n10731013	Trotskyite, Trotskyist, Trot
+n10731732	truant, hooky player
+n10732010	trumpeter, cornetist
+n10732521	trusty
+n10732854	Tudor
+n10732967	tumbler
+n10733820	tutee
+n10734394	twin
+n10734741	two-timer
+n10734891	Tyke
+n10734963	tympanist, timpanist
+n10735173	typist
+n10735298	tyrant, autocrat, despot
+n10735984	umpire, ump
+n10737103	understudy, standby
+n10737264	undesirable
+n10738111	unicyclist
+n10738215	unilateralist
+n10738670	Unitarian
+n10738871	Arminian
+n10739135	universal donor
+n10739297	UNIX guru
+n10739391	Unknown Soldier
+n10740594	upsetter
+n10740732	upstager
+n10740868	upstart, parvenu, nouveau-riche, arriviste
+n10741152	upstart
+n10741367	urchin
+n10741493	urologist
+n10742005	usherette
+n10742111	usher, doorkeeper
+n10742546	usurper, supplanter
+n10742997	utility man
+n10743124	utilizer, utiliser
+n10743356	Utopian
+n10744078	uxoricide
+n10744164	vacationer, vacationist
+n10745006	valedictorian, valedictory speaker
+n10745770	valley girl
+n10746931	vaulter, pole vaulter, pole jumper
+n10747119	vegetarian
+n10747424	vegan
+n10747548	venerator
+n10747965	venture capitalist
+n10748142	venturer, merchant-venturer
+n10748506	vermin, varmint
+n10748620	very important person, VIP, high-up, dignitary, panjandrum, high muckamuck
+n10749928	vibist, vibraphonist
+n10750031	vicar
+n10750188	vicar
+n10750640	vicar-general
+n10751026	vice chancellor
+n10751152	vicegerent
+n10751265	vice president, V.P.
+n10751710	vice-regent
+n10752480	victim, dupe
+n10753061	Victorian
+n10753182	victualer, victualler
+n10753339	vigilante, vigilance man
+n10753442	villager
+n10753989	vintager
+n10754189	vintner, wine merchant
+n10754281	violator, debaucher, ravisher
+n10754449	violator, lawbreaker, law offender
+n10755080	violist
+n10755164	virago
+n10755394	virologist
+n10755648	Visayan, Bisayan
+n10756061	viscountess
+n10756148	viscount
+n10756261	Visigoth
+n10756641	visionary
+n10756837	visiting fireman
+n10757050	visiting professor
+n10757492	visualizer, visualiser
+n10758337	vixen, harpy, hellcat
+n10758445	vizier
+n10758949	voicer
+n10759151	volunteer, unpaid worker
+n10759331	volunteer, military volunteer, voluntary
+n10759982	votary
+n10760199	votary
+n10760622	vouchee
+n10760951	vower
+n10761190	voyager
+n10761326	voyeur, Peeping Tom, peeper
+n10761519	vulcanizer, vulcaniser
+n10762212	waffler
+n10762480	Wagnerian
+n10763075	waif, street child
+n10763245	wailer
+n10763383	waiter, server
+n10763620	waitress
+n10764465	walking delegate
+n10764622	walk-on
+n10764719	wallah
+n10765305	wally
+n10765587	waltzer
+n10765679	wanderer, roamer, rover, bird of passage
+n10765885	Wandering Jew
+n10766260	wanton
+n10768148	warrantee
+n10768272	warrantee
+n10768903	washer
+n10769084	washerman, laundryman
+n10769188	washwoman, washerwoman, laundrywoman, laundress
+n10769321	wassailer, carouser
+n10769459	wastrel, waster
+n10771066	Wave
+n10772092	weatherman, weather forecaster
+n10772580	weekend warrior
+n10772937	weeder
+n10773665	welder
+n10773800	welfare case, charity case
+n10774329	westerner
+n10774756	West-sider
+n10775003	wetter
+n10775128	whaler
+n10776052	Whig
+n10776339	whiner, complainer, moaner, sniveller, crybaby, bellyacher, grumbler, squawker
+n10776887	whipper-in
+n10777299	whisperer
+n10778044	whiteface
+n10778148	Carmelite, White Friar
+n10778711	Augustinian
+n10778999	white hope, great white hope
+n10779610	white supremacist
+n10779897	whoremaster, whoremonger
+n10779995	whoremaster, whoremonger, john, trick
+n10780284	widow, widow woman
+n10780632	wife, married woman
+n10781236	wiggler, wriggler, squirmer
+n10781817	wimp, chicken, crybaby
+n10782362	wing commander
+n10782471	winger
+n10782791	winner
+n10782940	winner, victor
+n10783240	window dresser, window trimmer
+n10783539	winker
+n10783646	wiper
+n10783734	wireman, wirer
+n10784113	wise guy, smart aleck, wiseacre, wisenheimer, weisenheimer
+n10784544	witch doctor
+n10784922	withdrawer
+n10785480	withdrawer
+n10787470	woman, adult female
+n10788852	woman
+n10789415	wonder boy, golden boy
+n10789709	wonderer
+n10791115	working girl
+n10791221	workman, workingman, working man, working person
+n10791820	workmate
+n10791890	worldling
+n10792335	worshiper, worshipper
+n10792506	worthy
+n10792856	wrecker
+n10793570	wright
+n10793799	write-in candidate, write-in
+n10794014	writer, author
+n10801561	Wykehamist
+n10801802	yakuza
+n10802507	yard bird, yardbird
+n10802621	yardie
+n10802953	yardman
+n10803031	yardmaster, trainmaster, train dispatcher
+n10803282	yenta
+n10803978	yogi
+n10804287	young buck, young man
+n10804636	young Turk
+n10804732	Young Turk
+n10805501	Zionist
+n10806113	zoo keeper
+n10994097	Genet, Edmund Charles Edouard Genet, Citizen Genet
+n11100798	Kennan, George F. Kennan, George Frost Kennan
+n11196627	Munro, H. H. Munro, Hector Hugh Munro, Saki
+n11242849	Popper, Karl Popper, Sir Karl Raimund Popper
+n11318824	Stoker, Bram Stoker, Abraham Stoker
+n11346873	Townes, Charles Townes, Charles Hard Townes
+n11448153	dust storm, duster, sandstorm, sirocco
+n11487732	parhelion, mock sun, sundog
+n11508382	snow, snowfall
+n11511327	facula
+n11524451	wave
+n11530008	microflora
+n11531193	wilding
+n11531334	semi-climber
+n11532682	volva
+n11533212	basidiocarp
+n11533999	domatium
+n11536567	apomict
+n11536673	aquatic
+n11537327	bryophyte, nonvascular plant
+n11539289	acrocarp, acrocarpous moss
+n11542137	sphagnum, sphagnum moss, peat moss, bog moss
+n11542640	liverwort, hepatic
+n11544015	hepatica, Marchantia polymorpha
+n11545350	pecopteris
+n11545524	pteridophyte, nonflowering plant
+n11545714	fern
+n11547562	fern ally
+n11547855	spore
+n11548728	carpospore
+n11548870	chlamydospore
+n11549009	conidium, conidiospore
+n11549245	oospore
+n11549779	tetraspore
+n11549895	zoospore
+n11552133	cryptogam
+n11552386	spermatophyte, phanerogam, seed plant
+n11552594	seedling
+n11552806	annual
+n11552976	biennial
+n11553240	perennial
+n11553522	hygrophyte
+n11596108	gymnosperm
+n11597657	gnetum, Gnetum gnemon
+n11598287	Catha edulis
+n11598686	ephedra, joint fir
+n11598886	mahuang, Ephedra sinica
+n11599324	welwitschia, Welwitschia mirabilis
+n11600372	cycad
+n11601177	sago palm, Cycas revoluta
+n11601333	false sago, fern palm, Cycas circinalis
+n11601918	zamia
+n11602091	coontie, Florida arrowroot, Seminole bread, Zamia pumila
+n11602478	ceratozamia
+n11602873	dioon
+n11603246	encephalartos
+n11603462	kaffir bread, Encephalartos caffer
+n11603835	macrozamia
+n11604046	burrawong, Macrozamia communis, Macrozamia spiralis
+n11608250	pine, pine tree, true pine
+n11609475	pinon, pinyon
+n11609684	nut pine
+n11609862	pinon pine, Mexican nut pine, Pinus cembroides
+n11610047	Rocky mountain pinon, Pinus edulis
+n11610215	single-leaf, single-leaf pine, single-leaf pinyon, Pinus monophylla
+n11610437	bishop pine, bishop's pine, Pinus muricata
+n11610602	California single-leaf pinyon, Pinus californiarum
+n11610823	Parry's pinyon, Pinus quadrifolia, Pinus parryana
+n11611087	spruce pine, Pinus glabra
+n11611233	black pine, Pinus nigra
+n11611356	pitch pine, northern pitch pine, Pinus rigida
+n11611561	pond pine, Pinus serotina
+n11611758	stone pine, umbrella pine, European nut pine, Pinus pinea
+n11612018	Swiss pine, Swiss stone pine, arolla pine, cembra nut tree, Pinus cembra
+n11612235	cembra nut, cedar nut
+n11612349	Swiss mountain pine, mountain pine, dwarf mountain pine, mugho pine, mugo pine, Pinus mugo
+n11612575	ancient pine, Pinus longaeva
+n11612923	white pine
+n11613219	American white pine, eastern white pine, weymouth pine, Pinus strobus
+n11613459	western white pine, silver pine, mountain pine, Pinus monticola
+n11613692	southwestern white pine, Pinus strobiformis
+n11613867	limber pine, Pinus flexilis
+n11614039	whitebark pine, whitebarked pine, Pinus albicaulis
+n11614250	yellow pine
+n11614420	ponderosa, ponderosa pine, western yellow pine, bull pine, Pinus ponderosa
+n11614713	Jeffrey pine, Jeffrey's pine, black pine, Pinus jeffreyi
+n11615026	shore pine, lodgepole, lodgepole pine, spruce pine, Pinus contorta
+n11615259	Sierra lodgepole pine, Pinus contorta murrayana
+n11615387	loblolly pine, frankincense pine, Pinus taeda
+n11615607	jack pine, Pinus banksiana
+n11615812	swamp pine
+n11615967	longleaf pine, pitch pine, southern yellow pine, Georgia pine, Pinus palustris
+n11616260	shortleaf pine, short-leaf pine, shortleaf yellow pine, Pinus echinata
+n11616486	red pine, Canadian red pine, Pinus resinosa
+n11616662	Scotch pine, Scots pine, Scotch fir, Pinus sylvestris
+n11616852	scrub pine, Virginia pine, Jersey pine, Pinus virginiana
+n11617090	Monterey pine, Pinus radiata
+n11617272	bristlecone pine, Rocky Mountain bristlecone pine, Pinus aristata
+n11617631	table-mountain pine, prickly pine, hickory pine, Pinus pungens
+n11617878	knobcone pine, Pinus attenuata
+n11618079	Japanese red pine, Japanese table pine, Pinus densiflora
+n11618290	Japanese black pine, black pine, Pinus thunbergii
+n11618525	Torrey pine, Torrey's pine, soledad pine, grey-leaf pine, sabine pine, Pinus torreyana
+n11618861	larch, larch tree
+n11619227	American larch, tamarack, black larch, Larix laricina
+n11619455	western larch, western tamarack, Oregon larch, Larix occidentalis
+n11619687	subalpine larch, Larix lyallii
+n11619845	European larch, Larix decidua
+n11620016	Siberian larch, Larix siberica, Larix russica
+n11620389	golden larch, Pseudolarix amabilis
+n11620673	fir, fir tree, true fir
+n11621029	silver fir
+n11621281	amabilis fir, white fir, Pacific silver fir, red silver fir, Christmas tree, Abies amabilis
+n11621547	European silver fir, Christmas tree, Abies alba
+n11621727	white fir, Colorado fir, California white fir, Abies concolor, Abies lowiana
+n11621950	balsam fir, balm of Gilead, Canada balsam, Abies balsamea
+n11622184	Fraser fir, Abies fraseri
+n11622368	lowland fir, lowland white fir, giant fir, grand fir, Abies grandis
+n11622591	Alpine fir, subalpine fir, Abies lasiocarpa
+n11622771	Santa Lucia fir, bristlecone fir, Abies bracteata, Abies venusta
+n11623105	cedar, cedar tree, true cedar
+n11623815	cedar of Lebanon, Cedrus libani
+n11623967	deodar, deodar cedar, Himalayan cedar, Cedrus deodara
+n11624192	Atlas cedar, Cedrus atlantica
+n11624531	spruce
+n11625003	Norway spruce, Picea abies
+n11625223	weeping spruce, Brewer's spruce, Picea breweriana
+n11625391	Engelmann spruce, Engelmann's spruce, Picea engelmannii
+n11625632	white spruce, Picea glauca
+n11625804	black spruce, Picea mariana, spruce pine
+n11626010	Siberian spruce, Picea obovata
+n11626152	Sitka spruce, Picea sitchensis
+n11626409	oriental spruce, Picea orientalis
+n11626585	Colorado spruce, Colorado blue spruce, silver spruce, Picea pungens
+n11626826	red spruce, eastern spruce, yellow spruce, Picea rubens
+n11627168	hemlock, hemlock tree
+n11627512	eastern hemlock, Canadian hemlock, spruce pine, Tsuga canadensis
+n11627714	Carolina hemlock, Tsuga caroliniana
+n11627908	mountain hemlock, black hemlock, Tsuga mertensiana
+n11628087	western hemlock, Pacific hemlock, west coast hemlock, Tsuga heterophylla
+n11628456	douglas fir
+n11628793	green douglas fir, douglas spruce, douglas pine, douglas hemlock, Oregon fir, Oregon pine, Pseudotsuga menziesii
+n11629047	big-cone spruce, big-cone douglas fir, Pseudotsuga macrocarpa
+n11629354	Cathaya
+n11630017	cedar, cedar tree
+n11630489	cypress, cypress tree
+n11631159	gowen cypress, Cupressus goveniana
+n11631405	pygmy cypress, Cupressus pigmaea, Cupressus goveniana pigmaea
+n11631619	Santa Cruz cypress, Cupressus abramsiana, Cupressus goveniana abramsiana
+n11631854	Arizona cypress, Cupressus arizonica
+n11631985	Guadalupe cypress, Cupressus guadalupensis
+n11632167	Monterey cypress, Cupressus macrocarpa
+n11632376	Mexican cypress, cedar of Goa, Portuguese cypress, Cupressus lusitanica
+n11632619	Italian cypress, Mediterranean cypress, Cupressus sempervirens
+n11632929	King William pine, Athrotaxis selaginoides
+n11633284	Chilean cedar, Austrocedrus chilensis
+n11634736	incense cedar, red cedar, Calocedrus decurrens, Libocedrus decurrens
+n11635152	southern white cedar, coast white cedar, Atlantic white cedar, white cypress, white cedar, Chamaecyparis thyoides
+n11635433	Oregon cedar, Port Orford cedar, Lawson's cypress, Lawson's cedar, Chamaecyparis lawsoniana
+n11635830	yellow cypress, yellow cedar, Nootka cypress, Alaska cedar, Chamaecyparis nootkatensis
+n11636204	Japanese cedar, Japan cedar, sugi, Cryptomeria japonica
+n11636835	juniper berry
+n11639084	incense cedar
+n11639306	kawaka, Libocedrus plumosa
+n11639445	pahautea, Libocedrus bidwillii, mountain pine
+n11640132	metasequoia, dawn redwood, Metasequoia glyptostrodoides
+n11643835	arborvitae
+n11644046	western red cedar, red cedar, canoe cedar, Thuja plicata
+n11644226	American arborvitae, northern white cedar, white cedar, Thuja occidentalis
+n11644462	Oriental arborvitae, Thuja orientalis, Platycladus orientalis
+n11644872	hiba arborvitae, Thujopsis dolobrata
+n11645163	keteleeria
+n11645590	Wollemi pine
+n11645914	araucaria
+n11646167	monkey puzzle, chile pine, Araucaria araucana
+n11646344	norfolk island pine, Araucaria heterophylla, Araucaria excelsa
+n11646517	new caledonian pine, Araucaria columnaris
+n11646694	bunya bunya, bunya bunya tree, Araucaria bidwillii
+n11646955	hoop pine, Moreton Bay pine, Araucaria cunninghamii
+n11647306	kauri pine, dammar pine
+n11647703	kauri, kaury, Agathis australis
+n11647868	amboina pine, amboyna pine, Agathis dammara, Agathis alba
+n11648039	dundathu pine, queensland kauri, smooth bark kauri, Agathis robusta
+n11648268	red kauri, Agathis lanceolata
+n11648776	plum-yew
+n11649150	California nutmeg, nutmeg-yew, Torreya californica
+n11649359	stinking cedar, stinking yew, Torrey tree, Torreya taxifolia
+n11649878	celery pine
+n11650160	celery top pine, celery-topped pine, Phyllocladus asplenifolius
+n11650307	tanekaha, Phyllocladus trichomanoides
+n11650430	Alpine celery pine, Phyllocladus alpinus
+n11650558	yellowwood, yellowwood tree
+n11650759	gymnospermous yellowwood
+n11652039	podocarp
+n11652217	yacca, yacca podocarp, Podocarpus coriaceus
+n11652376	brown pine, Rockingham podocarp, Podocarpus elatus
+n11652578	cape yellowwood, African yellowwood, Podocarpus elongatus
+n11652753	South-African yellowwood, Podocarpus latifolius
+n11652966	alpine totara, Podocarpus nivalis
+n11653126	totara, Podocarpus totara
+n11653570	common yellowwood, bastard yellowwood, Afrocarpus falcata
+n11653904	kahikatea, New Zealand Dacryberry, New Zealand white pine, Dacrycarpus dacrydioides, Podocarpus dacrydioides
+n11654293	rimu, imou pine, red pine, Dacrydium cupressinum
+n11654438	tarwood, tar-wood, Dacrydium colensoi
+n11654984	common sickle pine, Falcatifolium falciforme
+n11655152	yellow-leaf sickle pine, Falcatifolium taxoides
+n11655592	tarwood, tar-wood, New Zealand mountain pine, Halocarpus bidwilli, Dacrydium bidwilli
+n11655974	westland pine, silver pine, Lagarostrobus colensoi
+n11656123	huon pine, Lagarostrobus franklinii, Dacrydium franklinii
+n11656549	Chilean rimu, Lepidothamnus fonkii
+n11656771	mountain rimu, Lepidothamnus laxifolius, Dacridium laxifolius
+n11657585	nagi, Nageia nagi
+n11658331	miro, black pine, Prumnopitys ferruginea, Podocarpus ferruginea
+n11658544	matai, black pine, Prumnopitys taxifolia, Podocarpus spicata
+n11658709	plum-fruited yew, Prumnopitys andina, Prumnopitys elegans
+n11659248	Prince Albert yew, Prince Albert's yew, Saxe-gothea conspicua
+n11659627	Sundacarpus amara, Prumnopitys amara, Podocarpus amara
+n11660300	Japanese umbrella pine, Sciadopitys verticillata
+n11661372	yew
+n11661909	Old World yew, English yew, Taxus baccata
+n11662128	Pacific yew, California yew, western yew, Taxus brevifolia
+n11662371	Japanese yew, Taxus cuspidata
+n11662585	Florida yew, Taxus floridana
+n11662937	New Caledonian yew, Austrotaxus spicata
+n11663263	white-berry yew, Pseudotaxus chienii
+n11664418	ginkgo, gingko, maidenhair tree, Ginkgo biloba
+n11665372	angiosperm, flowering plant
+n11666854	dicot, dicotyledon, magnoliopsid, exogen
+n11668117	monocot, monocotyledon, liliopsid, endogen
+n11669786	floret, floweret
+n11669921	flower
+n11672269	bloomer
+n11672400	wildflower, wild flower
+n11674019	apetalous flower
+n11674332	inflorescence
+n11675025	rosebud
+n11675404	gynostegium
+n11675738	pollinium
+n11676500	pistil
+n11676743	gynobase
+n11676850	gynophore
+n11677485	stylopodium
+n11677902	carpophore
+n11678010	cornstalk, corn stalk
+n11678299	petiolule
+n11678377	mericarp
+n11679378	micropyle
+n11680457	germ tube
+n11680596	pollen tube
+n11682659	gemma
+n11683216	galbulus
+n11683838	nectary, honey gland
+n11684264	pericarp, seed vessel
+n11684499	epicarp, exocarp
+n11684654	mesocarp
+n11685091	pip
+n11685621	silique, siliqua
+n11686195	cataphyll
+n11686652	perisperm
+n11686780	monocarp, monocarpic plant, monocarpous plant
+n11686912	sporophyte
+n11687071	gametophyte
+n11687432	megasporangium, macrosporangium
+n11687789	microspore
+n11687964	microsporangium
+n11688069	microsporophyll
+n11688378	archespore, archesporium
+n11689197	bonduc nut, nicker nut, nicker seed
+n11689367	Job's tears
+n11689483	oilseed, oil-rich seed
+n11689678	castor bean
+n11689815	cottonseed
+n11689957	candlenut
+n11690088	peach pit
+n11690254	hypanthium, floral cup, calyx tube
+n11690455	petal, flower petal
+n11691046	corolla
+n11691857	lip
+n11692265	perianth, chlamys, floral envelope, perigone, perigonium
+n11692792	thistledown
+n11693981	custard apple, custard apple tree
+n11694300	cherimoya, cherimoya tree, Annona cherimola
+n11694469	ilama, ilama tree, Annona diversifolia
+n11694664	soursop, prickly custard apple, soursop tree, Annona muricata
+n11694866	bullock's heart, bullock's heart tree, bullock heart, Annona reticulata
+n11695085	sweetsop, sweetsop tree, Annona squamosa
+n11695285	pond apple, pond-apple tree, Annona glabra
+n11695599	pawpaw, papaw, papaw tree, Asimina triloba
+n11695974	ilang-ilang, ylang-ylang, Cananga odorata
+n11696450	lancewood, lancewood tree, Oxandra lanceolata
+n11696935	Guinea pepper, negro pepper, Xylopia aethiopica
+n11697560	barberry
+n11697802	American barberry, Berberis canadensis
+n11698042	common barberry, European barberry, Berberis vulgaris
+n11698245	Japanese barberry, Berberis thunbergii
+n11699442	Oregon grape, Oregon holly grape, hollygrape, mountain grape, holly-leaves barberry, Mahonia aquifolium
+n11699751	Oregon grape, Mahonia nervosa
+n11700058	mayapple, May apple, wild mandrake, Podophyllum peltatum
+n11700279	May apple
+n11700864	allspice
+n11701066	Carolina allspice, strawberry shrub, strawberry bush, sweet shrub, Calycanthus floridus
+n11701302	spicebush, California allspice, Calycanthus occidentalis
+n11702713	katsura tree, Cercidiphyllum japonicum
+n11703669	laurel
+n11704093	true laurel, bay, bay laurel, bay tree, Laurus nobilis
+n11704620	camphor tree, Cinnamomum camphora
+n11704791	cinnamon, Ceylon cinnamon, Ceylon cinnamon tree, Cinnamomum zeylanicum
+n11705171	cassia, cassia-bark tree, Cinnamomum cassia
+n11705387	cassia bark, Chinese cinnamon
+n11705573	Saigon cinnamon, Cinnamomum loureirii
+n11705776	cinnamon bark
+n11706325	spicebush, spice bush, American spicebush, Benjamin bush, Lindera benzoin, Benzoin odoriferum
+n11706761	avocado, avocado tree, Persea Americana
+n11706942	laurel-tree, red bay, Persea borbonia
+n11707229	sassafras, sassafras tree, Sassafras albidum
+n11707827	California laurel, California bay tree, Oregon myrtle, pepperwood, spice tree, sassafras laurel, California olive, mountain laurel, Umbellularia californica
+n11708658	anise tree
+n11708857	purple anise, Illicium floridanum
+n11709045	star anise, Illicium anisatum
+n11709205	star anise, Chinese anise, Illicium verum
+n11709674	magnolia
+n11710136	southern magnolia, evergreen magnolia, large-flowering magnolia, bull bay, Magnolia grandiflora
+n11710393	umbrella tree, umbrella magnolia, elkwood, elk-wood, Magnolia tripetala
+n11710658	earleaved umbrella tree, Magnolia fraseri
+n11710827	cucumber tree, Magnolia acuminata
+n11710987	large-leaved magnolia, large-leaved cucumber tree, great-leaved macrophylla, Magnolia macrophylla
+n11711289	saucer magnolia, Chinese magnolia, Magnolia soulangiana
+n11711537	star magnolia, Magnolia stellata
+n11711764	sweet bay, swamp bay, swamp laurel, Magnolia virginiana
+n11711971	manglietia, genus Manglietia
+n11712282	tulip tree, tulip poplar, yellow poplar, canary whitewood, Liriodendron tulipifera
+n11713164	moonseed
+n11713370	common moonseed, Canada moonseed, yellow parilla, Menispermum canadense
+n11713763	Carolina moonseed, Cocculus carolinus
+n11714382	nutmeg, nutmeg tree, Myristica fragrans
+n11715430	water nymph, fragrant water lily, pond lily, Nymphaea odorata
+n11715678	European white lily, Nymphaea alba
+n11716698	southern spatterdock, Nuphar sagittifolium
+n11717399	lotus, Indian lotus, sacred lotus, Nelumbo nucifera
+n11717577	water chinquapin, American lotus, yanquapin, Nelumbo lutea
+n11718296	water-shield, fanwort, Cabomba caroliniana
+n11718681	water-shield, Brasenia schreberi, water-target
+n11719286	peony, paeony
+n11720353	buttercup, butterflower, butter-flower, crowfoot, goldcup, kingcup
+n11720643	meadow buttercup, tall buttercup, tall crowfoot, tall field buttercup, Ranunculus acris
+n11720891	water crowfoot, water buttercup, Ranunculus aquatilis
+n11721337	lesser celandine, pilewort, Ranunculus ficaria
+n11721642	lesser spearwort, Ranunculus flammula
+n11722036	greater spearwort, Ranunculus lingua
+n11722342	western buttercup, Ranunculus occidentalis
+n11722466	creeping buttercup, creeping crowfoot, Ranunculus repens
+n11722621	cursed crowfoot, celery-leaved buttercup, Ranunculus sceleratus
+n11722982	aconite
+n11723227	monkshood, helmetflower, helmet flower, Aconitum napellus
+n11723452	wolfsbane, wolfbane, wolf's bane, Aconitum lycoctonum
+n11723770	baneberry, cohosh, herb Christopher
+n11723986	baneberry
+n11724109	red baneberry, redberry, red-berry, snakeberry, Actaea rubra
+n11724660	pheasant's-eye, Adonis annua
+n11725015	anemone, windflower
+n11725311	Alpine anemone, mountain anemone, Anemone tetonensis
+n11725480	Canada anemone, Anemone Canadensis
+n11725623	thimbleweed, Anemone cylindrica
+n11725821	wood anemone, Anemone nemorosa
+n11725973	wood anemone, snowdrop, Anemone quinquefolia
+n11726145	longheaded thimbleweed, Anemone riparia
+n11726269	snowdrop anemone, snowdrop windflower, Anemone sylvestris
+n11726433	Virginia thimbleweed, Anemone virginiana
+n11726707	rue anemone, Anemonella thalictroides
+n11727091	columbine, aquilegia, aquilege
+n11727358	meeting house, honeysuckle, Aquilegia canadensis
+n11727540	blue columbine, Aquilegia caerulea, Aquilegia scopulorum calcarea
+n11727738	granny's bonnets, Aquilegia vulgaris
+n11728099	marsh marigold, kingcup, meadow bright, May blob, cowslip, water dragon, Caltha palustris
+n11728769	American bugbane, summer cohosh, Cimicifuga americana
+n11728945	black cohosh, black snakeroot, rattle-top, Cimicifuga racemosa
+n11729142	fetid bugbane, foetid bugbane, Cimicifuga foetida
+n11729478	clematis
+n11729860	pine hyacinth, Clematis baldwinii, Viorna baldwinii
+n11730015	blue jasmine, blue jessamine, curly clematis, marsh clematis, Clematis crispa
+n11730458	golden clematis, Clematis tangutica
+n11730602	scarlet clematis, Clematis texensis
+n11730750	leather flower, Clematis versicolor
+n11730933	leather flower, vase-fine, vase vine, Clematis viorna
+n11731157	virgin's bower, old man's beard, devil's darning needle, Clematis virginiana
+n11731659	purple clematis, purple virgin's bower, mountain clematis, Clematis verticillaris
+n11732052	goldthread, golden thread, Coptis groenlandica, Coptis trifolia groenlandica
+n11732567	rocket larkspur, Consolida ambigua, Delphinium ajacis
+n11733054	delphinium
+n11733312	larkspur
+n11733548	winter aconite, Eranthis hyemalis
+n11734493	lenten rose, black hellebore, Helleborus orientalis
+n11734698	green hellebore, Helleborus viridis
+n11735053	hepatica, liverleaf
+n11735570	goldenseal, golden seal, yellow root, turmeric root, Hydrastis Canadensis
+n11735977	false rue anemone, false rue, Isopyrum biternatum
+n11736362	giant buttercup, Laccopetalum giganteum
+n11736694	nigella
+n11736851	love-in-a-mist, Nigella damascena
+n11737009	fennel flower, Nigella hispanica
+n11737125	black caraway, nutmeg flower, Roman coriander, Nigella sativa
+n11737534	pasqueflower, pasque flower
+n11738547	meadow rue
+n11738997	false bugbane, Trautvetteria carolinensis
+n11739365	globeflower, globe flower
+n11739978	winter's bark, winter's bark tree, Drimys winteri
+n11740414	pepper shrub, Pseudowintera colorata, Wintera colorata
+n11741175	sweet gale, Scotch gale, Myrica gale
+n11741350	wax myrtle
+n11741575	bay myrtle, puckerbush, Myrica cerifera
+n11741797	bayberry, candleberry, swamp candleberry, waxberry, Myrica pensylvanica
+n11742310	sweet fern, Comptonia peregrina, Comptonia asplenifolia
+n11742878	corkwood, corkwood tree, Leitneria floridana
+n11744011	jointed rush, Juncus articulatus
+n11744108	toad rush, Juncus bufonius
+n11744471	slender rush, Juncus tenuis
+n11745817	zebrawood, zebrawood tree
+n11746600	Connarus guianensis
+n11747468	legume, leguminous plant
+n11748002	legume
+n11748811	peanut
+n11749112	granadilla tree, granadillo, Brya ebenus
+n11749603	arariba, Centrolobium robustum
+n11750173	tonka bean, coumara nut
+n11750508	courbaril, Hymenaea courbaril
+n11750989	melilotus, melilot, sweet clover
+n11751765	darling pea, poison bush
+n11751974	smooth darling pea, Swainsona galegifolia
+n11752578	clover, trefoil
+n11752798	alpine clover, Trifolium alpinum
+n11752937	hop clover, shamrock, lesser yellow trefoil, Trifolium dubium
+n11753143	crimson clover, Italian clover, Trifolium incarnatum
+n11753355	red clover, purple clover, Trifolium pratense
+n11753562	buffalo clover, Trifolium reflexum, Trifolium stoloniferum
+n11753700	white clover, dutch clover, shamrock, Trifolium repens
+n11754893	mimosa
+n11756092	acacia
+n11756329	shittah, shittah tree
+n11756669	wattle
+n11756870	black wattle, Acacia auriculiformis
+n11757017	gidgee, stinking wattle, Acacia cambegei
+n11757190	catechu, Jerusalem thorn, Acacia catechu
+n11757653	silver wattle, mimosa, Acacia dealbata
+n11757851	huisache, cassie, mimosa bush, sweet wattle, sweet acacia, scented wattle, flame tree, Acacia farnesiana
+n11758122	lightwood, Acacia melanoxylon
+n11758276	golden wattle, Acacia pycnantha
+n11758483	fever tree, Acacia xanthophloea
+n11758799	coralwood, coral-wood, red sandalwood, Barbados pride, peacock flower fence, Adenanthera pavonina
+n11759224	albizzia, albizia
+n11759404	silk tree, Albizia julibrissin, Albizzia julibrissin
+n11759609	siris, siris tree, Albizia lebbeck, Albizzia lebbeck
+n11759853	rain tree, saman, monkeypod, monkey pod, zaman, zamang, Albizia saman
+n11760785	calliandra
+n11761202	conacaste, elephant's ear, Enterolobium cyclocarpa
+n11761650	inga
+n11761836	ice-cream bean, Inga edulis
+n11762018	guama, Inga laurina
+n11762433	lead tree, white popinac, Leucaena glauca, Leucaena leucocephala
+n11762927	wild tamarind, Lysiloma latisiliqua, Lysiloma bahamensis
+n11763142	sabicu, Lysiloma sabicu
+n11763625	nitta tree
+n11763874	Parkia javanica
+n11764478	manila tamarind, camachile, huamachil, wild tamarind, Pithecellobium dulce
+n11764814	cat's-claw, catclaw, black bead, Pithecellodium unguis-cati
+n11765568	honey mesquite, Western honey mesquite, Prosopis glandulosa
+n11766046	algarroba, algarrobilla, algarobilla
+n11766189	screw bean, screwbean, tornillo, screwbean mesquite, Prosopis pubescens
+n11766432	screw bean
+n11767354	dogbane
+n11767877	Indian hemp, rheumatism weed, Apocynum cannabinum
+n11768816	bushman's poison, ordeal tree, Acocanthera oppositifolia, Acocanthera venenata
+n11769176	impala lily, mock azalia, desert rose, kudu lily, Adenium obesum, Adenium multiflorum
+n11769621	allamanda
+n11769803	common allamanda, golden trumpet, Allamanda cathartica
+n11770256	dita, dita bark, devil tree, Alstonia scholaris
+n11771147	Nepal trumpet flower, Easter lily vine, Beaumontia grandiflora
+n11771539	carissa
+n11771746	hedge thorn, natal plum, Carissa bispinosa
+n11771924	natal plum, amatungulu, Carissa macrocarpa, Carissa grandiflora
+n11772408	periwinkle, rose periwinkle, Madagascar periwinkle, old maid, Cape periwinkle, red periwinkle, cayenne jasmine, Catharanthus roseus, Vinca rosea
+n11772879	ivory tree, conessi, kurchi, kurchee, Holarrhena pubescens, Holarrhena antidysenterica
+n11773408	white dipladenia, Mandevilla boliviensis, Dipladenia boliviensis
+n11773628	Chilean jasmine, Mandevilla laxa
+n11773987	oleander, rose bay, Nerium oleander
+n11774513	frangipani, frangipanni
+n11774972	West Indian jasmine, pagoda tree, Plumeria alba
+n11775340	rauwolfia, rauvolfia
+n11775626	snakewood, Rauwolfia serpentina
+n11776234	Strophanthus kombe
+n11777080	yellow oleander, Thevetia peruviana, Thevetia neriifolia
+n11778092	myrtle, Vinca minor
+n11778257	large periwinkle, Vinca major
+n11779300	arum, aroid
+n11780148	cuckoopint, lords-and-ladies, jack-in-the-pulpit, Arum maculatum
+n11780424	black calla, Arum palaestinum
+n11781176	calamus
+n11782036	alocasia, elephant's ear, elephant ear
+n11782266	giant taro, Alocasia macrorrhiza
+n11782761	amorphophallus
+n11782878	pungapung, telingo potato, elephant yam, Amorphophallus paeonifolius, Amorphophallus campanulatus
+n11783162	devil's tongue, snake palm, umbrella arum, Amorphophallus rivieri
+n11783920	anthurium, tailflower, tail-flower
+n11784126	flamingo flower, flamingo plant, Anthurium andraeanum, Anthurium scherzerianum
+n11784497	jack-in-the-pulpit, Indian turnip, wake-robin, Arisaema triphyllum, Arisaema atrorubens
+n11785276	friar's-cowl, Arisarum vulgare
+n11785668	caladium
+n11785875	Caladium bicolor
+n11786131	wild calla, water arum, Calla palustris
+n11786539	taro, taro plant, dalo, dasheen, Colocasia esculenta
+n11786843	taro, cocoyam, dasheen, eddo
+n11787190	cryptocoryne, water trumpet
+n11788039	dracontium
+n11788727	golden pothos, pothos, ivy arum, Epipremnum aureum, Scindapsus aureus
+n11789066	skunk cabbage, Lysichiton americanum
+n11789438	monstera
+n11789589	ceriman, Monstera deliciosa
+n11789962	nephthytis
+n11790089	Nephthytis afzelii
+n11790788	arrow arum
+n11790936	green arrow arum, tuckahoe, Peltandra virginica
+n11791341	philodendron
+n11791569	pistia, water lettuce, water cabbage, Pistia stratiotes, Pistia stratoites
+n11792029	pothos
+n11792341	spathiphyllum, peace lily, spathe flower
+n11792742	skunk cabbage, polecat weed, foetid pothos, Symplocarpus foetidus
+n11793403	yautia, tannia, spoonflower, malanga, Xanthosoma sagittifolium, Xanthosoma atrovirens
+n11793779	calla lily, calla, arum lily, Zantedeschia aethiopica
+n11794024	pink calla, Zantedeschia rehmanii
+n11794139	golden calla
+n11794519	duckweed
+n11795049	common duckweed, lesser duckweed, Lemna minor
+n11795216	star-duckweed, Lemna trisulca
+n11795580	great duckweed, water flaxseed, Spirodela polyrrhiza
+n11796005	watermeal
+n11796188	common wolffia, Wolffia columbiana
+n11797321	aralia
+n11797508	American angelica tree, devil's walking stick, Hercules'-club, Aralia spinosa
+n11797981	American spikenard, petty morel, life-of-man, Aralia racemosa
+n11798270	bristly sarsaparilla, bristly sarsparilla, dwarf elder, Aralia hispida
+n11798496	Japanese angelica tree, Aralia elata
+n11798688	Chinese angelica, Chinese angelica tree, Aralia stipulata
+n11798978	ivy, common ivy, English ivy, Hedera helix
+n11799331	puka, Meryta sinclairii
+n11799732	ginseng, nin-sin, Panax ginseng, Panax schinseng, Panax pseudoginseng
+n11800236	ginseng
+n11800565	umbrella tree, Schefflera actinophylla, Brassaia actinophylla
+n11801392	birthwort, Aristolochia clematitis
+n11801665	Dutchman's-pipe, pipe vine, Aristolochia macrophylla, Aristolochia durior
+n11801891	Virginia snakeroot, Virginia serpentaria, Virginia serpentary, Aristolochia serpentaria
+n11802410	Canada ginger, black snakeroot, Asarum canadense
+n11802586	heartleaf, heart-leaf, Asarum virginicum
+n11802800	heartleaf, heart-leaf, Asarum shuttleworthii
+n11802995	asarabacca, Asarum europaeum
+n11805255	caryophyllaceous plant
+n11805544	corn cockle, corn campion, crown-of-the-field, Agrostemma githago
+n11805956	sandwort
+n11806219	mountain sandwort, mountain starwort, mountain daisy, Arenaria groenlandica
+n11806369	pine-barren sandwort, longroot, Arenaria caroliniana
+n11806521	seabeach sandwort, Arenaria peploides
+n11806679	rock sandwort, Arenaria stricta
+n11806814	thyme-leaved sandwort, Arenaria serpyllifolia
+n11807108	mouse-ear chickweed, mouse eared chickweed, mouse ear, clammy chickweed, chickweed
+n11807525	snow-in-summer, love-in-a-mist, Cerastium tomentosum
+n11807696	Alpine mouse-ear, Arctic mouse-ear, Cerastium alpinum
+n11807979	pink, garden pink
+n11808299	sweet William, Dianthus barbatus
+n11808468	carnation, clove pink, gillyflower, Dianthus caryophyllus
+n11808721	china pink, rainbow pink, Dianthus chinensis
+n11808932	Japanese pink, Dianthus chinensis heddewigii
+n11809094	maiden pink, Dianthus deltoides
+n11809271	cheddar pink, Diangus gratianopolitanus
+n11809437	button pink, Dianthus latifolius
+n11809594	cottage pink, grass pink, Dianthus plumarius
+n11809754	fringed pink, Dianthus supurbus
+n11810030	drypis
+n11810358	baby's breath, babies'-breath, Gypsophila paniculata
+n11811059	coral necklace, Illecebrum verticullatum
+n11811473	lychnis, catchfly
+n11811706	ragged robin, cuckoo flower, Lychnis flos-cuculi, Lychins floscuculi
+n11811921	scarlet lychnis, maltese cross, Lychins chalcedonica
+n11812094	mullein pink, rose campion, gardener's delight, dusty miller, Lychnis coronaria
+n11812910	sandwort, Moehringia lateriflora
+n11813077	sandwort, Moehringia mucosa
+n11814584	soapwort, hedge pink, bouncing Bet, bouncing Bess, Saponaria officinalis
+n11814996	knawel, knawe, Scleranthus annuus
+n11815491	silene, campion, catchfly
+n11815721	moss campion, Silene acaulis
+n11815918	wild pink, Silene caroliniana
+n11816121	red campion, red bird's eye, Silene dioica, Lychnis dioica
+n11816336	white campion, evening lychnis, white cockle, bladder campion, Silene latifolia, Lychnis alba
+n11816649	fire pink, Silene virginica
+n11816829	bladder campion, Silene uniflora, Silene vulgaris
+n11817160	corn spurry, corn spurrey, Spergula arvensis
+n11817501	sand spurry, sea spurry, Spergularia rubra
+n11817914	chickweed
+n11818069	common chickweed, Stellaria media
+n11818636	cowherb, cow cockle, Vaccaria hispanica, Vaccaria pyramidata, Saponaria vaccaria
+n11819509	Hottentot fig, Hottentot's fig, sour fig, Carpobrotus edulis, Mesembryanthemum edule
+n11819912	livingstone daisy, Dorotheanthus bellidiformis
+n11820965	fig marigold, pebble plant
+n11821184	ice plant, icicle plant, Mesembryanthemum crystallinum
+n11822300	New Zealand spinach, Tetragonia tetragonioides, Tetragonia expansa
+n11823043	amaranth
+n11823305	amaranth
+n11823436	tumbleweed, Amaranthus albus, Amaranthus graecizans
+n11823756	prince's-feather, gentleman's-cane, prince's-plume, red amaranth, purple amaranth, Amaranthus cruentus, Amaranthus hybridus hypochondriacus, Amaranthus hybridus erythrostachys
+n11824146	pigweed, Amaranthus hypochondriacus
+n11824344	thorny amaranth, Amaranthus spinosus
+n11824747	alligator weed, alligator grass, Alternanthera philoxeroides
+n11825351	cockscomb, common cockscomb, Celosia cristata, Celosia argentea cristata
+n11825749	cottonweed
+n11826198	globe amaranth, bachelor's button, Gomphrena globosa
+n11826569	bloodleaf
+n11827541	saltwort, Batis maritima
+n11828577	lamb's-quarters, pigweed, wild spinach, Chenopodium album
+n11828973	good-king-henry, allgood, fat hen, wild spinach, Chenopodium bonus-henricus
+n11829205	Jerusalem oak, feather geranium, Mexican tea, Chenopodium botrys, Atriplex mexicana
+n11829672	oak-leaved goosefoot, oakleaf goosefoot, Chenopodium glaucum
+n11829922	sowbane, red goosefoot, Chenopodium hybridum
+n11830045	nettle-leaved goosefoot, nettleleaf goosefoot, Chenopodium murale
+n11830252	red goosefoot, French spinach, Chenopodium rubrum
+n11830400	stinking goosefoot, Chenopodium vulvaria
+n11830714	orach, orache
+n11830906	saltbush
+n11831100	garden orache, mountain spinach, Atriplex hortensis
+n11831297	desert holly, Atriplex hymenelytra
+n11831521	quail bush, quail brush, white thistle, Atriplex lentiformis
+n11832214	beet, common beet, Beta vulgaris
+n11832480	beetroot, Beta vulgaris rubra
+n11832671	chard, Swiss chard, spinach beet, leaf beet, chard plant, Beta vulgaris cicla
+n11832899	mangel-wurzel, mangold-wurzel, mangold, Beta vulgaris vulgaris
+n11833373	winged pigweed, tumbleweed, Cycloloma atriplicifolium
+n11833749	halogeton, Halogeton glomeratus
+n11834272	glasswort, samphire, Salicornia europaea
+n11834654	saltwort, barilla, glasswort, kali, kelpwort, Salsola kali, Salsola soda
+n11834890	Russian thistle, Russian tumbleweed, Russian cactus, tumbleweed, Salsola kali tenuifolia
+n11835251	greasewood, black greasewood, Sarcobatus vermiculatus
+n11836327	scarlet musk flower, Nyctaginia capitata
+n11836722	sand verbena
+n11837204	sweet sand verbena, Abronia fragrans
+n11837351	yellow sand verbena, Abronia latifolia
+n11837562	beach pancake, Abronia maritima
+n11837743	beach sand verbena, pink sand verbena, Abronia umbellata
+n11837970	desert sand verbena, Abronia villosa
+n11838413	trailing four o'clock, trailing windmills, Allionia incarnata
+n11838916	bougainvillea
+n11839460	umbrellawort
+n11839568	four o'clock
+n11839823	common four-o'clock, marvel-of-Peru, Mirabilis jalapa, Mirabilis uniflora
+n11840067	California four o'clock, Mirabilis laevis, Mirabilis californica
+n11840246	sweet four o'clock, maravilla, Mirabilis longiflora
+n11840476	desert four o'clock, Colorado four o'clock, maravilla, Mirabilis multiflora
+n11840764	mountain four o'clock, Mirabilis oblongifolia
+n11841247	cockspur, Pisonia aculeata
+n11843441	rattail cactus, rat's-tail cactus, Aporocactus flagelliformis
+n11844371	saguaro, sahuaro, Carnegiea gigantea
+n11844892	night-blooming cereus
+n11845557	echinocactus, barrel cactus
+n11845793	hedgehog cactus
+n11845913	golden barrel cactus, Echinocactus grusonii
+n11846312	hedgehog cereus
+n11846425	rainbow cactus
+n11846765	epiphyllum, orchid cactus
+n11847169	barrel cactus
+n11848479	night-blooming cereus
+n11848867	chichipe, Lemaireocereus chichipe
+n11849271	mescal, mezcal, peyote, Lophophora williamsii
+n11849467	mescal button, sacred mushroom, magic mushroom
+n11849871	mammillaria
+n11849983	feather ball, Mammillaria plumosa
+n11850521	garambulla, garambulla cactus, Myrtillocactus geometrizans
+n11850918	Knowlton's cactus, Pediocactus knowltonii
+n11851258	nopal
+n11851578	prickly pear, prickly pear cactus
+n11851839	cholla, Opuntia cholla
+n11852028	nopal, Opuntia lindheimeri
+n11852148	tuna, Opuntia tuna
+n11852531	Barbados gooseberry, Barbados-gooseberry vine, Pereskia aculeata
+n11853079	mistletoe cactus
+n11853356	Christmas cactus, Schlumbergera buckleyi, Schlumbergera baridgesii
+n11853813	night-blooming cereus
+n11854479	crab cactus, Thanksgiving cactus, Zygocactus truncatus, Schlumbergera truncatus
+n11855274	pokeweed
+n11855435	Indian poke, Phytolacca acinosa
+n11855553	poke, pigeon berry, garget, scoke, Phytolacca americana
+n11855842	ombu, bella sombra, Phytolacca dioica
+n11856573	bloodberry, blood berry, rougeberry, rouge plant, Rivina humilis
+n11857696	portulaca
+n11857875	rose moss, sun plant, Portulaca grandiflora
+n11858077	common purslane, pussley, pussly, verdolagas, Portulaca oleracea
+n11858703	rock purslane
+n11858814	red maids, redmaids, Calandrinia ciliata
+n11859275	Carolina spring beauty, Claytonia caroliniana
+n11859472	spring beauty, Clatonia lanceolata
+n11859737	Virginia spring beauty, Claytonia virginica
+n11860208	siskiyou lewisia, Lewisia cotyledon
+n11860555	bitterroot, Lewisia rediviva
+n11861238	broad-leaved montia, Montia cordifolia
+n11861487	blinks, blinking chickweed, water chickweed, Montia lamprosperma
+n11861641	toad lily, Montia chamissoi
+n11861853	winter purslane, miner's lettuce, Cuban spinach, Montia perfoliata
+n11862835	flame flower, flame-flower, flameflower, Talinum aurantiacum
+n11863467	pigmy talinum, Talinum brevifolium
+n11863877	jewels-of-opar, Talinum paniculatum
+n11865071	caper
+n11865276	native pomegranate, Capparis arborea
+n11865429	caper tree, Jamaica caper tree, Capparis cynophallophora
+n11865574	caper tree, bay-leaved caper, Capparis flexuosa
+n11865874	common caper, Capparis spinosa
+n11866248	spiderflower, cleome
+n11866706	Rocky Mountain bee plant, stinking clover, Cleome serrulata
+n11867311	clammyweed, Polanisia graveolens, Polanisia dodecandra
+n11868814	crucifer, cruciferous plant
+n11869351	cress, cress plant
+n11869689	watercress
+n11870044	stonecress, stone cress
+n11870418	garlic mustard, hedge garlic, sauce-alone, jack-by-the-hedge, Alliaria officinalis
+n11870747	alyssum, madwort
+n11871059	rose of Jericho, resurrection plant, Anastatica hierochuntica
+n11871496	Arabidopsis thaliana, mouse-ear cress
+n11871748	Arabidopsis lyrata
+n11872146	rock cress, rockcress
+n11872324	sicklepod, Arabis Canadensis
+n11872658	tower mustard, tower cress, Turritis glabra, Arabis glabra
+n11873182	horseradish, horseradish root
+n11873612	winter cress, St. Barbara's herb, scurvy grass
+n11874081	yellow rocket, rockcress, rocket cress, Barbarea vulgaris, Sisymbrium barbarea
+n11874423	hoary alison, hoary alyssum, Berteroa incana
+n11874878	buckler mustard, Biscutalla laevigata
+n11875523	wild cabbage, Brassica oleracea
+n11875691	cabbage, cultivated cabbage, Brassica oleracea
+n11875938	head cabbage, head cabbage plant, Brassica oleracea capitata
+n11876204	savoy cabbage
+n11876432	brussels sprout, Brassica oleracea gemmifera
+n11876634	cauliflower, Brassica oleracea botrytis
+n11876803	broccoli, Brassica oleracea italica
+n11877193	collard
+n11877283	kohlrabi, Brassica oleracea gongylodes
+n11877473	turnip plant
+n11877646	turnip, white turnip, Brassica rapa
+n11877860	rutabaga, turnip cabbage, swede, Swedish turnip, rutabaga plant, Brassica napus napobrassica
+n11878101	broccoli raab, broccoli rabe, Brassica rapa ruvo
+n11878283	mustard
+n11878633	chinese mustard, indian mustard, leaf mustard, gai choi, Brassica juncea
+n11879054	bok choy, bok choi, pakchoi, pak choi, Chinese white cabbage, Brassica rapa chinensis
+n11879722	rape, colza, Brassica napus
+n11879895	rapeseed
+n11881189	shepherd's purse, shepherd's pouch, Capsella bursa-pastoris
+n11882074	lady's smock, cuckooflower, cuckoo flower, meadow cress, Cardamine pratensis
+n11882237	coral-root bittercress, coralroot, coralwort, Cardamine bulbifera, Dentaria bulbifera
+n11882426	crinkleroot, crinkle-root, crinkle root, pepper root, toothwort, Cardamine diphylla, Dentaria diphylla
+n11882636	American watercress, mountain watercress, Cardamine rotundifolia
+n11882821	spring cress, Cardamine bulbosa
+n11882972	purple cress, Cardamine douglasii
+n11883328	wallflower, Cheiranthus cheiri, Erysimum cheiri
+n11883628	prairie rocket
+n11883945	scurvy grass, common scurvy grass, Cochlearia officinalis
+n11884384	sea kale, sea cole, Crambe maritima
+n11884967	tansy mustard, Descurainia pinnata
+n11885856	draba
+n11887119	wallflower
+n11887310	prairie rocket
+n11887476	Siberian wall flower, Erysimum allionii, Cheiranthus allionii
+n11887750	western wall flower, Erysimum asperum, Cheiranthus asperus, Erysimum arkansanum
+n11888061	wormseed mustard, Erysimum cheiranthoides
+n11888424	heliophila
+n11888800	damask violet, Dame's violet, sweet rocket, Hesperis matronalis
+n11889205	tansy-leaved rocket, Hugueninia tanacetifolia, Sisymbrium tanacetifolia
+n11889619	candytuft
+n11890022	woad
+n11890150	dyer's woad, Isatis tinctoria
+n11890884	bladderpod
+n11891175	sweet alyssum, sweet alison, Lobularia maritima
+n11892029	Malcolm stock, stock
+n11892181	Virginian stock, Virginia stock, Malcolmia maritima
+n11892637	stock, gillyflower
+n11892817	brompton stock, Matthiola incana
+n11893640	bladderpod
+n11893916	chamois cress, Pritzelago alpina, Lepidium alpina
+n11894327	radish plant, radish
+n11894558	jointed charlock, wild radish, wild rape, runch, Raphanus raphanistrum
+n11894770	radish, Raphanus sativus
+n11895092	radish, daikon, Japanese radish, Raphanus sativus longipinnatus
+n11895472	marsh cress, yellow watercress, Rorippa islandica
+n11895714	great yellowcress, Rorippa amphibia, Nasturtium amphibium
+n11896141	schizopetalon, Schizopetalon walkeri
+n11896722	field mustard, wild mustard, charlock, chadlock, Brassica kaber, Sinapis arvensis
+n11897116	hedge mustard, Sisymbrium officinale
+n11897466	desert plume, prince's-plume, Stanleya pinnata, Cleome pinnata
+n11898639	pennycress
+n11898775	field pennycress, French weed, fanweed, penny grass, stinkweed, mithridate mustard, Thlaspi arvense
+n11899223	fringepod, lacepod
+n11899762	bladderpod
+n11899921	wasabi
+n11900569	poppy
+n11901294	Iceland poppy, Papaver alpinum
+n11901452	western poppy, Papaver californicum
+n11901597	prickly poppy, Papaver argemone
+n11901759	Iceland poppy, arctic poppy, Papaver nudicaule
+n11901977	oriental poppy, Papaver orientale
+n11902200	corn poppy, field poppy, Flanders poppy, Papaver rhoeas
+n11902389	opium poppy, Papaver somniferum
+n11902709	prickly poppy, argemone, white thistle, devil's fig
+n11902982	Mexican poppy, Argemone mexicana
+n11903333	bocconia, tree celandine, Bocconia frutescens
+n11903671	celandine, greater celandine, swallowwort, swallow wort, Chelidonium majus
+n11904109	corydalis
+n11904274	climbing corydalis, Corydalis claviculata, Fumaria claviculata
+n11905392	California poppy, Eschscholtzia californica
+n11905749	horn poppy, horned poppy, yellow horned poppy, sea poppy, Glaucium flavum
+n11906127	golden cup, Mexican tulip poppy, Hunnemania fumariifolia
+n11906514	plume poppy, bocconia, Macleaya cordata
+n11906917	blue poppy, Meconopsis betonicifolia
+n11907100	Welsh poppy, Meconopsis cambrica
+n11907405	creamcups, Platystemon californicus
+n11907689	matilija poppy, California tree poppy, Romneya coulteri
+n11908549	wind poppy, flaming poppy, Stylomecon heterophyllum, Papaver heterophyllum
+n11908846	celandine poppy, wood poppy, Stylophorum diphyllum
+n11909864	climbing fumitory, Allegheny vine, Adlumia fungosa, Fumaria fungosa
+n11910271	bleeding heart, lyreflower, lyre-flower, Dicentra spectabilis
+n11910460	Dutchman's breeches, Dicentra cucullaria
+n11910666	squirrel corn, Dicentra canadensis
+n11915214	composite, composite plant
+n11915658	compass plant, compass flower
+n11915899	everlasting, everlasting flower
+n11916467	achillea
+n11916696	yarrow, milfoil, Achillea millefolium
+n11917407	pink-and-white everlasting, pink paper daisy, Acroclinium roseum
+n11917835	white snakeroot, white sanicle, Ageratina altissima, Eupatorium rugosum
+n11918286	ageratum
+n11918473	common ageratum, Ageratum houstonianum
+n11918808	sweet sultan, Amberboa moschata, Centaurea moschata
+n11919447	ragweed, ambrosia, bitterweed
+n11919761	common ragweed, Ambrosia artemisiifolia
+n11919975	great ragweed, Ambrosia trifida
+n11920133	western ragweed, perennial ragweed, Ambrosia psilostachya
+n11920498	ammobium
+n11920663	winged everlasting, Ammobium alatum
+n11920998	pellitory, pellitory-of-Spain, Anacyclus pyrethrum
+n11921395	pearly everlasting, cottonweed, Anaphalis margaritacea
+n11921792	andryala
+n11922661	plantain-leaved pussytoes
+n11922755	field pussytoes
+n11922839	solitary pussytoes
+n11922926	mountain everlasting
+n11923174	mayweed, dog fennel, stinking mayweed, stinking chamomile, Anthemis cotula
+n11923397	yellow chamomile, golden marguerite, dyers' chamomile, Anthemis tinctoria
+n11923637	corn chamomile, field chamomile, corn mayweed, Anthemis arvensis
+n11924014	woolly daisy, dwarf daisy, Antheropeas wallacei, Eriophyllum wallacei
+n11924445	burdock, clotbur
+n11924849	great burdock, greater burdock, cocklebur, Arctium lappa
+n11925303	African daisy
+n11925450	blue-eyed African daisy, Arctotis stoechadifolia, Arctotis venusta
+n11925898	marguerite, marguerite daisy, Paris daisy, Chrysanthemum frutescens, Argyranthemum frutescens
+n11926365	silversword, Argyroxiphium sandwicense
+n11926833	arnica
+n11926976	heartleaf arnica, Arnica cordifolia
+n11927215	Arnica montana
+n11927740	lamb succory, dwarf nipplewort, Arnoseris minima
+n11928352	artemisia
+n11928858	mugwort
+n11929743	sweet wormwood, Artemisia annua
+n11930038	field wormwood, Artemisia campestris
+n11930203	tarragon, estragon, Artemisia dracunculus
+n11930353	sand sage, silvery wormwood, Artemisia filifolia
+n11930571	wormwood sage, prairie sagewort, Artemisia frigida
+n11930788	western mugwort, white sage, cudweed, prairie sage, Artemisia ludoviciana, Artemisia gnaphalodes
+n11930994	Roman wormwood, Artemis pontica
+n11931135	bud brush, bud sagebrush, Artemis spinescens
+n11931540	common mugwort, Artemisia vulgaris
+n11931918	aster
+n11932745	wood aster
+n11932927	whorled aster, Aster acuminatus
+n11933099	heath aster, Aster arenosus
+n11933257	heart-leaved aster, Aster cordifolius
+n11933387	white wood aster, Aster divaricatus
+n11933546	bushy aster, Aster dumosus
+n11933728	heath aster, Aster ericoides
+n11933903	white prairie aster, Aster falcatus
+n11934041	stiff aster, Aster linarifolius
+n11934239	goldilocks, goldilocks aster, Aster linosyris, Linosyris vulgaris
+n11934463	large-leaved aster, Aster macrophyllus
+n11934616	New England aster, Aster novae-angliae
+n11934807	Michaelmas daisy, New York aster, Aster novi-belgii
+n11935027	upland white aster, Aster ptarmicoides
+n11935187	Short's aster, Aster shortii
+n11935330	sea aster, sea starwort, Aster tripolium
+n11935469	prairie aster, Aster turbinellis
+n11935627	annual salt-marsh aster
+n11935715	aromatic aster
+n11935794	arrow leaved aster
+n11935877	azure aster
+n11935953	bog aster
+n11936027	crooked-stemmed aster
+n11936113	Eastern silvery aster
+n11936199	flat-topped white aster
+n11936287	late purple aster
+n11936369	panicled aster
+n11936448	perennial salt marsh aster
+n11936539	purple-stemmed aster
+n11936624	rough-leaved aster
+n11936707	rush aster
+n11936782	Schreiber's aster
+n11936864	small white aster
+n11936946	smooth aster
+n11937023	southern aster
+n11937102	starved aster, calico aster
+n11937195	tradescant's aster
+n11937278	wavy-leaved aster
+n11937360	Western silvery aster
+n11937446	willow aster
+n11937692	ayapana, Ayapana triplinervis, Eupatorium aya-pana
+n11938556	mule fat, Baccharis viminea
+n11939180	balsamroot
+n11939491	daisy
+n11939699	common daisy, English daisy, Bellis perennis
+n11940006	bur marigold, burr marigold, beggar-ticks, beggar's-ticks, sticktight
+n11940349	Spanish needles, Bidens bipinnata
+n11940599	tickseed sunflower, Bidens coronata, Bidens trichosperma
+n11940750	European beggar-ticks, trifid beggar-ticks, trifid bur marigold, Bidens tripartita
+n11941094	slender knapweed
+n11941478	false chamomile
+n11941924	Swan River daisy, Brachycome Iberidifolia
+n11942659	woodland oxeye, Buphthalmum salicifolium
+n11943133	Indian plantain
+n11943407	calendula
+n11943660	common marigold, pot marigold, ruddles, Scotch marigold, Calendula officinalis
+n11943992	China aster, Callistephus chinensis
+n11944196	thistle
+n11944751	welted thistle, Carduus crispus
+n11944954	musk thistle, nodding thistle, Carduus nutans
+n11945367	carline thistle
+n11945514	stemless carline thistle, Carlina acaulis
+n11945783	common carline thistle, Carlina vulgaris
+n11946051	safflower, false saffron, Carthamus tinctorius
+n11946313	safflower seed
+n11946727	catananche
+n11946918	blue succory, cupid's dart, Catananche caerulea
+n11947251	centaury
+n11947629	dusty miller, Centaurea cineraria, Centaurea gymnocarpa
+n11947802	cornflower, bachelor's button, bluebottle, Centaurea cyanus
+n11948044	star-thistle, caltrop, Centauria calcitrapa
+n11948264	knapweed
+n11948469	sweet sultan, Centaurea imperialis
+n11948864	great knapweed, greater knapweed, Centaurea scabiosa
+n11949015	Barnaby's thistle, yellow star-thistle, Centaurea solstitialis
+n11949402	chamomile, camomile, Chamaemelum nobilis, Anthemis nobilis
+n11949857	chaenactis
+n11950345	chrysanthemum
+n11950686	corn marigold, field marigold, Chrysanthemum segetum
+n11950877	crown daisy, Chrysanthemum coronarium
+n11951052	chop-suey greens, tong ho, shun giku, Chrysanthemum coronarium spatiosum
+n11951511	golden aster
+n11951820	Maryland golden aster, Chrysopsis mariana
+n11952346	goldenbush
+n11952541	rabbit brush, rabbit bush, Chrysothamnus nauseosus
+n11953038	chicory, succory, chicory plant, Cichorium intybus
+n11953339	endive, witloof, Cichorium endivia
+n11953610	chicory, chicory root
+n11953884	plume thistle, plumed thistle
+n11954161	Canada thistle, creeping thistle, Cirsium arvense
+n11954345	field thistle, Cirsium discolor
+n11954484	woolly thistle, Cirsium flodmanii
+n11954642	European woolly thistle, Cirsium eriophorum
+n11954798	melancholy thistle, Cirsium heterophylum, Cirsium helenioides
+n11955040	brook thistle, Cirsium rivulare
+n11955153	bull thistle, boar thistle, spear thistle, Cirsium vulgare, Cirsium lanceolatum
+n11955532	blessed thistle, sweet sultan, Cnicus benedictus
+n11955896	mistflower, mist-flower, ageratum, Conoclinium coelestinum, Eupatorium coelestinum
+n11956348	horseweed, Canadian fleabane, fleabane, Conyza canadensis, Erigeron canadensis
+n11956850	coreopsis, tickseed, tickweed, tick-weed
+n11957317	giant coreopsis, Coreopsis gigantea
+n11957514	sea dahlia, Coreopsis maritima
+n11957678	calliopsis, Coreopsis tinctoria
+n11958080	cosmos, cosmea
+n11958499	brass buttons, Cotula coronopifolia
+n11958888	billy buttons
+n11959259	hawk's-beard, hawk's-beards
+n11959632	artichoke, globe artichoke, artichoke plant, Cynara scolymus
+n11959862	cardoon, Cynara cardunculus
+n11960245	dahlia, Dahlia pinnata
+n11960673	German ivy, Delairea odorata, Senecio milkanioides
+n11961100	florist's chrysanthemum, florists' chrysanthemum, mum, Dendranthema grandifloruom, Chrysanthemum morifolium
+n11961446	cape marigold, sun marigold, star of the veldt
+n11961871	leopard's-bane, leopardbane
+n11962272	coneflower
+n11962667	globe thistle
+n11962994	elephant's-foot
+n11963572	tassel flower, Emilia sagitta
+n11963932	brittlebush, brittle bush, incienso, Encelia farinosa
+n11964446	sunray, Enceliopsis nudicaulis
+n11964848	engelmannia
+n11965218	fireweed, Erechtites hieracifolia
+n11965627	fleabane
+n11965962	blue fleabane, Erigeron acer
+n11966083	daisy fleabane, Erigeron annuus
+n11966215	orange daisy, orange fleabane, Erigeron aurantiacus
+n11966385	spreading fleabane, Erigeron divergens
+n11966617	seaside daisy, beach aster, Erigeron glaucous
+n11966896	Philadelphia fleabane, Erigeron philadelphicus
+n11967142	robin's plantain, Erigeron pulchellus
+n11967315	showy daisy, Erigeron speciosus
+n11967744	woolly sunflower
+n11967878	golden yarrow, Eriophyllum lanatum
+n11968519	dog fennel, Eupatorium capillifolium
+n11968704	Joe-Pye weed, spotted Joe-Pye weed, Eupatorium maculatum
+n11968931	boneset, agueweed, thoroughwort, Eupatorium perfoliatum
+n11969166	Joe-Pye weed, purple boneset, trumpet weed, marsh milkweed, Eupatorium purpureum
+n11969607	blue daisy, blue marguerite, Felicia amelloides
+n11969806	kingfisher daisy, Felicia bergeriana
+n11970101	cotton rose, cudweed, filago
+n11970298	herba impia, Filago germanica
+n11970586	gaillardia
+n11971248	gazania
+n11971406	treasure flower, Gazania rigens
+n11971783	African daisy
+n11971927	Barberton daisy, Transvaal daisy, Gerbera jamesonii
+n11972291	desert sunflower, Gerea canescens
+n11972759	cudweed
+n11972959	chafeweed, wood cudweed, Gnaphalium sylvaticum
+n11973341	gumweed, gum plant, tarweed, rosinweed
+n11973634	Grindelia robusta
+n11973749	curlycup gumweed, Grindelia squarrosa
+n11974373	little-head snakeweed, Gutierrezia microcephala
+n11974557	rabbitweed, rabbit-weed, snakeweed, broom snakeweed, broom snakeroot, turpentine weed, Gutierrezia sarothrae
+n11974888	broomweed, broom-weed, Gutierrezia texana
+n11975254	velvet plant, purple velvet plant, royal velvet plant, Gynura aurantiaca
+n11976170	goldenbush
+n11976314	camphor daisy, Haplopappus phyllocephalus
+n11976511	yellow spiny daisy, Haplopappus spinulosus
+n11976933	hoary golden bush, Hazardia cana
+n11977303	sneezeweed
+n11977660	orange sneezeweed, owlclaws, Helenium hoopesii
+n11977887	rosilla, Helenium puberulum
+n11978233	sunflower, helianthus
+n11978551	swamp sunflower, Helianthus angustifolius
+n11978713	common sunflower, mirasol, Helianthus annuus
+n11978961	giant sunflower, tall sunflower, Indian potato, Helianthus giganteus
+n11979187	showy sunflower, Helianthus laetiflorus
+n11979354	Maximilian's sunflower, Helianthus maximilianii
+n11979527	prairie sunflower, Helianthus petiolaris
+n11979715	Jerusalem artichoke, girasol, Jerusalem artichoke sunflower, Helianthus tuberosus
+n11979964	Jerusalem artichoke
+n11980318	strawflower, golden everlasting, yellow paper daisy, Helichrysum bracteatum
+n11980682	heliopsis, oxeye
+n11981192	strawflower
+n11981475	hairy golden aster, prairie golden aster, Heterotheca villosa, Chrysopsis villosa
+n11982115	hawkweed
+n11982545	rattlesnake weed, Hieracium venosum
+n11982939	alpine coltsfoot, Homogyne alpina, Tussilago alpina
+n11983375	alpine gold, alpine hulsea, Hulsea algida
+n11983606	dwarf hulsea, Hulsea nana
+n11984144	cat's-ear, California dandelion, capeweed, gosmore, Hypochaeris radicata
+n11984542	inula
+n11985053	marsh elder, iva
+n11985321	burweed marsh elder, false ragweed, Iva xanthifolia
+n11985739	krigia
+n11985903	dwarf dandelion, Krigia dandelion, Krigia bulbosa
+n11986511	garden lettuce, common lettuce, Lactuca sativa
+n11986729	cos lettuce, romaine lettuce, Lactuca sativa longifolia
+n11987126	leaf lettuce, Lactuca sativa crispa
+n11987349	celtuce, stem lettuce, Lactuca sativa asparagina
+n11987511	prickly lettuce, horse thistle, Lactuca serriola, Lactuca scariola
+n11988132	goldfields, Lasthenia chrysostoma
+n11988596	tidytips, tidy tips, Layia platyglossa
+n11988893	hawkbit
+n11989087	fall dandelion, arnica bud, Leontodon autumnalis
+n11989393	edelweiss, Leontopodium alpinum
+n11989869	oxeye daisy, ox-eyed daisy, marguerite, moon daisy, white daisy, Leucanthemum vulgare, Chrysanthemum leucanthemum
+n11990167	oxeye daisy, Leucanthemum maximum, Chrysanthemum maximum
+n11990313	shasta daisy, Leucanthemum superbum, Chrysanthemum maximum maximum
+n11990627	Pyrenees daisy, Leucanthemum lacustre, Chrysanthemum lacustre
+n11990920	north island edelweiss, Leucogenes leontopodium
+n11991263	blazing star, button snakeroot, gayfeather, gay-feather, snakeroot
+n11991549	dotted gayfeather, Liatris punctata
+n11991777	dense blazing star, Liatris pycnostachya
+n11992479	Texas star, Lindheimera texana
+n11992806	African daisy, yellow ageratum, Lonas inodora, Lonas annua
+n11993203	tahoka daisy, tansy leaf aster, Machaeranthera tanacetifolia
+n11993444	sticky aster, Machaeranthera bigelovii
+n11993675	Mojave aster, Machaeranthera tortifoloia
+n11994150	tarweed
+n11995092	sweet false chamomile, wild chamomile, German chamomile, Matricaria recutita, Matricaria chamomilla
+n11995396	pineapple weed, rayless chamomile, Matricaria matricarioides
+n11996251	climbing hempweed, climbing boneset, wild climbing hempweed, climbing hemp-vine, Mikania scandens
+n11996677	mutisia
+n11997032	rattlesnake root
+n11997160	white lettuce, cankerweed, Nabalus alba, Prenanthes alba
+n11997969	daisybush, daisy-bush, daisy bush
+n11998492	New Zealand daisybush, Olearia haastii
+n11998888	cotton thistle, woolly thistle, Scotch thistle, Onopordum acanthium, Onopordon acanthium
+n11999278	othonna
+n11999656	cascade everlasting, Ozothamnus secundiflorus, Helichrysum secundiflorum
+n12000191	butterweed
+n12001294	American feverfew, wild quinine, prairie dock, Parthenium integrifolium
+n12001707	cineraria, Pericallis cruenta, Senecio cruentus
+n12001924	florest's cineraria, Pericallis hybrida
+n12002428	butterbur, bog rhubarb, Petasites hybridus, Petasites vulgaris
+n12002651	winter heliotrope, sweet coltsfoot, Petasites fragrans
+n12002826	sweet coltsfoot, Petasites sagitattus
+n12003167	oxtongue, bristly oxtongue, bitterweed, bugloss, Picris echioides
+n12003696	hawkweed
+n12004120	mouse-ear hawkweed, Pilosella officinarum, Hieracium pilocella
+n12004547	stevia
+n12004987	rattlesnake root, Prenanthes purpurea
+n12005656	fleabane, feabane mullet, Pulicaria dysenterica
+n12006306	sheep plant, vegetable sheep, Raoulia lutescens, Raoulia australis
+n12006766	coneflower
+n12006930	Mexican hat, Ratibida columnaris
+n12007196	long-head coneflower, prairie coneflower, Ratibida columnifera
+n12007406	prairie coneflower, Ratibida tagetes
+n12007766	Swan River everlasting, rhodanthe, Rhodanthe manglesii, Helipterum manglesii
+n12008252	coneflower
+n12008487	black-eyed Susan, Rudbeckia hirta, Rudbeckia serotina
+n12008749	cutleaved coneflower, Rudbeckia laciniata
+n12009047	golden glow, double gold, hortensia, Rudbeckia laciniata hortensia
+n12009420	lavender cotton, Santolina chamaecyparissus
+n12009792	creeping zinnia, Sanvitalia procumbens
+n12010628	golden thistle
+n12010815	Spanish oyster plant, Scolymus hispanicus
+n12011370	nodding groundsel, Senecio bigelovii
+n12011620	dusty miller, Senecio cineraria, Cineraria maritima
+n12012111	butterweed, ragwort, Senecio glabellus
+n12012253	ragwort, tansy ragwort, ragweed, benweed, Senecio jacobaea
+n12012510	arrowleaf groundsel, Senecio triangularis
+n12013035	black salsify, viper's grass, scorzonera, Scorzonera hispanica
+n12013511	white-topped aster
+n12013701	narrow-leaved white-topped aster
+n12014085	silver sage, silver sagebrush, grey sage, gray sage, Seriphidium canum, Artemisia cana
+n12014355	sea wormwood, Seriphidium maritimum, Artemisia maritima
+n12014923	sawwort, Serratula tinctoria
+n12015221	rosinweed, Silphium laciniatum
+n12015525	milk thistle, lady's thistle, Our Lady's mild thistle, holy thistle, blessed thistle, Silybum marianum
+n12015959	goldenrod
+n12016434	silverrod, Solidago bicolor
+n12016567	meadow goldenrod, Canadian goldenrod, Solidago canadensis
+n12016777	Missouri goldenrod, Solidago missouriensis
+n12016914	alpine goldenrod, Solidago multiradiata
+n12017127	grey goldenrod, gray goldenrod, Solidago nemoralis
+n12017326	Blue Mountain tea, sweet goldenrod, Solidago odora
+n12017511	dyer's weed, Solidago rugosa
+n12017664	seaside goldenrod, beach goldenrod, Solidago sempervirens
+n12017853	narrow goldenrod, Solidago spathulata
+n12018014	Boott's goldenrod
+n12018100	Elliott's goldenrod
+n12018188	Ohio goldenrod
+n12018271	rough-stemmed goldenrod
+n12018363	showy goldenrod
+n12018447	tall goldenrod
+n12018530	zigzag goldenrod, broad leaved goldenrod
+n12018760	sow thistle, milk thistle
+n12019035	milkweed, Sonchus oleraceus
+n12019827	stevia
+n12020184	stokes' aster, cornflower aster, Stokesia laevis
+n12020507	marigold
+n12020736	African marigold, big marigold, Aztec marigold, Tagetes erecta
+n12020941	French marigold, Tagetes patula
+n12022054	painted daisy, pyrethrum, Tanacetum coccineum, Chrysanthemum coccineum
+n12022382	pyrethrum, Dalmatian pyrethrum, Dalmatia pyrethrum, Tanacetum cinerariifolium, Chrysanthemum cinerariifolium
+n12022821	northern dune tansy, Tanacetum douglasii
+n12023108	feverfew, Tanacetum parthenium, Chrysanthemum parthenium
+n12023407	dusty miller, silver-lace, silver lace, Tanacetum ptarmiciflorum, Chrysanthemum ptarmiciflorum
+n12023726	tansy, golden buttons, scented fern, Tanacetum vulgare
+n12024176	dandelion, blowball
+n12024445	common dandelion, Taraxacum ruderalia, Taraxacum officinale
+n12024690	dandelion green
+n12024805	Russian dandelion, kok-saghyz, kok-sagyz, Taraxacum kok-saghyz
+n12025220	stemless hymenoxys, Tetraneuris acaulis, Hymenoxys acaulis
+n12026018	Mexican sunflower, tithonia
+n12026476	Easter daisy, stemless daisy, Townsendia Exscapa
+n12026981	yellow salsify, Tragopogon dubius
+n12027222	salsify, oyster plant, vegetable oyster, Tragopogon porrifolius
+n12027658	meadow salsify, goatsbeard, shepherd's clock, Tragopogon pratensis
+n12028424	scentless camomile, scentless false camomile, scentless mayweed, scentless hayweed, corn mayweed, Tripleurospermum inodorum, Matricaria inodorum
+n12029039	turfing daisy, Tripleurospermum tchihatchewii, Matricaria tchihatchewii
+n12029635	coltsfoot, Tussilago farfara
+n12030092	ursinia
+n12030654	crownbeard, crown-beard, crown beard
+n12030908	wingstem, golden ironweed, yellow ironweed, golden honey plant, Verbesina alternifolia, Actinomeris alternifolia
+n12031139	cowpen daisy, golden crownbeard, golden crown beard, butter daisy, Verbesina encelioides, Ximenesia encelioides
+n12031388	gravelweed, Verbesina helianthoides
+n12031547	Virginia crownbeard, frostweed, frost-weed, Verbesina virginica
+n12031927	ironweed, vernonia
+n12032429	mule's ears, Wyethia amplexicaulis
+n12032686	white-rayed mule's ears, Wyethia helianthoides
+n12033139	cocklebur, cockle-bur, cockleburr, cockle-burr
+n12033504	xeranthemum
+n12033709	immortelle, Xeranthemum annuum
+n12034141	zinnia, old maid, old maid flower
+n12034384	white zinnia, Zinnia acerosa
+n12034594	little golden zinnia, Zinnia grandiflora
+n12035631	blazing star, Mentzelia livicaulis, Mentzelia laevicaulis
+n12035907	bartonia, Mentzelia lindleyi
+n12036067	achene
+n12036226	samara, key fruit, key
+n12036939	campanula, bellflower
+n12037499	creeping bellflower, Campanula rapunculoides
+n12037691	Canterbury bell, cup and saucer, Campanula medium
+n12038038	tall bellflower, Campanula americana
+n12038208	marsh bellflower, Campanula aparinoides
+n12038406	clustered bellflower, Campanula glomerata
+n12038585	peach bells, peach bell, willow bell, Campanula persicifolia
+n12038760	chimney plant, chimney bellflower, Campanula pyramidalis
+n12038898	rampion, rampion bellflower, Campanula rapunculus
+n12039317	tussock bellflower, spreading bellflower, Campanula carpatica
+n12041446	orchid, orchidaceous plant
+n12043444	orchis
+n12043673	male orchis, early purple orchid, Orchis mascula
+n12043836	butterfly orchid, butterfly orchis, Orchis papilionaceae
+n12044041	showy orchis, purple orchis, purple-hooded orchis, Orchis spectabilis
+n12044467	aerides
+n12044784	angrecum
+n12045157	jewel orchid
+n12045514	puttyroot, adam-and-eve, Aplectrum hyemale
+n12045860	arethusa
+n12046028	bog rose, wild pink, dragon's mouth, Arethusa bulbosa
+n12046428	bletia
+n12046815	Bletilla striata, Bletia striata
+n12047345	brassavola
+n12047884	spider orchid, Brassia lawrenceana
+n12048056	spider orchid, Brassia verrucosa
+n12048399	caladenia
+n12048928	calanthe
+n12049282	grass pink, Calopogon pulchellum, Calopogon tuberosum
+n12049562	calypso, fairy-slipper, Calypso bulbosa
+n12050533	cattleya
+n12050959	helleborine
+n12051103	red helleborine, Cephalanthera rubra
+n12051514	spreading pogonia, funnel-crest rosebud orchid, Cleistes divaricata, Pogonia divaricata
+n12051792	rosebud orchid, Cleistes rosea, Pogonia rosea
+n12052267	satyr orchid, Coeloglossum bracteatum
+n12052447	frog orchid, Coeloglossum viride
+n12052787	coelogyne
+n12053405	coral root
+n12053690	spotted coral root, Corallorhiza maculata
+n12053962	striped coral root, Corallorhiza striata
+n12054195	early coral root, pale coral root, Corallorhiza trifida
+n12055073	swan orchid, swanflower, swan-flower, swanneck, swan-neck
+n12055516	cymbid, cymbidium
+n12056099	cypripedia
+n12056217	lady's slipper, lady-slipper, ladies' slipper, slipper orchid
+n12056601	moccasin flower, nerveroot, Cypripedium acaule
+n12056758	common lady's-slipper, showy lady's-slipper, showy lady slipper, Cypripedium reginae, Cypripedium album
+n12056990	ram's-head, ram's-head lady's slipper, Cypripedium arietinum
+n12057211	yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum
+n12057447	large yellow lady's slipper, Cypripedium calceolus pubescens
+n12057660	California lady's slipper, Cypripedium californicum
+n12057895	clustered lady's slipper, Cypripedium fasciculatum
+n12058192	mountain lady's slipper, Cypripedium montanum
+n12058630	marsh orchid
+n12058822	common spotted orchid, Dactylorhiza fuchsii, Dactylorhiza maculata fuchsii
+n12059314	dendrobium
+n12059625	disa
+n12060546	phantom orchid, snow orchid, Eburophyton austinae
+n12061104	tulip orchid, Encyclia citrina, Cattleya citrina
+n12061380	butterfly orchid, Encyclia tampensis, Epidendrum tampense
+n12061614	butterfly orchid, butterfly orchis, Epidendrum venosum, Encyclia venosa
+n12062105	epidendron
+n12062468	helleborine
+n12062626	Epipactis helleborine
+n12062781	stream orchid, chatterbox, giant helleborine, Epipactis gigantea
+n12063211	tongueflower, tongue-flower
+n12063639	rattlesnake plantain, helleborine
+n12064389	fragrant orchid, Gymnadenia conopsea
+n12064591	short-spurred fragrant orchid, Gymnadenia odoratissima
+n12065316	fringed orchis, fringed orchid
+n12065649	frog orchid
+n12065777	rein orchid, rein orchis
+n12066018	bog rein orchid, bog candles, Habenaria dilatata
+n12066261	white fringed orchis, white fringed orchid, Habenaria albiflora
+n12066451	elegant Habenaria, Habenaria elegans
+n12066630	purple-fringed orchid, purple-fringed orchis, Habenaria fimbriata
+n12066821	coastal rein orchid, Habenaria greenei
+n12067029	Hooker's orchid, Habenaria hookeri
+n12067193	ragged orchid, ragged orchis, ragged-fringed orchid, green fringed orchis, Habenaria lacera
+n12067433	prairie orchid, prairie white-fringed orchis, Habenaria leucophaea
+n12067672	snowy orchid, Habenaria nivea
+n12067817	round-leaved rein orchid, Habenaria orbiculata
+n12068138	purple fringeless orchid, purple fringeless orchis, Habenaria peramoena
+n12068432	purple-fringed orchid, purple-fringed orchis, Habenaria psycodes
+n12068615	Alaska rein orchid, Habenaria unalascensis
+n12069009	crested coral root, Hexalectris spicata
+n12069217	Texas purple spike, Hexalectris warnockii
+n12069679	lizard orchid, Himantoglossum hircinum
+n12070016	laelia
+n12070381	liparis
+n12070583	twayblade
+n12070712	fen orchid, fen orchis, Liparis loeselii
+n12071259	broad-leaved twayblade, Listera convallarioides
+n12071477	lesser twayblade, Listera cordata
+n12071744	twayblade, Listera ovata
+n12072210	green adder's mouth, Malaxis-unifolia, Malaxis ophioglossoides
+n12072722	masdevallia
+n12073217	maxillaria
+n12073554	pansy orchid
+n12073991	odontoglossum
+n12074408	oncidium, dancing lady orchid, butterfly plant, butterfly orchid
+n12074867	bee orchid, Ophrys apifera
+n12075010	fly orchid, Ophrys insectifera, Ophrys muscifera
+n12075151	spider orchid
+n12075299	early spider orchid, Ophrys sphegodes
+n12075830	Venus' slipper, Venus's slipper, Venus's shoe
+n12076223	phaius
+n12076577	moth orchid, moth plant
+n12076852	butterfly plant, Phalaenopsis amabilis
+n12077244	rattlesnake orchid
+n12077944	lesser butterfly orchid, Platanthera bifolia, Habenaria bifolia
+n12078172	greater butterfly orchid, Platanthera chlorantha, Habenaria chlorantha
+n12078451	prairie white-fringed orchid, Platanthera leucophea
+n12078747	tangle orchid
+n12079120	Indian crocus
+n12079523	pleurothallis
+n12079963	pogonia
+n12080395	butterfly orchid
+n12080588	Psychopsis krameriana, Oncidium papilio kramerianum
+n12080820	Psychopsis papilio, Oncidium papilio
+n12081215	helmet orchid, greenhood
+n12081649	foxtail orchid
+n12082131	orange-blossom orchid, Sarcochilus falcatus
+n12083113	sobralia
+n12083591	ladies' tresses, lady's tresses
+n12083847	screw augur, Spiranthes cernua
+n12084158	hooded ladies' tresses, Spiranthes romanzoffiana
+n12084400	western ladies' tresses, Spiranthes porrifolia
+n12084555	European ladies' tresses, Spiranthes spiralis
+n12084890	stanhopea
+n12085267	stelis
+n12085664	fly orchid
+n12086012	vanda
+n12086192	blue orchid, Vanda caerulea
+n12086539	vanilla
+n12086778	vanilla orchid, Vanilla planifolia
+n12087961	yam, yam plant
+n12088223	yam
+n12088327	white yam, water yam, Dioscorea alata
+n12088495	cinnamon vine, Chinese yam, Dioscorea batata
+n12088909	elephant's-foot, tortoise plant, Hottentot bread vine, Hottentot's bread vine, Dioscorea elephantipes
+n12089320	wild yam, Dioscorea paniculata
+n12089496	cush-cush, Dioscorea trifida
+n12089846	black bryony, black bindweed, Tamus communis
+n12090890	primrose, primula
+n12091213	English primrose, Primula vulgaris
+n12091377	cowslip, paigle, Primula veris
+n12091550	oxlip, paigle, Primula elatior
+n12091697	Chinese primrose, Primula sinensis
+n12091953	polyanthus, Primula polyantha
+n12092262	pimpernel
+n12092417	scarlet pimpernel, red pimpernel, poor man's weatherglass, Anagallis arvensis
+n12092629	bog pimpernel, Anagallis tenella
+n12092930	chaffweed, bastard pimpernel, false pimpernel
+n12093329	cyclamen, Cyclamen purpurascens
+n12093600	sowbread, Cyclamen hederifolium, Cyclamen neopolitanum
+n12093885	sea milkwort, sea trifoly, black saltwort, Glaux maritima
+n12094244	featherfoil, feather-foil
+n12094401	water gillyflower, American featherfoil, Hottonia inflata
+n12094612	water violet, Hottonia palustris
+n12095020	loosestrife
+n12095281	gooseneck loosestrife, Lysimachia clethroides Duby
+n12095412	yellow pimpernel, Lysimachia nemorum
+n12095543	fringed loosestrife, Lysimachia ciliatum
+n12095647	moneywort, creeping Jenny, creeping Charlie, Lysimachia nummularia
+n12095934	swamp candles, Lysimachia terrestris
+n12096089	whorled loosestrife, Lysimachia quadrifolia
+n12096395	water pimpernel
+n12096563	brookweed, Samolus valerandii
+n12096674	brookweed, Samolus parviflorus, Samolus floribundus
+n12097396	coralberry, spiceberry, Ardisia crenata
+n12097556	marlberry, Ardisia escallonoides, Ardisia paniculata
+n12098403	plumbago
+n12098524	leadwort, Plumbago europaea
+n12098827	thrift
+n12099342	sea lavender, marsh rosemary, statice
+n12100187	barbasco, joewood, Jacquinia keyensis
+n12101870	gramineous plant, graminaceous plant
+n12102133	grass
+n12103680	midgrass
+n12103894	shortgrass, short-grass
+n12104104	sword grass
+n12104238	tallgrass, tall-grass
+n12104501	herbage, pasturage
+n12104734	goat grass, Aegilops triuncalis
+n12105125	wheatgrass, wheat-grass
+n12105353	crested wheatgrass, crested wheat grass, fairway crested wheat grass, Agropyron cristatum
+n12105828	bearded wheatgrass, Agropyron subsecundum
+n12105981	western wheatgrass, bluestem wheatgrass, Agropyron smithii
+n12106134	intermediate wheatgrass, Agropyron intermedium, Elymus hispidus
+n12106323	slender wheatgrass, Agropyron trachycaulum, Agropyron pauciflorum, Elymus trachycaulos
+n12107002	velvet bent, velvet bent grass, brown bent, Rhode Island bent, dog bent, Agrostis canina
+n12107191	cloud grass, Agrostis nebulosa
+n12107710	meadow foxtail, Alopecurus pratensis
+n12107970	foxtail, foxtail grass
+n12108432	broom grass
+n12108613	broom sedge, Andropogon virginicus
+n12108871	tall oat grass, tall meadow grass, evergreen grass, false oat, French rye, Arrhenatherum elatius
+n12109365	toetoe, toitoi, Arundo conspicua, Chionochloa conspicua
+n12109827	oat
+n12110085	cereal oat, Avena sativa
+n12110236	wild oat, wild oat grass, Avena fatua
+n12110352	slender wild oat, Avena barbata
+n12110475	wild red oat, animated oat, Avene sterilis
+n12110778	brome, bromegrass
+n12111238	chess, cheat, Bromus secalinus
+n12111627	field brome, Bromus arvensis
+n12112008	grama, grama grass, gramma, gramma grass
+n12112337	black grama, Bouteloua eriopoda
+n12112609	buffalo grass, Buchloe dactyloides
+n12112918	reed grass
+n12113195	feather reed grass, feathertop, Calamagrostis acutiflora
+n12113323	Australian reed grass, Calamagrostic quadriseta
+n12113657	burgrass, bur grass
+n12114010	buffel grass, Cenchrus ciliaris, Pennisetum cenchroides
+n12114590	Rhodes grass, Chloris gayana
+n12115180	pampas grass, Cortaderia selloana
+n12116058	giant star grass, Cynodon plectostachyum
+n12116429	orchard grass, cocksfoot, cockspur, Dactylis glomerata
+n12116734	Egyptian grass, crowfoot grass, Dactyloctenium aegypticum
+n12117017	crabgrass, crab grass, finger grass
+n12117235	smooth crabgrass, Digitaria ischaemum
+n12117326	large crabgrass, hairy finger grass, Digitaria sanguinalis
+n12117695	barnyard grass, barn grass, barn millet, Echinochloa crusgalli
+n12117912	Japanese millet, billion-dollar grass, Japanese barnyard millet, sanwa millet, Echinochloa frumentacea
+n12118414	yardgrass, yard grass, wire grass, goose grass, Eleusine indica
+n12118661	finger millet, ragi, ragee, African millet, coracan, corakan, kurakkan, Eleusine coracana
+n12119099	lyme grass
+n12119238	wild rye
+n12119390	giant ryegrass, Elymus condensatus, Leymus condensatus
+n12119539	sea lyme grass, European dune grass, Elymus arenarius, Leymus arenaria
+n12119717	Canada wild rye, Elymus canadensis
+n12120347	teff, teff grass, Eragrostis tef, Eragrostic abyssinica
+n12120578	weeping love grass, African love grass, Eragrostis curvula
+n12121033	plume grass
+n12121187	Ravenna grass, wool grass, Erianthus ravennae
+n12121610	fescue, fescue grass, meadow fescue, Festuca elatior
+n12122442	reed meadow grass, Glyceria grandis
+n12122725	velvet grass, Yorkshire fog, Holcus lanatus
+n12122918	creeping soft grass, Holcus mollis
+n12123648	barleycorn
+n12123741	barley grass, wall barley, Hordeum murinum
+n12124172	little barley, Hordeum pusillum
+n12124627	rye grass, ryegrass
+n12124818	perennial ryegrass, English ryegrass, Lolium perenne
+n12125001	Italian ryegrass, Italian rye, Lolium multiflorum
+n12125183	darnel, tare, bearded darnel, cheat, Lolium temulentum
+n12125584	nimblewill, nimble Will, Muhlenbergia schreberi
+n12126084	cultivated rice, Oryza sativa
+n12126360	ricegrass, rice grass
+n12126736	smilo, smilo grass, Oryzopsis miliacea
+n12127460	switch grass, Panicum virgatum
+n12127575	broomcorn millet, hog millet, Panicum miliaceum
+n12127768	goose grass, Texas millet, Panicum Texanum
+n12128071	dallisgrass, dallis grass, paspalum, Paspalum dilatatum
+n12128306	Bahia grass, Paspalum notatum
+n12128490	knotgrass, Paspalum distichum
+n12129134	fountain grass, Pennisetum ruppelii, Pennisetum setaceum
+n12129738	reed canary grass, gardener's garters, lady's laces, ribbon grass, Phalaris arundinacea
+n12129986	canary grass, birdseed grass, Phalaris canariensis
+n12130549	timothy, herd's grass, Phleum pratense
+n12131405	bluegrass, blue grass
+n12131550	meadowgrass, meadow grass
+n12132092	wood meadowgrass, Poa nemoralis, Agrostis alba
+n12132956	noble cane
+n12133151	munj, munja, Saccharum bengalense, Saccharum munja
+n12133462	broom beard grass, prairie grass, wire grass, Andropogon scoparius, Schizachyrium scoparium
+n12133682	bluestem, blue stem, Andropogon furcatus, Andropogon gerardii
+n12134025	rye, Secale cereale
+n12134486	bristlegrass, bristle grass
+n12134695	giant foxtail
+n12134836	yellow bristlegrass, yellow bristle grass, yellow foxtail, glaucous bristlegrass, Setaria glauca
+n12135049	green bristlegrass, green foxtail, rough bristlegrass, bottle-grass, bottle grass, Setaria viridis
+n12135576	Siberian millet, Setaria italica rubrofructa
+n12135729	German millet, golden wonder millet, Setaria italica stramineofructa
+n12135898	millet
+n12136392	rattan, rattan cane
+n12136581	malacca
+n12136720	reed
+n12137120	sorghum
+n12137569	grain sorghum
+n12137791	durra, doura, dourah, Egyptian corn, Indian millet, Guinea corn
+n12137954	feterita, federita, Sorghum vulgare caudatum
+n12138110	hegari
+n12138248	kaoliang
+n12138444	milo, milo maize
+n12138578	shallu, Sorghum vulgare rosburghii
+n12139196	broomcorn, Sorghum vulgare technicum
+n12139575	cordgrass, cord grass
+n12139793	salt reed grass, Spartina cynosuroides
+n12139921	prairie cordgrass, freshwater cordgrass, slough grass, Spartina pectinmata
+n12140511	smut grass, blackseed, carpet grass, Sporobolus poiretii
+n12140759	sand dropseed, Sporobolus cryptandrus
+n12140903	rush grass, rush-grass
+n12141167	St. Augustine grass, Stenotaphrum secundatum, buffalo grass
+n12141385	grain
+n12141495	cereal, cereal grass
+n12142085	wheat
+n12142357	wheat berry
+n12142450	durum, durum wheat, hard wheat, Triticum durum, Triticum turgidum, macaroni wheat
+n12143065	spelt, Triticum spelta, Triticum aestivum spelta
+n12143215	emmer, starch wheat, two-grain spelt, Triticum dicoccum
+n12143405	wild wheat, wild emmer, Triticum dicoccum dicoccoides
+n12143676	corn, maize, Indian corn, Zea mays
+n12144313	mealie
+n12144580	corn
+n12144987	dent corn, Zea mays indentata
+n12145148	flint corn, flint maize, Yankee corn, Zea mays indurata
+n12145477	popcorn, Zea mays everta
+n12146311	zoysia
+n12146488	Manila grass, Japanese carpet grass, Zoysia matrella
+n12146654	Korean lawn grass, Japanese lawn grass, Zoysia japonica
+n12147226	bamboo
+n12147835	common bamboo, Bambusa vulgaris
+n12148757	giant bamboo, kyo-chiku, Dendrocalamus giganteus
+n12150722	umbrella plant, umbrella sedge, Cyperus alternifolius
+n12150969	chufa, yellow nutgrass, earth almond, ground almond, rush nut, Cyperus esculentus
+n12151170	galingale, galangal, Cyperus longus
+n12151615	nutgrass, nut grass, nutsedge, nut sedge, Cyperus rotundus
+n12152031	sand sedge, sand reed, Carex arenaria
+n12152251	cypress sedge, Carex pseudocyperus
+n12152532	cotton grass, cotton rush
+n12152722	common cotton grass, Eriophorum angustifolium
+n12153033	hardstem bulrush, hardstemmed bulrush, Scirpus acutus
+n12153224	wool grass, Scirpus cyperinus
+n12153580	spike rush
+n12153741	water chestnut, Chinese water chestnut, Eleocharis dulcis
+n12153914	needle spike rush, needle rush, slender spike rush, hair grass, Eleocharis acicularis
+n12154114	creeping spike rush, Eleocharis palustris
+n12154773	pandanus, screw pine
+n12155009	textile screw pine, lauhala, Pandanus tectorius
+n12155583	cattail
+n12155773	cat's-tail, bullrush, bulrush, nailrod, reed mace, reedmace, Typha latifolia
+n12156679	bur reed
+n12156819	grain, caryopsis
+n12157056	kernel
+n12157179	rye
+n12157769	gourd, gourd vine
+n12158031	gourd
+n12158443	pumpkin, pumpkin vine, autumn pumpkin, Cucurbita pepo
+n12158798	squash, squash vine
+n12159055	summer squash, summer squash vine, Cucurbita pepo melopepo
+n12159388	yellow squash
+n12159555	marrow, marrow squash, vegetable marrow
+n12159804	zucchini, courgette
+n12159942	cocozelle, Italian vegetable marrow
+n12160125	cymling, pattypan squash
+n12160303	spaghetti squash
+n12160490	winter squash, winter squash plant
+n12160857	acorn squash
+n12161056	hubbard squash, Cucurbita maxima
+n12161285	turban squash, Cucurbita maxima turbaniformis
+n12161577	buttercup squash
+n12161744	butternut squash, Cucurbita maxima
+n12161969	winter crookneck, winter crookneck squash, Cucurbita moschata
+n12162181	cushaw, Cucurbita mixta, Cucurbita argyrosperma
+n12162425	prairie gourd, prairie gourd vine, Missouri gourd, wild pumpkin, buffalo gourd, calabazilla, Cucurbita foetidissima
+n12162758	prairie gourd
+n12163035	bryony, briony
+n12163279	white bryony, devil's turnip, Bryonia alba
+n12164363	sweet melon, muskmelon, sweet melon vine, Cucumis melo
+n12164656	cantaloupe, cantaloup, cantaloupe vine, cantaloup vine, Cucumis melo cantalupensis
+n12164881	winter melon, Persian melon, honeydew melon, winter melon vine, Cucumis melo inodorus
+n12165170	net melon, netted melon, nutmeg melon, Cucumis melo reticulatus
+n12165384	cucumber, cucumber vine, Cucumis sativus
+n12165758	squirting cucumber, exploding cucumber, touch-me-not, Ecballium elaterium
+n12166128	bottle gourd, calabash, Lagenaria siceraria
+n12166424	luffa, dishcloth gourd, sponge gourd, rag gourd, strainer vine
+n12166793	loofah, vegetable sponge, Luffa cylindrica
+n12166929	angled loofah, sing-kwa, Luffa acutangula
+n12167075	loofa, loofah, luffa, loufah sponge
+n12167436	balsam apple, Momordica balsamina
+n12167602	balsam pear, Momordica charantia
+n12168565	lobelia
+n12169099	water lobelia, Lobelia dortmanna
+n12170585	mallow
+n12171098	musk mallow, mus rose, Malva moschata
+n12171316	common mallow, Malva neglecta
+n12171966	okra, gumbo, okra plant, lady's-finger, Abelmoschus esculentus, Hibiscus esculentus
+n12172364	okra
+n12172481	abelmosk, musk mallow, Abelmoschus moschatus, Hibiscus moschatus
+n12172906	flowering maple
+n12173069	velvetleaf, velvet-leaf, velvetweed, Indian mallow, butter-print, China jute, Abutilon theophrasti
+n12173664	hollyhock
+n12173912	rose mallow, Alcea rosea, Althea rosea
+n12174311	althea, althaea, hollyhock
+n12174521	marsh mallow, white mallow, Althea officinalis
+n12174926	poppy mallow
+n12175181	fringed poppy mallow, Callirhoe digitata
+n12175370	purple poppy mallow, Callirhoe involucrata
+n12175598	clustered poppy mallow, Callirhoe triangulata
+n12176453	sea island cotton, tree cotton, Gossypium barbadense
+n12176709	Levant cotton, Gossypium herbaceum
+n12176953	upland cotton, Gossypium hirsutum
+n12177129	Peruvian cotton, Gossypium peruvianum
+n12177455	wild cotton, Arizona wild cotton, Gossypium thurberi
+n12178129	kenaf, kanaf, deccan hemp, bimli, bimli hemp, Indian hemp, Bombay hemp, Hibiscus cannabinus
+n12178780	sorrel tree, Hibiscus heterophyllus
+n12178896	rose mallow, swamp mallow, common rose mallow, swamp rose mallow, Hibiscus moscheutos
+n12179122	cotton rose, Confederate rose, Confederate rose mallow, Hibiscus mutabilis
+n12179632	roselle, rozelle, sorrel, red sorrel, Jamaica sorrel, Hibiscus sabdariffa
+n12180168	mahoe, majagua, mahagua, balibago, purau, Hibiscus tiliaceus
+n12180456	flower-of-an-hour, flowers-of-an-hour, bladder ketmia, black-eyed Susan, Hibiscus trionum
+n12180885	lacebark, ribbonwood, houhere, Hoheria populnea
+n12181352	wild hollyhock, Iliamna remota, Sphaeralcea remota
+n12181612	mountain hollyhock, Iliamna ruvularis, Iliamna acerifolia
+n12182049	seashore mallow
+n12182276	salt marsh mallow, Kosteletzya virginica
+n12183026	chaparral mallow, Malacothamnus fasciculatus, Sphaeralcea fasciculata
+n12183452	malope, Malope trifida
+n12183816	false mallow
+n12184095	waxmallow, wax mallow, sleeping hibiscus
+n12184468	glade mallow, Napaea dioica
+n12184912	pavonia
+n12185254	ribbon tree, ribbonwood, Plagianthus regius, Plagianthus betulinus
+n12185859	bush hibiscus, Radyera farragei, Hibiscus farragei
+n12186352	Virginia mallow, Sida hermaphrodita
+n12186554	Queensland hemp, jellyleaf, Sida rhombifolia
+n12186839	Indian mallow, Sida spinosa
+n12187247	checkerbloom, wild hollyhock, Sidalcea malviflora
+n12187663	globe mallow, false mallow
+n12187891	prairie mallow, red false mallow, Sphaeralcea coccinea, Malvastrum coccineum
+n12188289	tulipwood tree
+n12188635	portia tree, bendy tree, seaside mahoe, Thespesia populnea
+n12189429	red silk-cotton tree, simal, Bombax ceiba, Bombax malabarica
+n12189779	cream-of-tartar tree, sour gourd, Adansonia gregorii
+n12189987	baobab, monkey-bread tree, Adansonia digitata
+n12190410	kapok, ceiba tree, silk-cotton tree, white silk-cotton tree, Bombay ceiba, God tree, Ceiba pentandra
+n12190869	durian, durion, durian tree, Durio zibethinus
+n12191240	Montezuma
+n12192132	shaving-brush tree, Pseudobombax ellipticum
+n12192877	quandong, quandong tree, Brisbane quandong, silver quandong tree, blue fig, Elaeocarpus grandis
+n12193334	quandong, blue fig
+n12193665	makomako, New Zealand wine berry, wineberry, Aristotelia serrata, Aristotelia racemosa
+n12194147	Jamaican cherry, calabur tree, calabura, silk wood, silkwood, Muntingia calabura
+n12194613	breakax, breakaxe, break-axe, Sloanea jamaicensis
+n12195391	sterculia
+n12195533	Panama tree, Sterculia apetala
+n12195734	kalumpang, Java olives, Sterculia foetida
+n12196129	bottle-tree, bottle tree
+n12196336	flame tree, flame durrajong, Brachychiton acerifolius, Sterculia acerifolia
+n12196527	flame tree, broad-leaved bottletree, Brachychiton australis
+n12196694	kurrajong, currajong, Brachychiton populneus
+n12196954	Queensland bottletree, narrow-leaved bottletree, Brachychiton rupestris, Sterculia rupestris
+n12197359	kola, kola nut, kola nut tree, goora nut, Cola acuminata
+n12197601	kola nut, cola nut
+n12198286	Chinese parasol tree, Chinese parasol, Japanese varnish tree, phoenix tree, Firmiana simplex
+n12198793	flannelbush, flannel bush, California beauty
+n12199266	screw tree
+n12199399	nut-leaved screw tree, Helicteres isora
+n12199790	red beech, brown oak, booyong, crow's foot, stave wood, silky elm, Heritiera trifoliolata, Terrietia trifoliolata
+n12199982	looking glass tree, Heritiera macrophylla
+n12200143	looking-glass plant, Heritiera littoralis
+n12200504	honey bell, honeybells, Hermannia verticillata, Mahernia verticillata
+n12200905	mayeng, maple-leaved bayur, Pterospermum acerifolium
+n12201331	silver tree, Tarrietia argyrodendron
+n12201580	cacao, cacao tree, chocolate tree, Theobroma cacao
+n12201938	obeche, obechi, arere, samba, Triplochiton scleroxcylon
+n12202936	linden, linden tree, basswood, lime, lime tree
+n12203529	American basswood, American lime, Tilia americana
+n12203699	small-leaved linden, small-leaved lime, Tilia cordata
+n12203896	white basswood, cottonwood, Tilia heterophylla
+n12204032	Japanese linden, Japanese lime, Tilia japonica
+n12204175	silver lime, silver linden, Tilia tomentosa
+n12204730	corchorus
+n12205460	African hemp, Sparmannia africana
+n12205694	herb, herbaceous plant
+n12214789	protea
+n12215022	honeypot, king protea, Protea cynaroides
+n12215210	honeyflower, honey-flower, Protea mellifera
+n12215579	banksia
+n12215824	honeysuckle, Australian honeysuckle, coast banksia, Banksia integrifolia
+n12216215	smoke bush
+n12216628	Chilean firebush, Chilean flameflower, Embothrium coccineum
+n12216968	Chilean nut, Chile nut, Chile hazel, Chilean hazelnut, Guevina heterophylla, Guevina avellana
+n12217453	grevillea
+n12217851	red-flowered silky oak, Grevillea banksii
+n12218274	silky oak, Grevillea robusta
+n12218490	beefwood, Grevillea striata
+n12218868	cushion flower, pincushion hakea, Hakea laurina
+n12219668	rewa-rewa, New Zealand honeysuckle
+n12220019	honeyflower, honey-flower, mountain devil, Lambertia formosa
+n12220496	silver tree, Leucadendron argenteum
+n12220829	lomatia
+n12221191	macadamia, macadamia tree
+n12221368	Macadamia integrifolia
+n12221522	macadamia nut, macadamia nut tree, Macadamia ternifolia
+n12221801	Queensland nut, Macadamia tetraphylla
+n12222090	prickly ash, Orites excelsa
+n12222493	geebung
+n12222900	wheel tree, firewheel tree, Stenocarpus sinuatus
+n12223160	scrub beefwood, beefwood, Stenocarpus salignus
+n12223569	waratah, Telopea Oreades
+n12223764	waratah, Telopea speciosissima
+n12224978	casuarina
+n12225222	she-oak
+n12225349	beefwood
+n12225563	Australian pine, Casuarina equisetfolia
+n12226932	heath
+n12227658	tree heath, briar, brier, Erica arborea
+n12227909	briarroot
+n12228229	winter heath, spring heath, Erica carnea
+n12228387	bell heather, heather bell, fine-leaved heath, Erica cinerea
+n12228689	Cornish heath, Erica vagans
+n12228886	Spanish heath, Portuguese heath, Erica lusitanica
+n12229111	Prince-of-Wales'-heath, Prince of Wales heath, Erica perspicua
+n12229651	bog rosemary, moorwort, Andromeda glaucophylla
+n12229887	marsh andromeda, common bog rosemary, Andromeda polifolia
+n12230540	madrona, madrono, manzanita, Arbutus menziesii
+n12230794	strawberry tree, Irish strawberry, Arbutus unedo
+n12231192	bearberry
+n12231709	alpine bearberry, black bearberry, Arctostaphylos alpina
+n12232114	heartleaf manzanita, Arctostaphylos andersonii
+n12232280	Parry manzanita, Arctostaphylos manzanita
+n12232851	spike heath, Bruckenthalia spiculifolia
+n12233249	bryanthus
+n12234318	leatherleaf, Chamaedaphne calyculata
+n12234669	Connemara heath, St. Dabeoc's heath, Daboecia cantabrica
+n12235051	trailing arbutus, mayflower, Epigaea repens
+n12235479	creeping snowberry, moxie plum, maidenhair berry, Gaultheria hispidula
+n12236160	salal, shallon, Gaultheria shallon
+n12236546	huckleberry
+n12236768	black huckleberry, Gaylussacia baccata
+n12236977	dangleberry, dangle-berry, Gaylussacia frondosa
+n12237152	box huckleberry, Gaylussacia brachycera
+n12237486	kalmia
+n12237641	mountain laurel, wood laurel, American laurel, calico bush, Kalmia latifolia
+n12237855	swamp laurel, bog laurel, bog kalmia, Kalmia polifolia
+n12238756	trapper's tea, glandular Labrador tea
+n12238913	wild rosemary, marsh tea, Ledum palustre
+n12239240	sand myrtle, Leiophyllum buxifolium
+n12239647	leucothoe
+n12239880	dog laurel, dog hobble, switch-ivy, Leucothoe fontanesiana, Leucothoe editorum
+n12240150	sweet bells, Leucothoe racemosa
+n12240477	alpine azalea, mountain azalea, Loiseleuria procumbens
+n12240965	staggerbush, stagger bush, Lyonia mariana
+n12241192	maleberry, male berry, privet andromeda, he-huckleberry, Lyonia ligustrina
+n12241426	fetterbush, fetter bush, shiny lyonia, Lyonia lucida
+n12241880	false azalea, fool's huckleberry, Menziesia ferruginea
+n12242123	minniebush, minnie bush, Menziesia pilosa
+n12242409	sorrel tree, sourwood, titi, Oxydendrum arboreum
+n12242850	mountain heath, Phyllodoce caerulea, Bryanthus taxifolius
+n12243109	purple heather, Brewer's mountain heather, Phyllodoce breweri
+n12243693	fetterbush, mountain fetterbush, mountain andromeda, Pieris floribunda
+n12244153	rhododendron
+n12244458	coast rhododendron, Rhododendron californicum
+n12244650	rosebay, Rhododendron maxima
+n12244819	swamp azalea, swamp honeysuckle, white honeysuckle, Rhododendron viscosum
+n12245319	azalea
+n12245695	cranberry
+n12245885	American cranberry, large cranberry, Vaccinium macrocarpon
+n12246037	European cranberry, small cranberry, Vaccinium oxycoccus
+n12246232	blueberry, blueberry bush
+n12246773	farkleberry, sparkleberry, Vaccinium arboreum
+n12246941	low-bush blueberry, low blueberry, Vaccinium angustifolium, Vaccinium pennsylvanicum
+n12247202	rabbiteye blueberry, rabbit-eye blueberry, rabbiteye, Vaccinium ashei
+n12247407	dwarf bilberry, dwarf blueberry, Vaccinium caespitosum
+n12247963	evergreen blueberry, Vaccinium myrsinites
+n12248141	evergreen huckleberry, Vaccinium ovatum
+n12248359	bilberry, thin-leaved bilberry, mountain blue berry, Viccinium membranaceum
+n12248574	bilberry, whortleberry, whinberry, blaeberry, Viccinium myrtillus
+n12248780	bog bilberry, bog whortleberry, moor berry, Vaccinium uliginosum alpinum
+n12248941	dryland blueberry, dryland berry, Vaccinium pallidum
+n12249122	grouseberry, grouse-berry, grouse whortleberry, Vaccinium scoparium
+n12249294	deerberry, squaw huckleberry, Vaccinium stamineum
+n12249542	cowberry, mountain cranberry, lingonberry, lingenberry, lingberry, foxberry, Vaccinium vitis-idaea
+n12251001	diapensia
+n12251278	galax, galaxy, wandflower, beetleweed, coltsfoot, Galax urceolata
+n12251740	pyxie, pixie, pixy, Pyxidanthera barbulata
+n12252168	shortia
+n12252383	oconee bells, Shortia galacifolia
+n12252866	Australian heath
+n12253229	epacris
+n12253487	common heath, Epacris impressa
+n12253664	common heath, blunt-leaf heath, Epacris obtusifolia
+n12253835	Port Jackson heath, Epacris purpurascens
+n12254168	native cranberry, groundberry, ground-berry, cranberry heath, Astroloma humifusum, Styphelia humifusum
+n12255225	pink fivecorner, Styphelia triflora
+n12256112	wintergreen, pyrola
+n12256325	false wintergreen, Pyrola americana, Pyrola rotundifolia americana
+n12256522	lesser wintergreen, Pyrola minor
+n12256708	wild lily of the valley, shinleaf, Pyrola elliptica
+n12256920	wild lily of the valley, Pyrola rotundifolia
+n12257570	pipsissewa, prince's pine
+n12257725	love-in-winter, western prince's pine, Chimaphila umbellata, Chimaphila corymbosa
+n12258101	one-flowered wintergreen, one-flowered pyrola, Moneses uniflora, Pyrola uniflora
+n12258885	Indian pipe, waxflower, Monotropa uniflora
+n12259316	pinesap, false beachdrops, Monotropa hypopithys
+n12260799	beech, beech tree
+n12261359	common beech, European beech, Fagus sylvatica
+n12261571	copper beech, purple beech, Fagus sylvatica atropunicea, Fagus purpurea, Fagus sylvatica purpurea
+n12261808	American beech, white beech, red beech, Fagus grandifolia, Fagus americana
+n12262018	weeping beech, Fagus pendula, Fagus sylvatica pendula
+n12262185	Japanese beech
+n12262553	chestnut, chestnut tree
+n12263038	American chestnut, American sweet chestnut, Castanea dentata
+n12263204	European chestnut, sweet chestnut, Spanish chestnut, Castanea sativa
+n12263410	Chinese chestnut, Castanea mollissima
+n12263588	Japanese chestnut, Castanea crenata
+n12263738	Allegheny chinkapin, eastern chinquapin, chinquapin, dwarf chestnut, Castanea pumila
+n12263987	Ozark chinkapin, Ozark chinquapin, chinquapin, Castanea ozarkensis
+n12264512	oak chestnut
+n12264786	giant chinkapin, golden chinkapin, Chrysolepis chrysophylla, Castanea chrysophylla, Castanopsis chrysophylla
+n12265083	dwarf golden chinkapin, Chrysolepis sempervirens
+n12265394	tanbark oak, Lithocarpus densiflorus
+n12265600	Japanese oak, Lithocarpus glabra, Lithocarpus glaber
+n12266217	southern beech, evergreen beech
+n12266528	myrtle beech, Nothofagus cuninghamii
+n12266644	Coigue, Nothofagus dombeyi
+n12266796	New Zealand beech
+n12266984	silver beech, Nothofagus menziesii
+n12267133	roble beech, Nothofagus obliqua
+n12267265	rauli beech, Nothofagus procera
+n12267411	black beech, Nothofagus solanderi
+n12267534	hard beech, Nothofagus truncata
+n12267677	acorn
+n12267931	cupule, acorn cup
+n12268246	oak, oak tree
+n12269241	live oak
+n12269406	coast live oak, California live oak, Quercus agrifolia
+n12269652	white oak
+n12270027	American white oak, Quercus alba
+n12270278	Arizona white oak, Quercus arizonica
+n12270460	swamp white oak, swamp oak, Quercus bicolor
+n12270741	European turkey oak, turkey oak, Quercus cerris
+n12270946	canyon oak, canyon live oak, maul oak, iron oak, Quercus chrysolepis
+n12271187	scarlet oak, Quercus coccinea
+n12271451	jack oak, northern pin oak, Quercus ellipsoidalis
+n12271643	red oak
+n12271933	southern red oak, swamp red oak, turkey oak, Quercus falcata
+n12272239	Oregon white oak, Oregon oak, Garry oak, Quercus garryana
+n12272432	holm oak, holm tree, holly-leaved oak, evergreen oak, Quercus ilex
+n12272735	bear oak, Quercus ilicifolia
+n12272883	shingle oak, laurel oak, Quercus imbricaria
+n12273114	bluejack oak, turkey oak, Quercus incana
+n12273344	California black oak, Quercus kelloggii
+n12273515	American turkey oak, turkey oak, Quercus laevis
+n12273768	laurel oak, pin oak, Quercus laurifolia
+n12273939	California white oak, valley oak, valley white oak, roble, Quercus lobata
+n12274151	overcup oak, Quercus lyrata
+n12274358	bur oak, burr oak, mossy-cup oak, mossycup oak, Quercus macrocarpa
+n12274630	scrub oak
+n12274863	blackjack oak, blackjack, jack oak, Quercus marilandica
+n12275131	swamp chestnut oak, Quercus michauxii
+n12275317	Japanese oak, Quercus mongolica, Quercus grosseserrata
+n12275489	chestnut oak
+n12275675	chinquapin oak, chinkapin oak, yellow chestnut oak, Quercus muehlenbergii
+n12275888	myrtle oak, seaside scrub oak, Quercus myrtifolia
+n12276110	water oak, possum oak, Quercus nigra
+n12276314	Nuttall oak, Nuttall's oak, Quercus nuttalli
+n12276477	durmast, Quercus petraea, Quercus sessiliflora
+n12276628	basket oak, cow oak, Quercus prinus, Quercus montana
+n12276872	pin oak, swamp oak, Quercus palustris
+n12277150	willow oak, Quercus phellos
+n12277334	dwarf chinkapin oak, dwarf chinquapin oak, dwarf oak, Quercus prinoides
+n12277578	common oak, English oak, pedunculate oak, Quercus robur
+n12277800	northern red oak, Quercus rubra, Quercus borealis
+n12278107	Shumard oak, Shumard red oak, Quercus shumardii
+n12278371	post oak, box white oak, brash oak, iron oak, Quercus stellata
+n12278650	cork oak, Quercus suber
+n12278865	Spanish oak, Quercus texana
+n12279060	huckleberry oak, Quercus vaccinifolia
+n12279293	Chinese cork oak, Quercus variabilis
+n12279458	black oak, yellow oak, quercitron, quercitron oak, Quercus velutina
+n12279772	southern live oak, Quercus virginiana
+n12280060	interior live oak, Quercus wislizenii, Quercus wizlizenii
+n12280364	mast
+n12281241	birch, birch tree
+n12281788	yellow birch, Betula alleghaniensis, Betula leutea
+n12281974	American white birch, paper birch, paperbark birch, canoe birch, Betula cordifolia, Betula papyrifera
+n12282235	grey birch, gray birch, American grey birch, American gray birch, Betula populifolia
+n12282527	silver birch, common birch, European white birch, Betula pendula
+n12282737	downy birch, white birch, Betula pubescens
+n12282933	black birch, river birch, red birch, Betula nigra
+n12283147	sweet birch, cherry birch, black birch, Betula lenta
+n12283395	Yukon white birch, Betula neoalaskana
+n12283542	swamp birch, water birch, mountain birch, Western paper birch, Western birch, Betula fontinalis
+n12283790	Newfoundland dwarf birch, American dwarf birch, Betula glandulosa
+n12284262	alder, alder tree
+n12284821	common alder, European black alder, Alnus glutinosa, Alnus vulgaris
+n12285049	grey alder, gray alder, Alnus incana
+n12285195	seaside alder, Alnus maritima
+n12285369	white alder, mountain alder, Alnus rhombifolia
+n12285512	red alder, Oregon alder, Alnus rubra
+n12285705	speckled alder, Alnus rugosa
+n12285900	smooth alder, hazel alder, Alnus serrulata
+n12286068	green alder, Alnus veridis
+n12286197	green alder, Alnus veridis crispa, Alnus crispa
+n12286826	hornbeam
+n12286988	European hornbeam, Carpinus betulus
+n12287195	American hornbeam, Carpinus caroliniana
+n12287642	hop hornbeam
+n12287836	Old World hop hornbeam, Ostrya carpinifolia
+n12288005	Eastern hop hornbeam, ironwood, ironwood tree, Ostrya virginiana
+n12288823	hazelnut, hazel, hazelnut tree
+n12289310	American hazel, Corylus americana
+n12289433	cobnut, filbert, Corylus avellana, Corylus avellana grandis
+n12289585	beaked hazelnut, Corylus cornuta
+n12290748	centaury
+n12290975	rosita, Centaurium calycosum
+n12291143	lesser centaury, Centaurium minus
+n12291459	seaside centaury
+n12291671	slender centaury
+n12291959	prairie gentian, tulip gentian, bluebell, Eustoma grandiflorum
+n12292463	Persian violet, Exacum affine
+n12292877	columbo, American columbo, deer's-ear, deer's-ears, pyramid plant, American gentian
+n12293723	gentian
+n12294124	gentianella, Gentiana acaulis
+n12294331	closed gentian, blind gentian, bottle gentian, Gentiana andrewsii
+n12294542	explorer's gentian, Gentiana calycosa
+n12294723	closed gentian, blind gentian, Gentiana clausa
+n12294871	great yellow gentian, Gentiana lutea
+n12295033	marsh gentian, calathian violet, Gentiana pneumonanthe
+n12295237	soapwort gentian, Gentiana saponaria
+n12295429	striped gentian, Gentiana villosa
+n12295796	agueweed, ague weed, five-flowered gentian, stiff gentian, Gentianella quinquefolia, Gentiana quinquefolia
+n12296045	felwort, gentianella amarella
+n12296432	fringed gentian
+n12296735	Gentianopsis crinita, Gentiana crinita
+n12296929	Gentianopsis detonsa, Gentiana detonsa
+n12297110	Gentianopsid procera, Gentiana procera
+n12297280	Gentianopsis thermalis, Gentiana thermalis
+n12297507	tufted gentian, Gentianopsis holopetala, Gentiana holopetala
+n12297846	spurred gentian
+n12298165	sabbatia
+n12299640	toothbrush tree, mustard tree, Salvadora persica
+n12300840	olive tree
+n12301180	olive, European olive tree, Olea europaea
+n12301445	olive
+n12301613	black maire, Olea cunninghamii
+n12301766	white maire, Olea lanceolata
+n12302071	fringe tree
+n12302248	fringe bush, Chionanthus virginicus
+n12302565	forestiera
+n12303083	forsythia
+n12303462	ash, ash tree
+n12304115	white ash, Fraxinus Americana
+n12304286	swamp ash, Fraxinus caroliniana
+n12304420	flowering ash, Fraxinus cuspidata
+n12304703	European ash, common European ash, Fraxinus excelsior
+n12304899	Oregon ash, Fraxinus latifolia, Fraxinus oregona
+n12305089	black ash, basket ash, brown ash, hoop ash, Fraxinus nigra
+n12305293	manna ash, flowering ash, Fraxinus ornus
+n12305475	red ash, downy ash, Fraxinus pennsylvanica
+n12305654	green ash, Fraxinus pennsylvanica subintegerrima
+n12305819	blue ash, Fraxinus quadrangulata
+n12305986	mountain ash, Fraxinus texensis
+n12306089	pumpkin ash, Fraxinus tomentosa
+n12306270	Arizona ash, Fraxinus velutina
+n12306717	jasmine
+n12306938	primrose jasmine, Jasminum mesnyi
+n12307076	winter jasmine, Jasminum nudiflorum
+n12307240	common jasmine, true jasmine, jessamine, Jasminum officinale
+n12307756	privet
+n12308112	Amur privet, Ligustrum amurense
+n12308447	Japanese privet, Ligustrum japonicum
+n12308907	Ligustrum obtusifolium
+n12309277	common privet, Ligustrum vulgare
+n12309630	devilwood, American olive, Osmanthus americanus
+n12310021	mock privet
+n12310349	lilac
+n12310638	Himalayan lilac, Syringa emodi
+n12311045	Persian lilac, Syringa persica
+n12311224	Japanese tree lilac, Syringa reticulata, Syringa amurensis japonica
+n12311413	Japanese lilac, Syringa villosa
+n12311579	common lilac, Syringa vulgaris
+n12312110	bloodwort
+n12312728	kangaroo paw, kangaroo's paw, kangaroo's-foot, kangaroo-foot plant, Australian sword lily, Anigozanthus manglesii
+n12315060	Virginian witch hazel, Hamamelis virginiana
+n12315245	vernal witch hazel, Hamamelis vernalis
+n12315598	winter hazel, flowering hazel
+n12315999	fothergilla, witch alder
+n12316444	liquidambar
+n12316572	sweet gum, sweet gum tree, bilsted, red gum, American sweet gum, Liquidambar styraciflua
+n12317296	iron tree, iron-tree, ironwood, ironwood tree
+n12318378	walnut, walnut tree
+n12318782	California black walnut, Juglans californica
+n12318965	butternut, butternut tree, white walnut, Juglans cinerea
+n12319204	black walnut, black walnut tree, black hickory, Juglans nigra
+n12319414	English walnut, English walnut tree, Circassian walnut, Persian walnut, Juglans regia
+n12320010	hickory, hickory tree
+n12320414	water hickory, bitter pecan, water bitternut, Carya aquatica
+n12320627	pignut, pignut hickory, brown hickory, black hickory, Carya glabra
+n12320806	bitternut, bitternut hickory, bitter hickory, bitter pignut, swamp hickory, Carya cordiformis
+n12321077	pecan, pecan tree, Carya illinoensis, Carya illinoinsis
+n12321395	big shellbark, big shellbark hickory, big shagbark, king nut, king nut hickory, Carya laciniosa
+n12321669	nutmeg hickory, Carya myristicaeformis, Carya myristiciformis
+n12321873	shagbark, shagbark hickory, shellbark, shellbark hickory, Carya ovata
+n12322099	mockernut, mockernut hickory, black hickory, white-heart hickory, big-bud hickory, Carya tomentosa
+n12322501	wing nut, wing-nut
+n12322699	Caucasian walnut, Pterocarya fraxinifolia
+n12323665	dhawa, dhava
+n12324056	combretum
+n12324222	hiccup nut, hiccough nut, Combretum bracteosum
+n12324388	bush willow, Combretum appiculatum
+n12324558	bush willow, Combretum erythrophyllum
+n12324906	button tree, button mangrove, Conocarpus erectus
+n12325234	white mangrove, Laguncularia racemosa
+n12325787	oleaster
+n12327022	water milfoil
+n12327528	anchovy pear, anchovy pear tree, Grias cauliflora
+n12327846	brazil nut, brazil-nut tree, Bertholletia excelsa
+n12328398	loosestrife
+n12328567	purple loosestrife, spiked loosestrife, Lythrum salicaria
+n12328801	grass poly, hyssop loosestrife, Lythrum hyssopifolia
+n12329260	crape myrtle, crepe myrtle, crepe flower, Lagerstroemia indica
+n12329473	Queen's crape myrtle, pride-of-India, Lagerstroemia speciosa
+n12330239	myrtaceous tree
+n12330469	myrtle
+n12330587	common myrtle, Myrtus communis
+n12330891	bayberry, bay-rum tree, Jamaica bayberry, wild cinnamon, Pimenta acris
+n12331066	allspice, allspice tree, pimento tree, Pimenta dioica
+n12331263	allspice tree, Pimenta officinalis
+n12331655	sour cherry, Eugenia corynantha
+n12331788	nakedwood, Eugenia dicrana
+n12332030	Surinam cherry, pitanga, Eugenia uniflora
+n12332218	rose apple, rose-apple tree, jambosa, Eugenia jambos
+n12332555	feijoa, feijoa bush
+n12333053	jaboticaba, jaboticaba tree, Myrciaria cauliflora
+n12333530	guava, true guava, guava bush, Psidium guajava
+n12333771	guava, strawberry guava, yellow cattley guava, Psidium littorale
+n12333961	cattley guava, purple strawberry guava, Psidium cattleianum, Psidium littorale longipes
+n12334153	Brazilian guava, Psidium guineense
+n12334293	gum tree, gum
+n12334891	eucalyptus, eucalypt, eucalyptus tree
+n12335483	flooded gum
+n12335664	mallee
+n12335800	stringybark
+n12335937	smoothbark
+n12336092	red gum, peppermint, peppermint gum, Eucalyptus amygdalina
+n12336224	red gum, marri, Eucalyptus calophylla
+n12336333	river red gum, river gum, Eucalyptus camaldulensis, Eucalyptus rostrata
+n12336586	mountain swamp gum, Eucalyptus camphora
+n12336727	snow gum, ghost gum, white ash, Eucalyptus coriacea, Eucalyptus pauciflora
+n12336973	alpine ash, mountain oak, Eucalyptus delegatensis
+n12337131	white mallee, congoo mallee, Eucalyptus dumosa
+n12337246	white stringybark, thin-leaved stringybark, Eucalyptusd eugenioides
+n12337391	white mountain ash, Eucalyptus fraxinoides
+n12337617	blue gum, fever tree, Eucalyptus globulus
+n12337800	rose gum, Eucalypt grandis
+n12337922	cider gum, Eucalypt gunnii
+n12338034	swamp gum, Eucalypt ovata
+n12338146	spotted gum, Eucalyptus maculata
+n12338258	lemon-scented gum, Eucalyptus citriodora, Eucalyptus maculata citriodora
+n12338454	black mallee, black sally, black gum, Eucalytus stellulata
+n12338655	forest red gum, Eucalypt tereticornis
+n12338796	mountain ash, Eucalyptus regnans
+n12338979	manna gum, Eucalyptus viminalis
+n12339526	clove, clove tree, Syzygium aromaticum, Eugenia aromaticum, Eugenia caryophyllatum
+n12339831	clove
+n12340383	tupelo, tupelo tree
+n12340581	water gum, Nyssa aquatica
+n12340755	sour gum, black gum, pepperidge, Nyssa sylvatica
+n12341542	enchanter's nightshade
+n12341931	Circaea lutetiana
+n12342299	willowherb
+n12342498	fireweed, giant willowherb, rosebay willowherb, wickup, Epilobium angustifolium
+n12342852	California fuchsia, humming bird's trumpet, Epilobium canum canum, Zauschneria californica
+n12343480	fuchsia
+n12343753	lady's-eardrop, ladies'-eardrop, lady's-eardrops, ladies'-eardrops, Fuchsia coccinea
+n12344283	evening primrose
+n12344483	common evening primrose, German rampion, Oenothera biennis
+n12344700	sundrops, Oenothera fruticosa
+n12344837	Missouri primrose, Ozark sundrops, Oenothera macrocarpa
+n12345280	pomegranate, pomegranate tree, Punica granatum
+n12345899	mangrove, Rhizophora mangle
+n12346578	daphne
+n12346813	garland flower, Daphne cneorum
+n12346986	spurge laurel, wood laurel, Daphne laureola
+n12347158	mezereon, February daphne, Daphne mezereum
+n12349315	Indian rhododendron, Melastoma malabathricum
+n12349711	Medinilla magnifica
+n12350032	deer grass, meadow beauty
+n12350758	canna
+n12351091	achira, indian shot, arrowroot, Canna indica, Canna edulis
+n12351790	arrowroot, American arrowroot, obedience plant, Maranta arundinaceae
+n12352287	banana, banana tree
+n12352639	dwarf banana, Musa acuminata
+n12352844	Japanese banana, Musa basjoo
+n12352990	plantain, plantain tree, Musa paradisiaca
+n12353203	edible banana, Musa paradisiaca sapientum
+n12353431	abaca, Manila hemp, Musa textilis
+n12353754	Abyssinian banana, Ethiopian banana, Ensete ventricosum, Musa ensete
+n12355760	ginger
+n12356023	common ginger, Canton ginger, stem ginger, Zingiber officinale
+n12356395	turmeric, Curcuma longa, Curcuma domestica
+n12356960	galangal, Alpinia galanga
+n12357485	shellflower, shall-flower, shell ginger, Alpinia Zerumbet, Alpinia speciosa, Languas speciosa
+n12357968	grains of paradise, Guinea grains, Guinea pepper, melagueta pepper, Aframomum melegueta
+n12358293	cardamom, cardamon, Elettaria cardamomum
+n12360108	begonia
+n12360534	fibrous-rooted begonia
+n12360684	tuberous begonia
+n12360817	rhizomatous begonia
+n12360958	Christmas begonia, blooming-fool begonia, Begonia cheimantha
+n12361135	angel-wing begonia, Begonia cocchinea
+n12361560	beefsteak begonia, kidney begonia, Begonia erythrophylla, Begonia feastii
+n12361754	star begonia, star-leaf begonia, Begonia heracleifolia
+n12361946	rex begonia, king begonia, painted-leaf begonia, beefsteak geranium, Begonia rex
+n12362274	wax begonia, Begonia semperflorens
+n12362514	Socotra begonia, Begonia socotrana
+n12362668	hybrid tuberous begonia, Begonia tuberhybrida
+n12363301	dillenia
+n12363768	guinea gold vine, guinea flower
+n12364604	poon
+n12364940	calaba, Santa Maria tree, Calophyllum calaba
+n12365158	Maria, Calophyllum longifolium
+n12365285	laurelwood, lancewood tree, Calophyllum candidissimum
+n12365462	Alexandrian laurel, Calophyllum inophyllum
+n12365900	clusia
+n12366053	wild fig, Clusia flava
+n12366186	waxflower, Clusia insignis
+n12366313	pitch apple, strangler fig, Clusia rosea, Clusia major
+n12366675	mangosteen, mangosteen tree, Garcinia mangostana
+n12366870	gamboge tree, Garcinia hanburyi, Garcinia cambogia, Garcinia gummi-gutta
+n12367611	St John's wort
+n12368028	common St John's wort, tutsan, Hypericum androsaemum
+n12368257	great St John's wort, Hypericum ascyron, Hypericum pyramidatum
+n12368451	creeping St John's wort, Hypericum calycinum
+n12369066	low St Andrew's cross, Hypericum hypericoides
+n12369309	klammath weed, Hypericum perforatum
+n12369476	shrubby St John's wort, Hypericum prolificum, Hypericum spathulatum
+n12369665	St Peter's wort, Hypericum tetrapterum, Hypericum maculatum
+n12369845	marsh St-John's wort, Hypericum virginianum
+n12370174	mammee apple, mammee, mamey, mammee tree, Mammea americana
+n12370549	rose chestnut, ironwood, ironwood tree, Mesua ferrea
+n12371202	bower actinidia, tara vine, Actinidia arguta
+n12371439	Chinese gooseberry, kiwi, kiwi vine, Actinidia chinensis, Actinidia deliciosa
+n12371704	silvervine, silver vine, Actinidia polygama
+n12372233	wild cinnamon, white cinnamon tree, Canella winterana, Canella-alba
+n12373100	papaya, papaia, pawpaw, papaya tree, melon tree, Carica papaya
+n12373739	souari, souari nut, souari tree, Caryocar nuciferum
+n12374418	rockrose, rock rose
+n12374705	white-leaved rockrose, Cistus albidus
+n12374862	common gum cistus, Cistus ladanifer, Cistus ladanum
+n12375769	frostweed, frost-weed, frostwort, Helianthemum canadense, Crocanthemum canadense
+n12377198	dipterocarp
+n12377494	red lauan, red lauan tree, Shorea teysmanniana
+n12378249	governor's plum, governor plum, Madagascar plum, ramontchi, batoko palm, Flacourtia indica
+n12378753	kei apple, kei apple bush, Dovyalis caffra
+n12378963	ketembilla, kitembilla, kitambilla, ketembilla tree, Ceylon gooseberry, Dovyalis hebecarpa
+n12379531	chaulmoogra, chaulmoogra tree, chaulmugra, Hydnocarpus kurzii, Taraktagenos kurzii, Taraktogenos kurzii
+n12380761	wild peach, Kiggelaria africana
+n12381511	candlewood
+n12382233	boojum tree, cirio, Fouquieria columnaris, Idria columnaris
+n12382875	bird's-eye bush, Ochna serrulata
+n12383737	granadilla, purple granadillo, Passiflora edulis
+n12383894	granadilla, sweet granadilla, Passiflora ligularis
+n12384037	granadilla, giant granadilla, Passiflora quadrangularis
+n12384227	maypop, Passiflora incarnata
+n12384375	Jamaica honeysuckle, yellow granadilla, Passiflora laurifolia
+n12384569	banana passion fruit, Passiflora mollissima
+n12384680	sweet calabash, Passiflora maliformis
+n12384839	love-in-a-mist, running pop, wild water lemon, Passiflora foetida
+n12385429	reseda
+n12385566	mignonette, sweet reseda, Reseda odorata
+n12385830	dyer's rocket, dyer's mignonette, weld, Reseda luteola
+n12386945	false tamarisk, German tamarisk, Myricaria germanica
+n12387103	halophyte
+n12387633	viola
+n12387839	violet
+n12388143	field pansy, heartsease, Viola arvensis
+n12388293	American dog violet, Viola conspersa
+n12388858	dog violet, heath violet, Viola canina
+n12388989	horned violet, tufted pansy, Viola cornuta
+n12389130	two-eyed violet, heartsease, Viola ocellata
+n12389501	bird's-foot violet, pansy violet, Johnny-jump-up, wood violet, Viola pedata
+n12389727	downy yellow violet, Viola pubescens
+n12389932	long-spurred violet, Viola rostrata
+n12390099	pale violet, striped violet, cream violet, Viola striata
+n12390314	hedge violet, wood violet, Viola sylvatica, Viola reichenbachiana
+n12392070	nettle
+n12392549	stinging nettle, Urtica dioica
+n12392765	Roman nettle, Urtica pipulifera
+n12393269	ramie, ramee, Chinese silk plant, China grass, Boehmeria nivea
+n12394118	wood nettle, Laportea canadensis
+n12394328	Australian nettle, Australian nettle tree
+n12394638	pellitory-of-the-wall, wall pellitory, pellitory, Parietaria difussa
+n12395068	richweed, clearweed, dead nettle, Pilea pumilla
+n12395289	artillery plant, Pilea microphylla
+n12395463	friendship plant, panamica, panamiga, Pilea involucrata
+n12395906	Queensland grass-cloth plant, Pipturus argenteus
+n12396091	Pipturus albidus
+n12396924	cannabis, hemp
+n12397431	Indian hemp, Cannabis indica
+n12399132	mulberry, mulberry tree
+n12399384	white mulberry, Morus alba
+n12399534	black mulberry, Morus nigra
+n12399656	red mulberry, Morus rubra
+n12399899	osage orange, bow wood, mock orange, Maclura pomifera
+n12400489	breadfruit, breadfruit tree, Artocarpus communis, Artocarpus altilis
+n12400720	jackfruit, jackfruit tree, Artocarpus heterophyllus
+n12400924	marang, marang tree, Artocarpus odoratissima
+n12401335	fig tree
+n12401684	fig, common fig, common fig tree, Ficus carica
+n12401893	caprifig, Ficus carica sylvestris
+n12402051	golden fig, Florida strangler fig, strangler fig, wild fig, Ficus aurea
+n12402348	banyan, banyan tree, banian, banian tree, Indian banyan, East Indian fig tree, Ficus bengalensis
+n12402596	pipal, pipal tree, pipul, peepul, sacred fig, bo tree, Ficus religiosa
+n12402840	India-rubber tree, India-rubber plant, India-rubber fig, rubber plant, Assam rubber, Ficus elastica
+n12403075	mistletoe fig, mistletoe rubber plant, Ficus diversifolia, Ficus deltoidea
+n12403276	Port Jackson fig, rusty rig, little-leaf fig, Botany Bay fig, Ficus rubiginosa
+n12403513	sycamore, sycamore fig, mulberry fig, Ficus sycomorus
+n12403994	paper mulberry, Broussonetia papyrifera
+n12404729	trumpetwood, trumpet-wood, trumpet tree, snake wood, imbauba, Cecropia peltata
+n12405714	elm, elm tree
+n12406304	winged elm, wing elm, Ulmus alata
+n12406488	American elm, white elm, water elm, rock elm, Ulmus americana
+n12406715	smooth-leaved elm, European field elm, Ulmus carpinifolia
+n12406902	cedar elm, Ulmus crassifolia
+n12407079	witch elm, wych elm, Ulmus glabra
+n12407222	Dutch elm, Ulmus hollandica
+n12407396	Huntingdon elm, Ulmus hollandica vegetata
+n12407545	water elm, Ulmus laevis
+n12407715	Chinese elm, Ulmus parvifolia
+n12407890	English elm, European elm, Ulmus procera
+n12408077	Siberian elm, Chinese elm, dwarf elm, Ulmus pumila
+n12408280	slippery elm, red elm, Ulmus rubra
+n12408466	Jersey elm, guernsey elm, wheately elm, Ulmus sarniensis, Ulmus campestris sarniensis, Ulmus campestris wheatleyi
+n12408717	September elm, red elm, Ulmus serotina
+n12408873	rock elm, Ulmus thomasii
+n12409231	hackberry, nettle tree
+n12409470	European hackberry, Mediterranean hackberry, Celtis australis
+n12409651	American hackberry, Celtis occidentalis
+n12409840	sugarberry, Celtis laevigata
+n12411461	iridaceous plant
+n12412355	bearded iris
+n12412606	beardless iris
+n12412987	orrisroot, orris
+n12413165	dwarf iris, Iris cristata
+n12413301	Dutch iris, Iris filifolia
+n12413419	Florentine iris, orris, Iris germanica florentina, Iris florentina
+n12413642	stinking iris, gladdon, gladdon iris, stinking gladwyn, roast beef plant, Iris foetidissima
+n12413880	German iris, Iris germanica
+n12414035	Japanese iris, Iris kaempferi
+n12414159	German iris, Iris kochii
+n12414329	Dalmatian iris, Iris pallida
+n12414449	Persian iris, Iris persica
+n12414818	Dutch iris, Iris tingitana
+n12414932	dwarf iris, vernal iris, Iris verna
+n12415595	Spanish iris, xiphium iris, Iris xiphium
+n12416073	blackberry-lily, leopard lily, Belamcanda chinensis
+n12416423	crocus
+n12416703	saffron, saffron crocus, Crocus sativus
+n12417836	corn lily
+n12418221	blue-eyed grass
+n12418507	wandflower, Sparaxis tricolor
+n12419037	amaryllis
+n12419878	salsilla, Bomarea edulis
+n12420124	salsilla, Bomarea salsilla
+n12420535	blood lily
+n12420722	Cape tulip, Haemanthus coccineus
+n12421137	hippeastrum, Hippeastrum puniceum
+n12421467	narcissus
+n12421683	daffodil, Narcissus pseudonarcissus
+n12421917	jonquil, Narcissus jonquilla
+n12422129	jonquil
+n12422559	Jacobean lily, Aztec lily, Strekelia formosissima
+n12425281	liliaceous plant
+n12426623	mountain lily, Lilium auratum
+n12426749	Canada lily, wild yellow lily, meadow lily, wild meadow lily, Lilium canadense
+n12427184	tiger lily, leopard lily, pine lily, Lilium catesbaei
+n12427391	Columbia tiger lily, Oregon lily, Lilium columbianum
+n12427566	tiger lily, devil lily, kentan, Lilium lancifolium
+n12427757	Easter lily, Bermuda lily, white trumpet lily, Lilium longiflorum
+n12427946	coast lily, Lilium maritinum
+n12428076	Turk's-cap, martagon, Lilium martagon
+n12428242	Michigan lily, Lilium michiganense
+n12428412	leopard lily, panther lily, Lilium pardalinum
+n12428747	Turk's-cap, Turk's cap-lily, Lilium superbum
+n12429352	African lily, African tulip, blue African lily, Agapanthus africanus
+n12430198	colicroot, colic root, crow corn, star grass, unicorn root
+n12430471	ague root, ague grass, Aletris farinosa
+n12430675	yellow colicroot, Aletris aurea
+n12431434	alliaceous plant
+n12432069	Hooker's onion, Allium acuminatum
+n12432356	wild leek, Levant garlic, kurrat, Allium ampeloprasum
+n12432574	Canada garlic, meadow leek, rose leek, Allium canadense
+n12432707	keeled garlic, Allium carinatum
+n12433081	onion
+n12433178	shallot, eschalot, multiplier onion, Allium cepa aggregatum, Allium ascalonicum
+n12433769	nodding onion, nodding wild onion, lady's leek, Allium cernuum
+n12433952	Welsh onion, Japanese leek, Allium fistulosum
+n12434106	red-skinned onion, Allium haematochiton
+n12434483	daffodil garlic, flowering onion, Naples garlic, Allium neopolitanum
+n12434634	few-flowered leek, Allium paradoxum
+n12434775	garlic, Allium sativum
+n12434985	sand leek, giant garlic, Spanish garlic, rocambole, Allium scorodoprasum
+n12435152	chives, chive, cive, schnittlaugh, Allium schoenoprasum
+n12435486	crow garlic, false garlic, field garlic, stag's garlic, wild garlic, Allium vineale
+n12435649	wild garlic, wood garlic, Ramsons, Allium ursinum
+n12435777	garlic chive, Chinese chive, Oriental garlic, Allium tuberosum
+n12435965	round-headed leek, Allium sphaerocephalum
+n12436090	three-cornered leek, triquetrous leek, Allium triquetrum
+n12436907	cape aloe, Aloe ferox
+n12437513	kniphofia, tritoma, flame flower, flame-flower, flameflower
+n12437769	poker plant, Kniphofia uvaria
+n12437930	red-hot poker, Kniphofia praecox
+n12439154	fly poison, Amianthum muscaetoxicum, Amianthum muscitoxicum
+n12439830	amber lily, Anthericum torreyi
+n12441183	asparagus, edible asparagus, Asparagus officinales
+n12441390	asparagus fern, Asparagus setaceous, Asparagus plumosus
+n12441552	smilax, Asparagus asparagoides
+n12441958	asphodel
+n12442548	Jacob's rod
+n12443323	aspidistra, cast-iron plant, bar-room plant, Aspidistra elatio
+n12443736	coral drops, Bessera elegans
+n12444095	Christmas bells
+n12444898	climbing onion, Bowiea volubilis
+n12446200	mariposa, mariposa tulip, mariposa lily
+n12446519	globe lily, fairy lantern
+n12446737	cat's-ear
+n12446908	white globe lily, white fairy lantern, Calochortus albus
+n12447121	yellow globe lily, golden fairy lantern, Calochortus amabilis
+n12447346	rose globe lily, Calochortus amoenus
+n12447581	star tulip, elegant cat's ears, Calochortus elegans
+n12447891	desert mariposa tulip, Calochortus kennedyi
+n12448136	yellow mariposa tulip, Calochortus luteus
+n12448361	sagebrush mariposa tulip, Calochortus macrocarpus
+n12448700	sego lily, Calochortus nuttallii
+n12449296	camas, camass, quamash, camosh, camash
+n12449526	common camas, Camassia quamash
+n12449784	Leichtlin's camas, Camassia leichtlinii
+n12449934	wild hyacinth, indigo squill, Camassia scilloides
+n12450344	dogtooth violet, dogtooth, dog's-tooth violet
+n12450607	white dogtooth violet, white dog's-tooth violet, blonde lilian, Erythronium albidum
+n12450840	yellow adder's tongue, trout lily, amberbell, Erythronium americanum
+n12451070	European dogtooth, Erythronium dens-canis
+n12451240	fawn lily, Erythronium californicum
+n12451399	glacier lily, snow lily, Erythronium grandiflorum
+n12451566	avalanche lily, Erythronium montanum
+n12451915	fritillary, checkered lily
+n12452256	mission bells, rice-grain fritillary, Fritillaria affinis, Fritillaria lanceolata, Fritillaria mutica
+n12452480	mission bells, black fritillary, Fritillaria biflora
+n12452673	stink bell, Fritillaria agrestis
+n12452836	crown imperial, Fritillaria imperialis
+n12453018	white fritillary, Fritillaria liliaceae
+n12453186	snake's head fritillary, guinea-hen flower, checkered daffodil, leper lily, Fritillaria meleagris
+n12453714	adobe lily, pink fritillary, Fritillaria pluriflora
+n12453857	scarlet fritillary, Fritillaria recurva
+n12454159	tulip
+n12454436	dwarf tulip, Tulipa armena, Tulipa suaveolens
+n12454556	lady tulip, candlestick tulip, Tulipa clusiana
+n12454705	Tulipa gesneriana
+n12454793	cottage tulip
+n12454949	Darwin tulip
+n12455950	gloriosa, glory lily, climbing lily, creeping lily, Gloriosa superba
+n12457091	lemon lily, Hemerocallis lilio-asphodelus, Hemerocallis flava
+n12458550	common hyacinth, Hyacinthus orientalis
+n12458713	Roman hyacinth, Hyacinthus orientalis albulus
+n12458874	summer hyacinth, cape hyacinth, Hyacinthus candicans, Galtonia candicans
+n12459629	star-of-Bethlehem
+n12460146	bath asparagus, Prussian asparagus, Ornithogalum pyrenaicum
+n12460697	grape hyacinth
+n12460957	common grape hyacinth, Muscari neglectum
+n12461109	tassel hyacinth, Muscari comosum
+n12461466	scilla, squill
+n12461673	spring squill, Scilla verna, sea onion
+n12462032	false asphodel
+n12462221	Scotch asphodel, Tofieldia pusilla
+n12462582	sea squill, sea onion, squill, Urginea maritima
+n12462805	squill
+n12463134	butcher's broom, Ruscus aculeatus
+n12463743	bog asphodel
+n12463975	European bog asphodel, Narthecium ossifragum
+n12464128	American bog asphodel, Narthecium americanum
+n12464476	hellebore, false hellebore
+n12464649	white hellebore, American hellebore, Indian poke, bugbane, Veratrum viride
+n12465557	squaw grass, bear grass, Xerophyllum tenax
+n12466727	death camas, zigadene
+n12467018	alkali grass, Zigadenus elegans
+n12467197	white camas, Zigadenus glaucus
+n12467433	poison camas, Zigadenus nuttalli
+n12467592	grassy death camas, Zigadenus venenosus, Zigadenus venenosus gramineus
+n12468545	prairie wake-robin, prairie trillium, Trillium recurvatum
+n12468719	dwarf-white trillium, snow trillium, early wake-robin
+n12469517	herb Paris, Paris quadrifolia
+n12470092	sarsaparilla
+n12470512	bullbrier, greenbrier, catbrier, horse brier, horse-brier, brier, briar, Smilax rotundifolia
+n12470907	rough bindweed, Smilax aspera
+n12472024	clintonia, Clinton's lily
+n12473608	false lily of the valley, Maianthemum canadense
+n12473840	false lily of the valley, Maianthemum bifolium
+n12474167	Solomon's-seal
+n12474418	great Solomon's-seal, Polygonatum biflorum, Polygonatum commutatum
+n12475035	bellwort, merry bells, wild oats
+n12475242	strawflower, cornflower, Uvularia grandiflora
+n12475774	pia, Indian arrowroot, Tacca leontopetaloides, Tacca pinnatifida
+n12476510	agave, century plant, American aloe
+n12477163	American agave, Agave americana
+n12477401	sisal, Agave sisalana
+n12477583	maguey, cantala, Agave cantala
+n12477747	maguey, Agave atrovirens
+n12477983	Agave tequilana
+n12478768	cabbage tree, grass tree, Cordyline australis
+n12479537	dracaena
+n12480456	tuberose, Polianthes tuberosa
+n12480895	sansevieria, bowstring hemp
+n12481150	African bowstring hemp, African hemp, Sansevieria guineensis
+n12481289	Ceylon bowstring hemp, Sansevieria zeylanica
+n12481458	mother-in-law's tongue, snake plant, Sansevieria trifasciata
+n12482437	Spanish bayonet, Yucca aloifolia
+n12482668	Spanish bayonet, Yucca baccata
+n12482893	Joshua tree, Yucca brevifolia
+n12483282	soapweed, soap-weed, soap tree, Yucca elata
+n12483427	Adam's needle, Adam's needle-and-thread, spoonleaf yucca, needle palm, Yucca filamentosa
+n12483625	bear grass, Yucca glauca
+n12483841	Spanish dagger, Yucca gloriosa
+n12484244	Our Lord's candle, Yucca whipplei
+n12484784	water shamrock, buckbean, bogbean, bog myrtle, marsh trefoil, Menyanthes trifoliata
+n12485653	butterfly bush, buddleia
+n12485981	yellow jasmine, yellow jessamine, Carolina jasmine, evening trumpet flower, Gelsemium sempervirens
+n12486574	flax
+n12487058	calabar bean, ordeal bean
+n12488454	bonduc, bonduc tree, Caesalpinia bonduc, Caesalpinia bonducella
+n12488709	divi-divi, Caesalpinia coriaria
+n12489046	Mysore thorn, Caesalpinia decapetala, Caesalpinia sepiaria
+n12489676	brazilian ironwood, Caesalpinia ferrea
+n12489815	bird of paradise, poinciana, Caesalpinia gilliesii, Poinciana gilliesii
+n12490490	shingle tree, Acrocarpus fraxinifolius
+n12491017	mountain ebony, orchid tree, Bauhinia variegata
+n12491435	msasa, Brachystegia speciformis
+n12491826	cassia
+n12492106	golden shower tree, drumstick tree, purging cassia, pudding pipe tree, canafistola, canafistula, Cassia fistula
+n12492460	pink shower, pink shower tree, horse cassia, Cassia grandis
+n12492682	rainbow shower, Cassia javonica
+n12492900	horse cassia, Cassia roxburghii, Cassia marginata
+n12493208	carob, carob tree, carob bean tree, algarroba, Ceratonia siliqua
+n12493426	carob, carob bean, algarroba bean, algarroba, locust bean, locust pod
+n12493868	paloverde
+n12494794	royal poinciana, flamboyant, flame tree, peacock flower, Delonix regia, Poinciana regia
+n12495146	locust tree, locust
+n12495670	water locust, swamp locust, Gleditsia aquatica
+n12495895	honey locust, Gleditsia triacanthos
+n12496427	Kentucky coffee tree, bonduc, chicot, Gymnocladus dioica
+n12496949	logwood, logwood tree, campeachy, bloodwood tree, Haematoxylum campechianum
+n12497669	Jerusalem thorn, horsebean, Parkinsonia aculeata
+n12498055	palo verde, Parkinsonia florida, Cercidium floridum
+n12498457	Dalmatian laburnum, Petteria ramentacea, Cytisus ramentaceus
+n12499163	senna
+n12499757	avaram, tanner's cassia, Senna auriculata, Cassia auriculata
+n12499979	Alexandria senna, Alexandrian senna, true senna, tinnevelly senna, Indian senna, Senna alexandrina, Cassia acutifolia, Cassia augustifolia
+n12500309	wild senna, Senna marilandica, Cassia marilandica
+n12500518	sicklepod, Senna obtusifolia, Cassia tora
+n12500751	coffee senna, mogdad coffee, styptic weed, stinking weed, Senna occidentalis, Cassia occidentalis
+n12501202	tamarind, tamarind tree, tamarindo, Tamarindus indica
+n12504570	false indigo, bastard indigo, Amorpha californica
+n12504783	false indigo, bastard indigo, Amorpha fruticosa
+n12505253	hog peanut, wild peanut, Amphicarpaea bracteata, Amphicarpa bracteata
+n12506181	angelim, andelmin
+n12506341	cabbage bark, cabbage-bark tree, cabbage tree, Andira inermis
+n12506991	kidney vetch, Anthyllis vulneraria
+n12507379	groundnut, groundnut vine, Indian potato, potato bean, wild bean, Apios americana, Apios tuberosa
+n12507823	rooibos, Aspalathus linearis, Aspalathus cedcarbergensis
+n12508309	milk vetch, milk-vetch
+n12508618	alpine milk vetch, Astragalus alpinus
+n12508762	purple milk vetch, Astragalus danicus
+n12509109	camwood, African sandalwood, Baphia nitida
+n12509476	wild indigo, false indigo
+n12509665	blue false indigo, Baptisia australis
+n12509821	white false indigo, Baptisia lactea
+n12509993	indigo broom, horsefly weed, rattle weed, Baptisia tinctoria
+n12510343	dhak, dak, palas, Butea frondosa, Butea monosperma
+n12510774	pigeon pea, pigeon-pea plant, cajan pea, catjang pea, red gram, dhal, dahl, Cajanus cajan
+n12511488	sword bean, Canavalia gladiata
+n12511856	pea tree, caragana
+n12512095	Siberian pea tree, Caragana arborescens
+n12512294	Chinese pea tree, Caragana sinica
+n12512674	Moreton Bay chestnut, Australian chestnut
+n12513172	butterfly pea, Centrosema virginianum
+n12513613	Judas tree, love tree, Circis siliquastrum
+n12513933	redbud, Cercis canadensis
+n12514138	western redbud, California redbud, Cercis occidentalis
+n12514592	tagasaste, Chamaecytisus palmensis, Cytesis proliferus
+n12514992	weeping tree broom
+n12515393	flame pea
+n12515711	chickpea, chickpea plant, Egyptian pea, Cicer arietinum
+n12515925	chickpea, garbanzo
+n12516165	Kentucky yellowwood, gopherwood, Cladrastis lutea, Cladrastis kentukea
+n12516584	glory pea, clianthus
+n12516828	desert pea, Sturt pea, Sturt's desert pea, Clianthus formosus, Clianthus speciosus
+n12517077	parrot's beak, parrot's bill, Clianthus puniceus
+n12517445	butterfly pea, Clitoria mariana
+n12517642	blue pea, butterfly pea, Clitoria turnatea
+n12518013	telegraph plant, semaphore plant, Codariocalyx motorius, Desmodium motorium, Desmodium gyrans
+n12518481	bladder senna, Colutea arborescens
+n12519089	axseed, crown vetch, Coronilla varia
+n12519563	crotalaria, rattlebox
+n12520406	guar, cluster bean, Cyamopsis tetragonolobus, Cyamopsis psoraloides
+n12521186	white broom, white Spanish broom, Cytisus albus, Cytisus multiflorus
+n12521394	common broom, Scotch broom, green broom, Cytisus scoparius
+n12522188	rosewood, rosewood tree
+n12522678	Indian blackwood, East Indian rosewood, East India rosewood, Indian rosewood, Dalbergia latifolia
+n12522894	sissoo, sissu, sisham, Dalbergia sissoo
+n12523141	kingwood, kingwood tree, Dalbergia cearensis
+n12523475	Brazilian rosewood, caviuna wood, jacaranda, Dalbergia nigra
+n12523850	cocobolo, Dalbergia retusa
+n12524188	blackwood, blackwood tree
+n12525168	bitter pea
+n12525513	derris
+n12525753	derris root, tuba root, Derris elliptica
+n12526178	prairie mimosa, prickle-weed, Desmanthus ilinoensis
+n12526516	tick trefoil, beggar lice, beggar's lice
+n12526754	beggarweed, Desmodium tortuosum, Desmodium purpureum
+n12527081	Australian pea, Dipogon lignosus, Dolichos lignosus
+n12527738	coral tree, erythrina
+n12528109	kaffir boom, Cape kafferboom, Erythrina caffra
+n12528382	coral bean tree, Erythrina corallodendrum
+n12528549	ceibo, crybaby tree, cry-baby tree, common coral tree, Erythrina crista-galli
+n12528768	kaffir boom, Transvaal kafferboom, Erythrina lysistemon
+n12528974	Indian coral tree, Erythrina variegata, Erythrina Indica
+n12529220	cork tree, Erythrina vespertilio
+n12529500	goat's rue, goat rue, Galega officinalis
+n12529905	poison bush, poison pea, gastrolobium
+n12530629	Spanish broom, Spanish gorse, Genista hispanica
+n12530818	woodwaxen, dyer's greenweed, dyer's-broom, dyeweed, greenweed, whin, woadwaxen, Genista tinctoria
+n12531328	chanar, chanal, Geoffroea decorticans
+n12531727	gliricidia
+n12532564	soy, soybean, soya bean
+n12532886	licorice, liquorice, Glycyrrhiza glabra
+n12533190	wild licorice, wild liquorice, American licorice, American liquorice, Glycyrrhiza lepidota
+n12533437	licorice root
+n12534208	Western Australia coral pea, Hardenbergia comnptoniana
+n12534625	sweet vetch, Hedysarum boreale
+n12534862	French honeysuckle, sulla, Hedysarum coronarium
+n12536291	anil, Indigofera suffruticosa, Indigofera anil
+n12537253	scarlet runner, running postman, Kennedia prostrata
+n12537569	hyacinth bean, bonavist, Indian bean, Egyptian bean, Lablab purpureus, Dolichos lablab
+n12538209	Scotch laburnum, Alpine golden chain, Laburnum alpinum
+n12539074	vetchling
+n12539306	wild pea
+n12539832	everlasting pea
+n12540250	beach pea, sea pea, Lathyrus maritimus, Lathyrus japonicus
+n12540647	grass vetch, grass vetchling, Lathyrus nissolia
+n12540966	marsh pea, Lathyrus palustris
+n12541157	common vetchling, meadow pea, yellow vetchling, Lathyrus pratensis
+n12541403	grass pea, Indian pea, khesari, Lathyrus sativus
+n12542043	Tangier pea, Tangier peavine, Lalthyrus tingitanus
+n12542240	heath pea, earth-nut pea, earthnut pea, tuberous vetch, Lathyrus tuberosus
+n12543186	bicolor lespediza, ezo-yama-hagi, Lespedeza bicolor
+n12543455	japanese clover, japan clover, jap clover, Lespedeza striata
+n12543639	Korean lespedeza, Lespedeza stipulacea
+n12543826	sericea lespedeza, Lespedeza sericea, Lespedeza cuneata
+n12544240	lentil, lentil plant, Lens culinaris
+n12544539	lentil
+n12545232	prairie bird's-foot trefoil, compass plant, prairie lotus, prairie trefoil, Lotus americanus
+n12545635	bird's foot trefoil, bird's foot clover, babies' slippers, bacon and eggs, Lotus corniculatus
+n12545865	winged pea, asparagus pea, Lotus tetragonolobus
+n12546183	lupine, lupin
+n12546420	white lupine, field lupine, wolf bean, Egyptian lupine, Lupinus albus
+n12546617	tree lupine, Lupinus arboreus
+n12546962	wild lupine, sundial lupine, Indian beet, old-maid's bonnet, Lupinus perennis
+n12547215	bluebonnet, buffalo clover, Texas bluebonnet, Lupinus subcarnosus
+n12547503	Texas bluebonnet, Lupinus texensis
+n12548280	medic, medick, trefoil
+n12548564	moon trefoil, Medicago arborea
+n12548804	sickle alfalfa, sickle lucerne, sickle medick, Medicago falcata
+n12549005	Calvary clover, Medicago intertexta, Medicago echinus
+n12549192	black medick, hop clover, yellow trefoil, nonesuch clover, Medicago lupulina
+n12549420	alfalfa, lucerne, Medicago sativa
+n12549799	millettia
+n12550210	mucuna
+n12550408	cowage, velvet bean, Bengal bean, Benghal bean, Florida bean, Mucuna pruriens utilis, Mucuna deeringiana, Mucuna aterrima, Stizolobium deeringiana
+n12551173	tolu tree, tolu balsam tree, Myroxylon balsamum, Myroxylon toluiferum
+n12551457	Peruvian balsam, Myroxylon pereirae, Myroxylon balsamum pereirae
+n12552309	sainfoin, sanfoin, holy clover, esparcet, Onobrychis viciifolia, Onobrychis viciaefolia
+n12552893	restharrow, rest-harrow, Ononis repens
+n12553742	bead tree, jumby bean, jumby tree, Ormosia monosperma
+n12554029	jumby bead, jumbie bead, Ormosia coarctata
+n12554526	locoweed, crazyweed, crazy weed
+n12554729	purple locoweed, purple loco, Oxytropis lambertii
+n12554911	tumbleweed
+n12555255	yam bean, Pachyrhizus erosus
+n12555859	shamrock pea, Parochetus communis
+n12556656	pole bean
+n12557064	kidney bean, frijol, frijole
+n12557438	haricot
+n12557556	wax bean
+n12557681	scarlet runner, scarlet runner bean, Dutch case-knife bean, runner bean, Phaseolus coccineus, Phaseolus multiflorus
+n12558230	lima bean, lima bean plant, Phaseolus limensis
+n12558425	sieva bean, butter bean, butter-bean plant, lima bean, Phaseolus lunatus
+n12558680	tepary bean, Phaseolus acutifolius latifolius
+n12559044	chaparral pea, stingaree-bush, Pickeringia montana
+n12559518	Jamaica dogwood, fish fuddle, Piscidia piscipula, Piscidia erythrina
+n12560282	pea
+n12560621	garden pea
+n12560775	edible-pod pea, edible-podded pea, Pisum sativum macrocarpon
+n12561169	sugar snap pea, snap pea
+n12561309	field pea, field-pea plant, Austrian winter pea, Pisum sativum arvense, Pisum arvense
+n12561594	field pea
+n12562141	common flat pea, native holly, Playlobium obtusangulum
+n12562577	quira
+n12562785	roble, Platymiscium trinitatis
+n12563045	Panama redwood tree, Panama redwood, Platymiscium pinnatum
+n12563702	Indian beech, Pongamia glabra
+n12564083	winged bean, winged pea, goa bean, goa bean vine, Manila bean, Psophocarpus tetragonolobus
+n12564613	breadroot, Indian breadroot, pomme blanche, pomme de prairie, Psoralea esculenta
+n12565102	bloodwood tree, kiaat, Pterocarpus angolensis
+n12565912	kino, Pterocarpus marsupium
+n12566331	red sandalwood, red sanders, red sanderswood, red saunders, Pterocarpus santalinus
+n12566954	kudzu, kudzu vine, Pueraria lobata
+n12567950	bristly locust, rose acacia, moss locust, Robinia hispida
+n12568186	black locust, yellow locust, Robinia pseudoacacia
+n12568649	clammy locust, Robinia viscosa
+n12569037	carib wood, Sabinea carinalis
+n12569616	Colorado River hemp, Sesbania exaltata
+n12569851	scarlet wisteria tree, vegetable hummingbird, Sesbania grandiflora
+n12570394	Japanese pagoda tree, Chinese scholartree, Chinese scholar tree, Sophora japonica, Sophora sinensis
+n12570703	mescal bean, coral bean, frijolito, frijolillo, Sophora secundiflora
+n12570972	kowhai, Sophora tetraptera
+n12571781	jade vine, emerald creeper, Strongylodon macrobotrys
+n12572546	hoary pea
+n12572759	bastard indigo, Tephrosia purpurea
+n12572858	catgut, goat's rue, wild sweet pea, Tephrosia virginiana
+n12573256	bush pea
+n12573474	false lupine, golden pea, yellow pea, Thermopsis macrophylla
+n12573647	Carolina lupine, Thermopsis villosa
+n12573911	tipu, tipu tree, yellow jacaranda, pride of Bolivia
+n12574320	bird's foot trefoil, Trigonella ornithopodioides
+n12574470	fenugreek, Greek clover, Trigonella foenumgraecum
+n12574866	gorse, furze, whin, Irish gorse, Ulex europaeus
+n12575322	vetch
+n12575812	tufted vetch, bird vetch, Calnada pea, Vicia cracca
+n12576323	broad bean, fava bean, horsebean
+n12576451	bitter betch, Vicia orobus
+n12576695	bush vetch, Vicia sepium
+n12577362	moth bean, Vigna aconitifolia, Phaseolus aconitifolius
+n12577895	snailflower, snail-flower, snail flower, snail bean, corkscrew flower, Vigna caracalla, Phaseolus caracalla
+n12578255	mung, mung bean, green gram, golden gram, Vigna radiata, Phaseolus aureus
+n12578626	cowpea, cowpea plant, black-eyed pea, Vigna unguiculata, Vigna sinensis
+n12578916	cowpea, black-eyed pea
+n12579038	asparagus bean, yard-long bean, Vigna unguiculata sesquipedalis, Vigna sesquipedalis
+n12579404	swamp oak, Viminaria juncea, Viminaria denudata
+n12579822	keurboom, Virgilia capensis, Virgilia oroboides
+n12580012	keurboom, Virgilia divaricata
+n12580654	Japanese wistaria, Wisteria floribunda
+n12580786	Chinese wistaria, Wisteria chinensis
+n12580896	American wistaria, American wisteria, Wisteria frutescens
+n12581110	silky wisteria, Wisteria venusta
+n12582231	palm, palm tree
+n12582665	sago palm
+n12582846	feather palm
+n12583126	fan palm
+n12583401	palmetto
+n12583681	coyol, coyol palm, Acrocomia vinifera
+n12583855	grugru, gri-gri, grugru palm, macamba, Acrocomia aculeata
+n12584191	areca
+n12584365	betel palm, Areca catechu
+n12584715	sugar palm, gomuti, gomuti palm, Arenga pinnata
+n12585137	piassava palm, pissaba palm, Bahia piassava, bahia coquilla, Attalea funifera
+n12585373	coquilla nut
+n12585629	palmyra, palmyra palm, toddy palm, wine palm, lontar, longar palm, Borassus flabellifer
+n12586298	calamus
+n12586499	rattan, rattan palm, Calamus rotang
+n12586725	lawyer cane, Calamus australis
+n12586989	fishtail palm
+n12587132	wine palm, jaggery palm, kitul, kittul, kitul tree, toddy palm, Caryota urens
+n12587487	wax palm, Ceroxylon andicola, Ceroxylon alpinum
+n12587803	coconut, coconut palm, coco palm, coco, cocoa palm, coconut tree, Cocos nucifera
+n12588320	carnauba, carnauba palm, wax palm, Copernicia prunifera, Copernicia cerifera
+n12588780	caranday, caranda, caranda palm, wax palm, Copernicia australis, Copernicia alba
+n12589142	corozo, corozo palm
+n12589458	gebang palm, Corypha utan, Corypha gebanga
+n12589687	latanier, latanier palm
+n12589841	talipot, talipot palm, Corypha umbraculifera
+n12590232	oil palm
+n12590499	African oil palm, Elaeis guineensis
+n12590600	American oil palm, Elaeis oleifera
+n12590715	palm nut, palm kernel
+n12591017	cabbage palm, Euterpe oleracea
+n12591351	cabbage palm, cabbage tree, Livistona australis
+n12591702	true sago palm, Metroxylon sagu
+n12592058	nipa palm, Nipa fruticans
+n12592544	babassu, babassu palm, coco de macao, Orbignya phalerata, Orbignya spesiosa, Orbignya martiana
+n12592839	babassu nut
+n12593122	cohune palm, Orbignya cohune, cohune
+n12593341	cohune nut
+n12593994	date palm, Phoenix dactylifera
+n12594324	ivory palm, ivory-nut palm, ivory plant, Phytelephas macrocarpa
+n12594989	raffia palm, Raffia farinifera, Raffia ruffia
+n12595699	bamboo palm, Raffia vinifera
+n12595964	lady palm
+n12596148	miniature fan palm, bamboo palm, fern rhapis, Rhapis excelsa
+n12596345	reed rhapis, slender lady palm, Rhapis humilis
+n12596709	royal palm, Roystonea regia
+n12596849	cabbage palm, Roystonea oleracea
+n12597134	cabbage palmetto, cabbage palm, Sabal palmetto
+n12597466	saw palmetto, scrub palmetto, Serenoa repens
+n12597798	thatch palm, thatch tree, silver thatch, broom palm, Thrinax parviflora
+n12598027	key palm, silvertop palmetto, silver thatch, Thrinax microcarpa, Thrinax morrisii, Thrinax keyensis
+n12599185	English plantain, narrow-leaved plantain, ribgrass, ribwort, ripple-grass, buckthorn, Plantago lanceolata
+n12599435	broad-leaved plantain, common plantain, white-man's foot, whiteman's foot, cart-track plant, Plantago major
+n12599661	hoary plantain, Plantago media
+n12599874	fleawort, psyllium, Spanish psyllium, Plantago psyllium
+n12600095	rugel's plantain, broad-leaved plantain, Plantago rugelii
+n12600267	hoary plantain, Plantago virginica
+n12601494	buckwheat, Polygonum fagopyrum, Fagopyrum esculentum
+n12601805	prince's-feather, princess feather, kiss-me-over-the-garden-gate, prince's-plume, Polygonum orientale
+n12602262	eriogonum
+n12602434	umbrella plant, Eriogonum allenii
+n12602612	wild buckwheat, California buckwheat, Erigonum fasciculatum
+n12602980	rhubarb, rhubarb plant
+n12603273	Himalayan rhubarb, Indian rhubarb, red-veined pie plant, Rheum australe, Rheum emodi
+n12603449	pie plant, garden rhubarb, Rheum cultorum, Rheum rhabarbarum, Rheum rhaponticum
+n12603672	Chinese rhubarb, Rheum palmatum
+n12604228	sour dock, garden sorrel, Rumex acetosa
+n12604460	sheep sorrel, sheep's sorrel, Rumex acetosella
+n12604639	bitter dock, broad-leaved dock, yellow dock, Rumex obtusifolius
+n12604845	French sorrel, garden sorrel, Rumex scutatus
+n12605683	yellow-eyed grass
+n12606438	commelina
+n12606545	spiderwort, dayflower
+n12607456	pineapple, pineapple plant, Ananas comosus
+n12609379	pipewort, Eriocaulon aquaticum
+n12610328	water hyacinth, water orchid, Eichhornia crassipes, Eichhornia spesiosa
+n12610740	water star grass, mud plantain, Heteranthera dubia
+n12611640	naiad, water nymph
+n12612170	water plantain, Alisma plantago-aquatica
+n12612811	narrow-leaved water plantain
+n12613706	hydrilla, Hydrilla verticillata
+n12614096	American frogbit, Limnodium spongia
+n12614477	waterweed
+n12614625	Canadian pondweed, Elodea canadensis
+n12615232	tape grass, eelgrass, wild celery, Vallisneria spiralis
+n12615710	pondweed
+n12616248	curled leaf pondweed, curly pondweed, Potamogeton crispus
+n12616630	loddon pondweed, Potamogeton nodosus, Potamogeton americanus
+n12616996	frog's lettuce
+n12617559	arrow grass, Triglochin maritima
+n12618146	horned pondweed, Zannichellia palustris
+n12618727	eelgrass, grass wrack, sea wrack, Zostera marina
+n12620196	rose, rosebush
+n12620546	hip, rose hip, rosehip
+n12620969	banksia rose, Rosa banksia
+n12621410	damask rose, summer damask rose, Rosa damascena
+n12621619	sweetbrier, sweetbriar, brier, briar, eglantine, Rosa eglanteria
+n12621945	Cherokee rose, Rosa laevigata
+n12622297	musk rose, Rosa moschata
+n12622875	agrimonia, agrimony
+n12623077	harvest-lice, Agrimonia eupatoria
+n12623211	fragrant agrimony, Agrimonia procera
+n12623818	alderleaf Juneberry, alder-leaved serviceberry, Amelanchier alnifolia
+n12624381	flowering quince
+n12624568	japonica, maule's quince, Chaenomeles japonica
+n12625003	coco plum, coco plum tree, cocoa plum, icaco, Chrysobalanus icaco
+n12625383	cotoneaster
+n12625670	Cotoneaster dammeri
+n12625823	Cotoneaster horizontalis
+n12626674	parsley haw, parsley-leaved thorn, Crataegus apiifolia, Crataegus marshallii
+n12626878	scarlet haw, Crataegus biltmoreana
+n12627119	blackthorn, pear haw, pear hawthorn, Crataegus calpodendron, Crataegus tomentosa
+n12627347	cockspur thorn, cockspur hawthorn, Crataegus crus-galli
+n12627526	mayhaw, summer haw, Crataegus aestivalis
+n12628356	red haw, downy haw, Crataegus mollis, Crataegus coccinea mollis
+n12628705	red haw, Crataegus pedicellata, Crataegus coccinea
+n12628986	quince, quince bush, Cydonia oblonga
+n12629305	mountain avens, Dryas octopetala
+n12629666	loquat, loquat tree, Japanese medlar, Japanese plum, Eriobotrya japonica
+n12630763	beach strawberry, Chilean strawberry, Fragaria chiloensis
+n12630999	Virginia strawberry, scarlet strawberry, Fragaria virginiana
+n12631331	avens
+n12631637	yellow avens, Geum alleppicum strictum, Geum strictum
+n12631932	yellow avens, Geum macrophyllum
+n12632335	prairie smoke, purple avens, Geum triflorum
+n12632733	bennet, white avens, Geum virginianum
+n12633061	toyon, tollon, Christmasberry, Christmas berry, Heteromeles arbutifolia, Photinia arbutifolia
+n12633638	apple tree
+n12633994	apple, orchard apple tree, Malus pumila
+n12634211	wild apple, crab apple, crabapple
+n12634429	crab apple, crabapple, cultivated crab apple
+n12634734	Siberian crab, Siberian crab apple, cherry apple, cherry crab, Malus baccata
+n12634986	wild crab, Malus sylvestris
+n12635151	American crab apple, garland crab, Malus coronaria
+n12635359	Oregon crab apple, Malus fusca
+n12635532	Southern crab apple, flowering crab, Malus angustifolia
+n12635744	Iowa crab, Iowa crab apple, prairie crab, western crab apple, Malus ioensis
+n12635955	Bechtel crab, flowering crab
+n12636224	medlar, medlar tree, Mespilus germanica
+n12636885	cinquefoil, five-finger
+n12637123	silverweed, goose-tansy, goose grass, Potentilla anserina
+n12637485	salad burnet, burnet bloodwort, pimpernel, Poterium sanguisorba
+n12638218	plum, plum tree
+n12638556	wild plum, wild plum tree
+n12638753	Allegheny plum, Alleghany plum, sloe, Prunus alleghaniensis
+n12638964	American red plum, August plum, goose plum, Prunus americana
+n12639168	chickasaw plum, hog plum, hog plum bush, Prunus angustifolia
+n12639376	beach plum, beach plum bush, Prunus maritima
+n12639584	common plum, Prunus domestica
+n12639736	bullace, Prunus insititia
+n12639910	damson plum, damson plum tree, Prunus domestica insititia
+n12640081	big-tree plum, Prunus mexicana
+n12640284	Canada plum, Prunus nigra
+n12640435	plumcot, plumcot tree
+n12640607	apricot, apricot tree
+n12640839	Japanese apricot, mei, Prunus mume
+n12641007	common apricot, Prunus armeniaca
+n12641180	purple apricot, black apricot, Prunus dasycarpa
+n12641413	cherry, cherry tree
+n12641931	wild cherry, wild cherry tree
+n12642090	wild cherry
+n12642200	sweet cherry, Prunus avium
+n12642435	heart cherry, oxheart, oxheart cherry
+n12642600	gean, mazzard, mazzard cherry
+n12642964	capulin, capulin tree, Prunus capuli
+n12643113	cherry laurel, laurel cherry, mock orange, wild orange, Prunus caroliniana
+n12643313	cherry plum, myrobalan, myrobalan plum, Prunus cerasifera
+n12643473	sour cherry, sour cherry tree, Prunus cerasus
+n12643688	amarelle, Prunus cerasus caproniana
+n12643877	morello, Prunus cerasus austera
+n12644283	marasca
+n12644902	almond tree
+n12645174	almond, sweet almond, Prunus dulcis, Prunus amygdalus, Amygdalus communis
+n12645530	bitter almond, Prunus dulcis amara, Amygdalus communis amara
+n12646072	jordan almond
+n12646197	dwarf flowering almond, Prunus glandulosa
+n12646397	holly-leaved cherry, holly-leaf cherry, evergreen cherry, islay, Prunus ilicifolia
+n12646605	fuji, fuji cherry, Prunus incisa
+n12646740	flowering almond, oriental bush cherry, Prunus japonica
+n12646950	cherry laurel, laurel cherry, Prunus laurocerasus
+n12647231	Catalina cherry, Prunus lyonii
+n12647376	bird cherry, bird cherry tree
+n12647560	hagberry tree, European bird cherry, common bird cherry, Prunus padus
+n12647787	hagberry
+n12647893	pin cherry, Prunus pensylvanica
+n12648045	peach, peach tree, Prunus persica
+n12648196	nectarine, nectarine tree, Prunus persica nectarina
+n12648424	sand cherry, Prunus pumila, Prunus pumilla susquehanae, Prunus susquehanae, Prunus cuneata
+n12648693	Japanese plum, Prunus salicina
+n12648888	black cherry, black cherry tree, rum cherry, Prunus serotina
+n12649065	flowering cherry
+n12649317	oriental cherry, Japanese cherry, Japanese flowering cherry, Prunus serrulata
+n12649539	Japanese flowering cherry, Prunus sieboldii
+n12649866	Sierra plum, Pacific plum, Prunus subcordata
+n12650038	rosebud cherry, winter flowering cherry, Prunus subhirtella
+n12650229	Russian almond, dwarf Russian almond, Prunus tenella
+n12650379	flowering almond, Prunus triloba
+n12650556	chokecherry, chokecherry tree, Prunus virginiana
+n12650805	chokecherry
+n12650915	western chokecherry, Prunus virginiana demissa, Prunus demissa
+n12651229	Pyracantha, pyracanth, fire thorn, firethorn
+n12651611	pear, pear tree, Pyrus communis
+n12651821	fruit tree
+n12653218	bramble bush
+n12653436	lawyerbush, lawyer bush, bush lawyer, Rubus cissoides, Rubus australis
+n12653633	stone bramble, Rubus saxatilis
+n12654227	sand blackberry, Rubus cuneifolius
+n12654857	boysenberry, boysenberry bush
+n12655062	loganberry, Rubus loganobaccus, Rubus ursinus loganobaccus
+n12655245	American dewberry, Rubus canadensis
+n12655351	Northern dewberry, American dewberry, Rubus flagellaris
+n12655498	Southern dewberry, Rubus trivialis
+n12655605	swamp dewberry, swamp blackberry, Rubus hispidus
+n12655726	European dewberry, Rubus caesius
+n12655869	raspberry, raspberry bush
+n12656369	wild raspberry, European raspberry, framboise, Rubus idaeus
+n12656528	American raspberry, Rubus strigosus, Rubus idaeus strigosus
+n12656685	black raspberry, blackcap, blackcap raspberry, thimbleberry, Rubus occidentalis
+n12656909	salmonberry, Rubus spectabilis
+n12657082	salmonberry, salmon berry, thimbleberry, Rubus parviflorus
+n12657755	wineberry, Rubus phoenicolasius
+n12658118	mountain ash
+n12658308	rowan, rowan tree, European mountain ash, Sorbus aucuparia
+n12658481	rowanberry
+n12658603	American mountain ash, Sorbus americana
+n12658715	Western mountain ash, Sorbus sitchensis
+n12658846	service tree, sorb apple, sorb apple tree, Sorbus domestica
+n12659064	wild service tree, Sorbus torminalis
+n12659356	spirea, spiraea
+n12659539	bridal wreath, bridal-wreath, Saint Peter's wreath, St. Peter's wreath, Spiraea prunifolia
+n12660601	madderwort, rubiaceous plant
+n12661045	Indian madder, munjeet, Rubia cordifolia
+n12661227	madder, Rubia tinctorum
+n12661538	woodruff
+n12662074	dagame, lemonwood tree, Calycophyllum candidissimum
+n12662379	blolly, West Indian snowberry, Chiococca alba
+n12662772	coffee, coffee tree
+n12663023	Arabian coffee, Coffea arabica
+n12663254	Liberian coffee, Coffea liberica
+n12663359	robusta coffee, Rio Nunez coffee, Coffea robusta, Coffea canephora
+n12663804	cinchona, chinchona
+n12664005	Cartagena bark, Cinchona cordifolia, Cinchona lancifolia
+n12664187	calisaya, Cinchona officinalis, Cinchona ledgeriana, Cinchona calisaya
+n12664469	cinchona tree, Cinchona pubescens
+n12664710	cinchona, cinchona bark, Peruvian bark, Jesuit's bark
+n12665048	bedstraw
+n12665271	sweet woodruff, waldmeister, woodruff, fragrant bedstraw, Galium odoratum, Asperula odorata
+n12665659	Northern bedstraw, Northern snow bedstraw, Galium boreale
+n12665857	yellow bedstraw, yellow cleavers, Our Lady's bedstraw, Galium verum
+n12666050	wild licorice, Galium lanceolatum
+n12666159	cleavers, clivers, goose grass, catchweed, spring cleavers, Galium aparine
+n12666369	wild madder, white madder, white bedstraw, infant's-breath, false baby's breath, Galium mollugo
+n12666965	cape jasmine, cape jessamine, Gardenia jasminoides, Gardenia augusta
+n12667406	genipa
+n12667582	genipap fruit, jagua, marmalade box, Genipa Americana
+n12667964	hamelia
+n12668131	scarlet bush, scarlet hamelia, coloradillo, Hamelia patens, Hamelia erecta
+n12669803	lemonwood, lemon-wood, lemonwood tree, lemon-wood tree, Psychotria capensis
+n12670334	negro peach, Sarcocephalus latifolius, Sarcocephalus esculentus
+n12670758	wild medlar, wild medlar tree, medlar, Vangueria infausta
+n12670962	Spanish tamarind, Vangueria madagascariensis
+n12671651	abelia
+n12672289	bush honeysuckle, Diervilla sessilifolia
+n12673588	American twinflower, Linnaea borealis americana
+n12674120	honeysuckle
+n12674685	American fly honeysuckle, fly honeysuckle, Lonicera canadensis
+n12674895	Italian honeysuckle, Italian woodbine, Lonicera caprifolium
+n12675299	yellow honeysuckle, Lonicera flava
+n12675515	hairy honeysuckle, Lonicera hirsuta
+n12675876	Japanese honeysuckle, Lonicera japonica
+n12676134	Hall's honeysuckle, Lonicera japonica halliana
+n12676370	Morrow's honeysuckle, Lonicera morrowii
+n12676534	woodbine, Lonicera periclymenum
+n12676703	trumpet honeysuckle, coral honeysuckle, trumpet flower, trumpet vine, Lonicera sempervirens
+n12677120	European fly honeysuckle, European honeysuckle, Lonicera xylosteum
+n12677331	swamp fly honeysuckle
+n12677612	snowberry, common snowberry, waxberry, Symphoricarpos alba
+n12677841	coralberry, Indian currant, Symphoricarpos orbiculatus
+n12678794	blue elder, blue elderberry, Sambucus caerulea
+n12679023	dwarf elder, danewort, Sambucus ebulus
+n12679432	American red elder, red-berried elder, stinking elder, Sambucus pubens
+n12679593	European red elder, red-berried elder, Sambucus racemosa
+n12679876	feverroot, horse gentian, tinker's root, wild coffee, Triostium perfoliatum
+n12680402	cranberry bush, cranberry tree, American cranberry bush, highbush cranberry, Viburnum trilobum
+n12680652	wayfaring tree, twist wood, twistwood, Viburnum lantana
+n12680864	guelder rose, European cranberrybush, European cranberry bush, crampbark, cranberry tree, Viburnum opulus
+n12681376	arrow wood, Viburnum recognitum
+n12681579	black haw, Viburnum prunifolium
+n12681893	weigela, Weigela florida
+n12682411	teasel, teazel, teasle
+n12682668	common teasel, Dipsacus fullonum
+n12682882	fuller's teasel, Dipsacus sativus
+n12683096	wild teasel, Dipsacus sylvestris
+n12683407	scabious, scabiosa
+n12683571	sweet scabious, pincushion flower, mournful widow, Scabiosa atropurpurea
+n12683791	field scabious, Scabiosa arvensis
+n12684379	jewelweed, lady's earrings, orange balsam, celandine, touch-me-not, Impatiens capensis
+n12685431	geranium
+n12685831	cranesbill, crane's bill
+n12686077	wild geranium, spotted cranesbill, Geranium maculatum
+n12686274	meadow cranesbill, Geranium pratense
+n12686496	Richardson's geranium, Geranium richardsonii
+n12686676	herb robert, herbs robert, herb roberts, Geranium robertianum
+n12686877	sticky geranium, Geranium viscosissimum
+n12687044	dove's foot geranium, Geranium molle
+n12687462	rose geranium, sweet-scented geranium, Pelargonium graveolens
+n12687698	fish geranium, bedding geranium, zonal pelargonium, Pelargonium hortorum
+n12687957	ivy geranium, ivy-leaved geranium, hanging geranium, Pelargonium peltatum
+n12688187	apple geranium, nutmeg geranium, Pelargonium odoratissimum
+n12688372	lemon geranium, Pelargonium limoneum
+n12688716	storksbill, heron's bill
+n12689305	musk clover, muskus grass, white-stemmed filaree, Erodium moschatum
+n12690653	incense tree
+n12691428	elephant tree, Bursera microphylla
+n12691661	gumbo-limbo, Bursera simaruba
+n12692024	Boswellia carteri
+n12692160	salai, Boswellia serrata
+n12692521	balm of gilead, Commiphora meccanensis
+n12692714	myrrh tree, Commiphora myrrha
+n12693244	Protium heptaphyllum
+n12693352	Protium guianense
+n12693865	water starwort
+n12694486	barbados cherry, acerola, Surinam cherry, West Indian cherry, Malpighia glabra
+n12695144	mahogany, mahogany tree
+n12695975	chinaberry, chinaberry tree, China tree, Persian lilac, pride-of-India, azederach, azedarach, Melia azederach, Melia azedarach
+n12696492	neem, neem tree, nim tree, margosa, arishth, Azadirachta indica, Melia Azadirachta
+n12696830	neem seed
+n12697152	Spanish cedar, Spanish cedar tree, Cedrela odorata
+n12697514	satinwood, satinwood tree, Chloroxylon swietenia
+n12698027	African scented mahogany, cedar mahogany, sapele mahogany, Entandrophragma cylindricum
+n12698435	silver ash
+n12698598	native beech, flindosa, flindosy, Flindersia australis
+n12698774	bunji-bunji, Flindersia schottiana
+n12699031	African mahogany
+n12699301	lanseh tree, langsat, langset, Lansium domesticum
+n12699922	true mahogany, Cuban mahogany, Dominican mahogany, Swietinia mahogani
+n12700088	Honduras mahogany, Swietinia macrophylla
+n12700357	Philippine mahogany, Philippine cedar, kalantas, Toona calantas, Cedrela calantas
+n12702124	caracolito, Ruptiliocarpon caracolito
+n12703190	common wood sorrel, cuckoo bread, shamrock, Oxalis acetosella
+n12703383	Bermuda buttercup, English-weed, Oxalis pes-caprae, Oxalis cernua
+n12703557	creeping oxalis, creeping wood sorrel, Oxalis corniculata
+n12703716	goatsfoot, goat's foot, Oxalis caprina
+n12703856	violet wood sorrel, Oxalis violacea
+n12704041	oca, oka, Oxalis tuberosa, Oxalis crenata
+n12704343	carambola, carambola tree, Averrhoa carambola
+n12704513	bilimbi, Averrhoa bilimbi
+n12705013	milkwort
+n12705220	senega, Polygala alba
+n12705458	orange milkwort, yellow milkwort, candyweed, yellow bachelor's button, Polygala lutea
+n12705698	flowering wintergreen, gaywings, bird-on-the-wing, fringed polygala, Polygala paucifolia
+n12705978	Seneca snakeroot, Seneka snakeroot, senga root, senega root, senega snakeroot, Polygala senega
+n12706410	common milkwort, gand flower, Polygala vulgaris
+n12707199	rue, herb of grace, Ruta graveolens
+n12707781	citrus, citrus tree
+n12708293	orange, orange tree
+n12708654	sour orange, Seville orange, bitter orange, bitter orange tree, bigarade, marmalade orange, Citrus aurantium
+n12708941	bergamot, bergamot orange, Citrus bergamia
+n12709103	pomelo, pomelo tree, pummelo, shaddock, Citrus maxima, Citrus grandis, Citrus decumana
+n12709349	citron, citron tree, Citrus medica
+n12709688	grapefruit, Citrus paradisi
+n12709901	mandarin, mandarin orange, mandarin orange tree, Citrus reticulata
+n12710295	tangerine, tangerine tree
+n12710415	clementine, clementine tree
+n12710577	satsuma, satsuma tree
+n12710693	sweet orange, sweet orange tree, Citrus sinensis
+n12710917	temple orange, temple orange tree, tangor, king orange, Citrus nobilis
+n12711182	tangelo, tangelo tree, ugli fruit, Citrus tangelo
+n12711398	rangpur, rangpur lime, lemanderin, Citrus limonia
+n12711596	lemon, lemon tree, Citrus limon
+n12711817	sweet lemon, sweet lime, Citrus limetta
+n12711984	lime, lime tree, Citrus aurantifolia
+n12712320	citrange, citrange tree, Citroncirus webberi
+n12712626	fraxinella, dittany, burning bush, gas plant, Dictamnus alba
+n12713063	kumquat, cumquat, kumquat tree
+n12713358	marumi, marumi kumquat, round kumquat, Fortunella japonica
+n12713521	nagami, nagami kumquat, oval kumquat, Fortunella margarita
+n12713866	cork tree, Phellodendron amurense
+n12714254	trifoliate orange, trifoliata, wild orange, Poncirus trifoliata
+n12714755	prickly ash
+n12714949	toothache tree, sea ash, Zanthoxylum americanum, Zanthoxylum fraxineum
+n12715195	Hercules'-club, Hercules'-clubs, Hercules-club, Zanthoxylum clava-herculis
+n12715914	bitterwood tree
+n12716400	marupa, Simarouba amara
+n12716594	paradise tree, bitterwood, Simarouba glauca
+n12717072	ailanthus
+n12717224	tree of heaven, tree of the gods, Ailanthus altissima
+n12717644	wild mango, dika, wild mango tree, Irvingia gabonensis
+n12718074	pepper tree, Kirkia wilmsii
+n12718483	Jamaica quassia, bitterwood, Picrasma excelsa, Picrasma excelsum
+n12718995	quassia, bitterwood, Quassia amara
+n12719684	nasturtium
+n12719944	garden nasturtium, Indian cress, Tropaeolum majus
+n12720200	bush nasturtium, Tropaeolum minus
+n12720354	canarybird flower, canarybird vine, canary creeper, Tropaeolum peregrinum
+n12721122	bean caper, Syrian bean caper, Zygophyllum fabago
+n12721477	palo santo, Bulnesia sarmienti
+n12722071	lignum vitae, Guaiacum officinale
+n12723062	creosote bush, coville, hediondilla, Larrea tridentata
+n12723610	caltrop, devil's weed, Tribulus terestris
+n12724942	willow, willow tree
+n12725521	osier
+n12725738	white willow, Huntingdon willow, Salix alba
+n12725940	silver willow, silky willow, Salix alba sericea, Salix sericea
+n12726159	golden willow, Salix alba vitellina, Salix vitellina
+n12726357	cricket-bat willow, Salix alba caerulea
+n12726528	arctic willow, Salix arctica
+n12726670	weeping willow, Babylonian weeping willow, Salix babylonica
+n12726902	Wisconsin weeping willow, Salix pendulina, Salix blanda, Salix pendulina blanda
+n12727101	pussy willow, Salix discolor
+n12727301	sallow
+n12727518	goat willow, florist's willow, pussy willow, Salix caprea
+n12727729	peachleaf willow, peach-leaved willow, almond-leaves willow, Salix amygdaloides
+n12727960	almond willow, black Hollander, Salix triandra, Salix amygdalina
+n12728164	hoary willow, sage willow, Salix candida
+n12728322	crack willow, brittle willow, snap willow, Salix fragilis
+n12728508	prairie willow, Salix humilis
+n12728656	dwarf willow, Salix herbacea
+n12728864	grey willow, gray willow, Salix cinerea
+n12729023	arroyo willow, Salix lasiolepis
+n12729164	shining willow, Salix lucida
+n12729315	swamp willow, black willow, Salix nigra
+n12729521	bay willow, laurel willow, Salix pentandra
+n12729729	purple willow, red willow, red osier, basket willow, purple osier, Salix purpurea
+n12729950	balsam willow, Salix pyrifolia
+n12730143	creeping willow, Salix repens
+n12730370	Sitka willow, silky willow, Salix sitchensis
+n12730544	dwarf grey willow, dwarf gray willow, sage willow, Salix tristis
+n12730776	bearberry willow, Salix uva-ursi
+n12731029	common osier, hemp willow, velvet osier, Salix viminalis
+n12731401	poplar, poplar tree
+n12731835	balsam poplar, hackmatack, tacamahac, Populus balsamifera
+n12732009	white poplar, white aspen, abele, aspen poplar, silver-leaved poplar, Populus alba
+n12732252	grey poplar, gray poplar, Populus canescens
+n12732491	black poplar, Populus nigra
+n12732605	Lombardy poplar, Populus nigra italica
+n12732756	cottonwood
+n12732966	Eastern cottonwood, necklace poplar, Populus deltoides
+n12733218	black cottonwood, Western balsam poplar, Populus trichocarpa
+n12733428	swamp cottonwood, black cottonwood, downy poplar, swamp poplar, Populus heterophylla
+n12733647	aspen
+n12733870	quaking aspen, European quaking aspen, Populus tremula
+n12734070	American quaking aspen, American aspen, Populus tremuloides
+n12734215	Canadian aspen, bigtooth aspen, bigtoothed aspen, big-toothed aspen, large-toothed aspen, large tooth aspen, Populus grandidentata
+n12735160	sandalwood tree, true sandalwood, Santalum album
+n12736603	quandong, quandang, quandong tree, Eucarya acuminata, Fusanus acuminatus
+n12736999	rabbitwood, buffalo nut, Pyrularia pubera
+n12737383	Loranthaceae, family Loranthaceae, mistletoe family
+n12737898	mistletoe, Loranthus europaeus
+n12738259	American mistletoe, Arceuthobium pusillum
+n12739332	mistletoe, Viscum album, Old World mistletoe
+n12739966	American mistletoe, Phoradendron serotinum, Phoradendron flavescens
+n12740967	aalii
+n12741222	soapberry, soapberry tree
+n12741586	wild China tree, Sapindus drumondii, Sapindus marginatus
+n12741792	China tree, false dogwood, jaboncillo, chinaberry, Sapindus saponaria
+n12742290	akee, akee tree, Blighia sapida
+n12742741	soapberry vine
+n12742878	heartseed, Cardiospermum grandiflorum
+n12743009	balloon vine, heart pea, Cardiospermum halicacabum
+n12743352	longan, lungen, longanberry, Dimocarpus longan, Euphorbia litchi, Nephelium longana
+n12743823	harpullia
+n12743976	harpulla, Harpullia cupanioides
+n12744142	Moreton Bay tulipwood, Harpullia pendula
+n12744387	litchi, lichee, litchi tree, Litchi chinensis, Nephelium litchi
+n12744850	Spanish lime, Spanish lime tree, honey berry, mamoncillo, genip, ginep, Melicocca bijuga, Melicocca bijugatus
+n12745386	rambutan, rambotan, rambutan tree, Nephelium lappaceum
+n12745564	pulasan, pulassan, pulasan tree, Nephelium mutabile
+n12746884	pachysandra
+n12747120	Allegheny spurge, Allegheny mountain spurge, Pachysandra procumbens
+n12748248	bittersweet, American bittersweet, climbing bittersweet, false bittersweet, staff vine, waxwork, shrubby bittersweet, Celastrus scandens
+n12749049	spindle tree, spindleberry, spindleberry tree
+n12749456	winged spindle tree, Euonymous alatus
+n12749679	wahoo, burning bush, Euonymus atropurpureus
+n12749852	strawberry bush, wahoo, Euonymus americanus
+n12750076	evergreen bittersweet, Euonymus fortunei radicans, Euonymus radicans vegetus
+n12750767	cyrilla, leatherwood, white titi, Cyrilla racemiflora
+n12751172	titi, buckwheat tree, Cliftonia monophylla
+n12751675	crowberry
+n12752205	maple
+n12753007	silver maple, Acer saccharinum
+n12753245	sugar maple, rock maple, Acer saccharum
+n12753573	red maple, scarlet maple, swamp maple, Acer rubrum
+n12753762	moosewood, moose-wood, striped maple, striped dogwood, goosefoot maple, Acer pennsylvanicum
+n12754003	Oregon maple, big-leaf maple, Acer macrophyllum
+n12754174	dwarf maple, Rocky-mountain maple, Acer glabrum
+n12754311	mountain maple, mountain alder, Acer spicatum
+n12754468	vine maple, Acer circinatum
+n12754648	hedge maple, field maple, Acer campestre
+n12754781	Norway maple, Acer platanoides
+n12754981	sycamore, great maple, scottish maple, Acer pseudoplatanus
+n12755225	box elder, ash-leaved maple, Acer negundo
+n12755387	California box elder, Acer negundo Californicum
+n12755559	pointed-leaf maple, Acer argutum
+n12755727	Japanese maple, full moon maple, Acer japonicum
+n12755876	Japanese maple, Acer palmatum
+n12756457	holly
+n12757115	Chinese holly, Ilex cornuta
+n12757303	bearberry, possum haw, winterberry, Ilex decidua
+n12757458	inkberry, gallberry, gall-berry, evergreen winterberry, Ilex glabra
+n12757668	mate, Paraguay tea, Ilex paraguariensis
+n12757816	American holly, Christmas holly
+n12757930	low gallberry holly
+n12758014	tall gallberry holly
+n12758099	yaupon holly
+n12758176	deciduous holly
+n12758250	juneberry holly
+n12758325	largeleaf holly
+n12758399	Geogia holly
+n12758471	common winterberry holly
+n12758555	smooth winterberry holly
+n12759273	cashew, cashew tree, Anacardium occidentale
+n12759668	goncalo alves, Astronium fraxinifolium
+n12760539	Venetian sumac, wig tree, Cotinus coggygria
+n12760875	laurel sumac, Malosma laurina, Rhus laurina
+n12761284	mango, mango tree, Mangifera indica
+n12761702	pistachio, Pistacia vera, pistachio tree
+n12761905	terebinth, Pistacia terebinthus
+n12762049	mastic, mastic tree, lentisk, Pistacia lentiscus
+n12762405	Australian sumac, Rhodosphaera rhodanthema, Rhus rhodanthema
+n12762896	sumac, sumach, shumac
+n12763529	smooth sumac, scarlet sumac, vinegar tree, Rhus glabra
+n12764008	sugar-bush, sugar sumac, Rhus ovata
+n12764202	staghorn sumac, velvet sumac, Virginian sumac, vinegar tree, Rhus typhina
+n12764507	squawbush, squaw-bush, skunkbush, Rhus trilobata
+n12764978	aroeira blanca, Schinus chichita
+n12765115	pepper tree, molle, Peruvian mastic tree, Schinus molle
+n12765402	Brazilian pepper tree, Schinus terebinthifolius
+n12765846	hog plum, yellow mombin, yellow mombin tree, Spondias mombin
+n12766043	mombin, mombin tree, jocote, Spondias purpurea
+n12766595	poison ash, poison dogwood, poison sumac, Toxicodendron vernix, Rhus vernix
+n12766869	poison ivy, markweed, poison mercury, poison oak, Toxicodendron radicans, Rhus radicans
+n12767208	western poison oak, Toxicodendron diversilobum, Rhus diversiloba
+n12767423	eastern poison oak, Toxicodendron quercifolium, Rhus quercifolia, Rhus toxicodenedron
+n12767648	varnish tree, lacquer tree, Chinese lacquer tree, Japanese lacquer tree, Japanese varnish tree, Japanese sumac, Toxicodendron vernicifluum, Rhus verniciflua
+n12768369	horse chestnut, buckeye, Aesculus hippocastanum
+n12768682	buckeye, horse chestnut, conker
+n12768809	sweet buckeye
+n12768933	Ohio buckeye
+n12769065	dwarf buckeye, bottlebrush buckeye
+n12769219	red buckeye
+n12769318	particolored buckeye
+n12770529	ebony, ebony tree, Diospyros ebenum
+n12770892	marblewood, marble-wood, Andaman marble, Diospyros kurzii
+n12771085	marblewood, marble-wood
+n12771192	persimmon, persimmon tree
+n12771390	Japanese persimmon, kaki, Diospyros kaki
+n12771597	American persimmon, possumwood, Diospyros virginiana
+n12771890	date plum, Diospyros lotus
+n12772753	buckthorn
+n12772908	southern buckthorn, shittimwood, shittim, mock orange, Bumelia lycioides
+n12773142	false buckthorn, chittamwood, chittimwood, shittimwood, black haw, Bumelia lanuginosa
+n12773651	star apple, caimito, Chrysophyllum cainito
+n12773917	satinleaf, satin leaf, caimitillo, damson plum, Chrysophyllum oliviforme
+n12774299	balata, balata tree, beefwood, bully tree, Manilkara bidentata
+n12774641	sapodilla, sapodilla tree, Manilkara zapota, Achras zapota
+n12775070	gutta-percha tree, Palaquium gutta
+n12775393	gutta-percha tree
+n12775717	canistel, canistel tree, Pouteria campechiana nervosa
+n12775919	marmalade tree, mammee, sapote, Pouteria zapota, Calocarpum zapota
+n12776558	sweetleaf, Symplocus tinctoria
+n12776774	Asiatic sweetleaf, sapphire berry, Symplocus paniculata
+n12777436	styrax
+n12777680	snowbell, Styrax obassia
+n12777778	Japanese snowbell, Styrax japonicum
+n12777892	Texas snowbell, Texas snowbells, Styrax texana
+n12778398	silver-bell tree, silverbell tree, snowdrop tree, opossum wood, Halesia carolina, Halesia tetraptera
+n12778605	carnivorous plant
+n12779603	pitcher plant
+n12779851	common pitcher plant, huntsman's cup, huntsman's cups, Sarracenia purpurea
+n12780325	hooded pitcher plant, Sarracenia minor
+n12780563	huntsman's horn, huntsman's horns, yellow trumpet, yellow pitcher plant, trumpets, Sarracenia flava
+n12781940	tropical pitcher plant
+n12782530	sundew, sundew plant, daily dew
+n12782915	Venus's flytrap, Venus's flytraps, Dionaea muscipula
+n12783316	waterwheel plant, Aldrovanda vesiculosa
+n12783730	Drosophyllum lusitanicum
+n12784371	roridula
+n12784889	Australian pitcher plant, Cephalotus follicularis
+n12785724	sedum
+n12785889	stonecrop
+n12786273	rose-root, midsummer-men, Sedum rosea
+n12786464	orpine, orpin, livelong, live-forever, Sedum telephium
+n12786836	pinwheel, Aeonium haworthii
+n12787364	Christmas bush, Christmas tree, Ceratopetalum gummiferum
+n12788854	hortensia, Hydrangea macrophylla hortensis
+n12789054	fall-blooming hydrangea, Hydrangea paniculata
+n12789554	carpenteria, Carpenteria californica
+n12789977	decumary, Decumaria barbata, Decumaria barbara
+n12790430	deutzia
+n12791064	philadelphus
+n12791329	mock orange, syringa, Philadelphus coronarius
+n12793015	saxifrage, breakstone, rockfoil
+n12793284	yellow mountain saxifrage, Saxifraga aizoides
+n12793494	meadow saxifrage, fair-maids-of-France, Saxifraga granulata
+n12793695	mossy saxifrage, Saxifraga hypnoides
+n12793886	western saxifrage, Saxifraga occidentalis
+n12794135	purple saxifrage, Saxifraga oppositifolia
+n12794367	star saxifrage, starry saxifrage, Saxifraga stellaris
+n12794568	strawberry geranium, strawberry saxifrage, mother-of-thousands, Saxifraga stolonifera, Saxifraga sarmentosam
+n12794985	astilbe
+n12795209	false goatsbeard, Astilbe biternata
+n12795352	dwarf astilbe, Astilbe chinensis pumila
+n12795555	spirea, spiraea, Astilbe japonica
+n12796022	bergenia
+n12796385	coast boykinia, Boykinia elata, Boykinia occidentalis
+n12796849	golden saxifrage, golden spleen
+n12797368	umbrella plant, Indian rhubarb, Darmera peltata, Peltiphyllum peltatum
+n12797860	bridal wreath, bridal-wreath, Francoa ramosa
+n12798284	alumroot, alumbloom
+n12798910	coralbells, Heuchera sanguinea
+n12799269	leatherleaf saxifrage, Leptarrhena pyrolifolia
+n12799776	woodland star, Lithophragma affine, Lithophragma affinis, Tellima affinis
+n12800049	prairie star, Lithophragma parviflorum
+n12800586	miterwort, mitrewort, bishop's cap
+n12801072	five-point bishop's cap, Mitella pentandra
+n12801520	parnassia, grass-of-Parnassus
+n12801781	bog star, Parnassia palustris
+n12801966	fringed grass of Parnassus, Parnassia fimbriata
+n12803226	false alumroot, fringe cups, Tellima grandiflora
+n12803754	foamflower, coolwart, false miterwort, false mitrewort, Tiarella cordifolia
+n12803958	false miterwort, false mitrewort, Tiarella unifoliata
+n12804352	pickaback plant, piggyback plant, youth-on-age, Tolmiea menziesii
+n12805146	currant, currant bush
+n12805561	black currant, European black currant, Ribes nigrum
+n12805762	white currant, Ribes sativum
+n12806015	gooseberry, gooseberry bush, Ribes uva-crispa, Ribes grossularia
+n12806732	plane tree, sycamore, platan
+n12807251	London plane, Platanus acerifolia
+n12807409	American sycamore, American plane, buttonwood, Platanus occidentalis
+n12807624	oriental plane, Platanus orientalis
+n12807773	California sycamore, Platanus racemosa
+n12808007	Arizona sycamore, Platanus wrightii
+n12809868	Greek valerian, Polemonium reptans
+n12810007	northern Jacob's ladder, Polemonium boreale
+n12810151	skunkweed, skunk-weed, Polemonium viscosum
+n12810595	phlox
+n12811027	moss pink, mountain phlox, moss phlox, dwarf phlox, Phlox subulata
+n12811713	evening-snow, Linanthus dichotomus
+n12812235	acanthus
+n12812478	bear's breech, bear's breeches, sea holly, Acanthus mollis
+n12812801	caricature plant, Graptophyllum pictum
+n12813189	black-eyed Susan, black-eyed Susan vine, Thunbergia alata
+n12814643	catalpa, Indian bean
+n12814857	Catalpa bignioides
+n12814960	Catalpa speciosa
+n12815198	desert willow, Chilopsis linearis
+n12815668	calabash, calabash tree, Crescentia cujete
+n12815838	calabash
+n12816508	borage, tailwort, Borago officinalis
+n12816942	common amsinckia, Amsinckia intermedia
+n12817464	anchusa
+n12817694	bugloss, alkanet, Anchusa officinalis
+n12817855	cape forget-me-not, Anchusa capensis
+n12818004	cape forget-me-not, Anchusa riparia
+n12818346	Spanish elm, Equador laurel, salmwood, cypre, princewood, Cordia alliodora
+n12818601	princewood, Spanish elm, Cordia gerascanthus
+n12818966	Chinese forget-me-not, Cynoglossum amabile
+n12819141	hound's-tongue, Cynoglossum officinale
+n12819354	hound's-tongue, Cynoglossum virginaticum
+n12819728	blueweed, blue devil, blue thistle, viper's bugloss, Echium vulgare
+n12820113	beggar's lice, beggar lice
+n12820669	gromwell, Lithospermum officinale
+n12820853	puccoon, Lithospermum caroliniense
+n12821505	Virginia bluebell, Virginia cowslip, Mertensia virginica
+n12821895	garden forget-me-not, Myosotis sylvatica
+n12822115	forget-me-not, mouse ear, Myosotis scorpiodes
+n12822466	false gromwell
+n12822769	comfrey, cumfrey
+n12822955	common comfrey, boneset, Symphytum officinale
+n12823717	convolvulus
+n12823859	bindweed
+n12824053	field bindweed, wild morning-glory, Convolvulus arvensis
+n12824289	scammony, Convolvulus scammonia
+n12824735	silverweed
+n12825497	dodder
+n12826143	dichondra, Dichondra micrantha
+n12827270	cypress vine, star-glory, Indian pink, Ipomoea quamoclit, Quamoclit pennata
+n12827537	moonflower, belle de nuit, Ipomoea alba
+n12827907	wild potato vine, wild sweet potato vine, man-of-the-earth, manroot, scammonyroot, Ipomoea panurata, Ipomoea fastigiata
+n12828220	red morning-glory, star ipomoea, Ipomoea coccinea
+n12828379	man-of-the-earth, Ipomoea leptophylla
+n12828520	scammony, Ipomoea orizabensis
+n12828791	Japanese morning glory, Ipomoea nil
+n12828977	imperial Japanese morning glory, Ipomoea imperialis
+n12829582	gesneriad
+n12829975	gesneria
+n12830222	achimenes, hot water plant
+n12830568	aeschynanthus
+n12831141	lace-flower vine, Alsobia dianthiflora, Episcia dianthiflora
+n12831535	columnea
+n12831932	episcia
+n12832315	gloxinia
+n12832538	Canterbury bell, Gloxinia perennis
+n12832822	kohleria
+n12833149	African violet, Saintpaulia ionantha
+n12833985	streptocarpus
+n12834190	Cape primrose
+n12834798	waterleaf
+n12834938	Virginia waterleaf, Shawnee salad, shawny, Indian salad, John's cabbage, Hydrophyllum virginianum
+n12835331	yellow bells, California yellow bells, whispering bells, Emmanthe penduliflora
+n12835766	yerba santa, Eriodictyon californicum
+n12836212	nemophila
+n12836337	baby blue-eyes, Nemophila menziesii
+n12836508	five-spot, Nemophila maculata
+n12836862	scorpionweed, scorpion weed, phacelia
+n12837052	California bluebell, Phacelia campanularia
+n12837259	California bluebell, whitlavia, Phacelia minor, Phacelia whitlavia
+n12837466	fiddleneck, Phacelia tanacetifolia
+n12837803	fiesta flower, Pholistoma auritum, Nemophila aurita
+n12839574	basil thyme, basil balm, mother of thyme, Acinos arvensis, Satureja acinos
+n12839979	giant hyssop
+n12840168	yellow giant hyssop, Agastache nepetoides
+n12840362	anise hyssop, Agastache foeniculum
+n12840502	Mexican hyssop, Agastache mexicana
+n12840749	bugle, bugleweed
+n12841007	creeping bugle, Ajuga reptans
+n12841193	erect bugle, blue bugle, Ajuga genevensis
+n12841354	pyramid bugle, Ajuga pyramidalis
+n12842302	wood mint
+n12842519	hairy wood mint, Blephilia hirsuta
+n12842642	downy wood mint, Blephilia celiata
+n12842887	calamint
+n12843144	common calamint, Calamintha sylvatica, Satureja calamintha officinalis
+n12843316	large-flowered calamint, Calamintha grandiflora, Clinopodium grandiflorum, Satureja grandiflora
+n12843557	lesser calamint, field balm, Calamintha nepeta, Calamintha nepeta glantulosa, Satureja nepeta, Satureja calamintha glandulosa
+n12843970	wild basil, cushion calamint, Clinopodium vulgare, Satureja vulgaris
+n12844409	horse balm, horseweed, stoneroot, stone-root, richweed, stone root, Collinsonia canadensis
+n12844939	coleus, flame nettle
+n12845187	country borage, Coleus aromaticus, Coleus amboinicus, Plectranthus amboinicus
+n12845413	painted nettle, Joseph's coat, Coleus blumei, Solenostemon blumei, Solenostemon scutellarioides
+n12845908	Apalachicola rosemary, Conradina glabra
+n12846335	dragonhead, dragon's head, Dracocephalum parviflorum
+n12846690	elsholtzia
+n12847008	hemp nettle, dead nettle, Galeopsis tetrahit
+n12847374	ground ivy, alehoof, field balm, gill-over-the-ground, runaway robin, Glechoma hederaceae, Nepeta hederaceae
+n12847927	pennyroyal, American pennyroyal, Hedeoma pulegioides
+n12848499	hyssop, Hyssopus officinalis
+n12849061	dead nettle
+n12849279	white dead nettle, Lamium album
+n12849416	henbit, Lamium amplexicaule
+n12849952	English lavender, Lavandula angustifolia, Lavandula officinalis
+n12850168	French lavender, Lavandula stoechas
+n12850336	spike lavender, French lavender, Lavandula latifolia
+n12850906	dagga, Cape dagga, red dagga, wilde dagga, Leonotis leonurus
+n12851094	lion's-ear, Leonotis nepetaefolia, Leonotis nepetifolia
+n12851469	motherwort, Leonurus cardiaca
+n12851860	pitcher sage, Lepechinia calycina, Sphacele calycina
+n12852234	bugleweed, Lycopus virginicus
+n12852428	water horehound, Lycopus americanus
+n12852570	gipsywort, gypsywort, Lycopus europaeus
+n12853080	origanum
+n12853287	oregano, marjoram, pot marjoram, wild marjoram, winter sweet, Origanum vulgare
+n12853482	sweet marjoram, knotted marjoram, Origanum majorana, Majorana hortensis
+n12854048	horehound
+n12854193	common horehound, white horehound, Marrubium vulgare
+n12854600	lemon balm, garden balm, sweet balm, bee balm, beebalm, Melissa officinalis
+n12855365	corn mint, field mint, Mentha arvensis
+n12855494	water-mint, water mint, Mentha aquatica
+n12855710	bergamot mint, lemon mint, eau de cologne mint, Mentha citrata
+n12855886	horsemint, Mentha longifolia
+n12856091	peppermint, Mentha piperita
+n12856287	spearmint, Mentha spicata
+n12856479	apple mint, applemint, Mentha rotundifolia, Mentha suaveolens
+n12856680	pennyroyal, Mentha pulegium
+n12857204	yerba buena, Micromeria chamissonis, Micromeria douglasii, Satureja douglasii
+n12857779	molucca balm, bells of Ireland, Molucella laevis
+n12858150	monarda, wild bergamot
+n12858397	bee balm, beebalm, bergamot mint, oswego tea, Monarda didyma
+n12858618	horsemint, Monarda punctata
+n12858871	bee balm, beebalm, Monarda fistulosa
+n12858987	lemon mint, horsemint, Monarda citriodora
+n12859153	plains lemon monarda, Monarda pectinata
+n12859272	basil balm, Monarda clinopodia
+n12859679	mustang mint, Monardella lanceolata
+n12859986	catmint, catnip, Nepeta cataria
+n12860365	basil
+n12860978	beefsteak plant, Perilla frutescens crispa
+n12861345	phlomis
+n12861541	Jerusalem sage, Phlomis fruticosa
+n12861892	physostegia
+n12862512	plectranthus
+n12862828	patchouli, patchouly, pachouli, Pogostemon cablin
+n12863234	self-heal, heal all, Prunella vulgaris
+n12863624	mountain mint
+n12864160	rosemary, Rosmarinus officinalis
+n12865037	clary sage, Salvia clarea
+n12865562	purple sage, chaparral sage, Salvia leucophylla
+n12865708	cancerweed, cancer weed, Salvia lyrata
+n12865824	common sage, ramona, Salvia officinalis
+n12866002	meadow clary, Salvia pratensis
+n12866162	clary, Salvia sclarea
+n12866333	pitcher sage, Salvia spathacea
+n12866459	Mexican mint, Salvia divinorum
+n12866635	wild sage, wild clary, vervain sage, Salvia verbenaca
+n12866968	savory
+n12867184	summer savory, Satureja hortensis, Satureia hortensis
+n12867449	winter savory, Satureja montana, Satureia montana
+n12867826	skullcap, helmetflower
+n12868019	blue pimpernel, blue skullcap, mad-dog skullcap, mad-dog weed, Scutellaria lateriflora
+n12868880	hedge nettle, dead nettle, Stachys sylvatica
+n12869061	hedge nettle, Stachys palustris
+n12869478	germander
+n12869668	American germander, wood sage, Teucrium canadense
+n12870048	cat thyme, marum, Teucrium marum
+n12870225	wood sage, Teucrium scorodonia
+n12870535	thyme
+n12870682	common thyme, Thymus vulgaris
+n12870891	wild thyme, creeping thyme, Thymus serpyllum
+n12871272	blue curls
+n12871696	turpentine camphor weed, camphorweed, vinegarweed, Trichostema lanceolatum
+n12871859	bastard pennyroyal, Trichostema dichotomum
+n12872458	bladderwort
+n12872914	butterwort
+n12873341	genlisea
+n12873984	martynia, Martynia annua
+n12875269	common unicorn plant, devil's claw, common devil's claw, elephant-tusk, proboscis flower, ram's horn, Proboscidea louisianica
+n12875697	sand devil's claw, Proboscidea arenaria, Martynia arenaria
+n12875861	sweet unicorn plant, Proboscidea fragrans, Martynia fragrans
+n12876899	figwort
+n12877244	snapdragon
+n12877493	white snapdragon, Antirrhinum coulterianum
+n12877637	yellow twining snapdragon, Antirrhinum filipes
+n12877838	Mediterranean snapdragon, Antirrhinum majus
+n12878169	kitten-tails
+n12878325	Alpine besseya, Besseya alpina
+n12878784	false foxglove, Aureolaria pedicularia, Gerardia pedicularia
+n12879068	false foxglove, Aureolaria virginica, Gerardia virginica
+n12879527	calceolaria, slipperwort
+n12879963	Indian paintbrush, painted cup
+n12880244	desert paintbrush, Castilleja chromosa
+n12880462	giant red paintbrush, Castilleja miniata
+n12880638	great plains paintbrush, Castilleja sessiliflora
+n12880799	sulfur paintbrush, Castilleja sulphurea
+n12881105	shellflower, shell-flower, turtlehead, snakehead, snake-head, Chelone glabra
+n12881913	maiden blue-eyed Mary, Collinsia parviflora
+n12882158	blue-eyed Mary, Collinsia verna
+n12882779	foxglove, digitalis
+n12882945	common foxglove, fairy bell, fingerflower, finger-flower, fingerroot, finger-root, Digitalis purpurea
+n12883265	yellow foxglove, straw foxglove, Digitalis lutea
+n12883628	gerardia
+n12884100	blue toadflax, old-field toadflax, Linaria canadensis
+n12884260	toadflax, butter-and-eggs, wild snapdragon, devil's flax, Linaria vulgaris
+n12885045	golden-beard penstemon, Penstemon barbatus
+n12885265	scarlet bugler, Penstemon centranthifolius
+n12885510	red shrubby penstemon, redwood penstemon
+n12885754	Platte River penstemon, Penstemon cyananthus
+n12886185	hot-rock penstemon, Penstemon deustus
+n12886402	Jones' penstemon, Penstemon dolius
+n12886600	shrubby penstemon, lowbush penstemon, Penstemon fruticosus
+n12886831	narrow-leaf penstemon, Penstemon linarioides
+n12887293	balloon flower, scented penstemon, Penstemon palmeri
+n12887532	Parry's penstemon, Penstemon parryi
+n12887713	rock penstemon, cliff penstemon, Penstemon rupicola
+n12888016	Rydberg's penstemon, Penstemon rydbergii
+n12888234	cascade penstemon, Penstemon serrulatus
+n12888457	Whipple's penstemon, Penstemon whippleanus
+n12889219	moth mullein, Verbascum blattaria
+n12889412	white mullein, Verbascum lychnitis
+n12889579	purple mullein, Verbascum phoeniceum
+n12889713	common mullein, great mullein, Aaron's rod, flannel mullein, woolly mullein, torch, Verbascum thapsus
+n12890265	veronica, speedwell
+n12890490	field speedwell, Veronica agrestis
+n12890685	brooklime, American brooklime, Veronica americana
+n12890928	corn speedwell, Veronica arvensis
+n12891093	brooklime, European brooklime, Veronica beccabunga
+n12891305	germander speedwell, bird's eye, Veronica chamaedrys
+n12891469	water speedwell, Veronica michauxii, Veronica anagallis-aquatica
+n12891643	common speedwell, gypsyweed, Veronica officinalis
+n12891824	purslane speedwell, Veronica peregrina
+n12892013	thyme-leaved speedwell, Veronica serpyllifolia
+n12893463	nightshade
+n12893993	horse nettle, ball nettle, bull nettle, ball nightshade, Solanum carolinense
+n12895298	African holly, Solanum giganteum
+n12895811	potato vine, Solanum jasmoides
+n12896615	garden huckleberry, wonderberry, sunberry, Solanum nigrum guineese, Solanum melanocerasum, Solanum burbankii
+n12897118	naranjilla, Solanum quitoense
+n12897788	potato vine, giant potato creeper, Solanum wendlandii
+n12897999	potato tree, Brazilian potato tree, Solanum wrightii, Solanum macranthum
+n12898342	belladonna, belladonna plant, deadly nightshade, Atropa belladonna
+n12898774	bush violet, browallia
+n12899166	lady-of-the-night, Brunfelsia americana
+n12899537	angel's trumpet, maikoa, Brugmansia arborea, Datura arborea
+n12899752	angel's trumpet, Brugmansia suaveolens, Datura suaveolens
+n12899971	red angel's trumpet, Brugmansia sanguinea, Datura sanguinea
+n12900783	cone pepper, Capsicum annuum conoides
+n12901724	bird pepper, Capsicum frutescens baccatum, Capsicum baccatum
+n12902466	day jessamine, Cestrum diurnum
+n12902662	night jasmine, night jessamine, Cestrum nocturnum
+n12903014	tree tomato, tamarillo
+n12903367	thorn apple
+n12903503	jimsonweed, jimson weed, Jamestown weed, common thorn apple, apple of Peru, Datura stramonium
+n12903964	pichi, Fabiana imbricata
+n12904314	henbane, black henbane, stinking nightshade, Hyoscyamus niger
+n12904562	Egyptian henbane, Hyoscyamus muticus
+n12904938	matrimony vine, boxthorn
+n12905135	common matrimony vine, Duke of Argyll's tea tree, Lycium barbarum, Lycium halimifolium
+n12905412	Christmasberry, Christmas berry, Lycium carolinianum
+n12906214	plum tomato
+n12906498	mandrake, devil's apples, Mandragora officinarum
+n12906771	mandrake root, mandrake
+n12907057	apple of Peru, shoo fly, Nicandra physaloides
+n12907671	flowering tobacco, Jasmine tobacco, Nicotiana alata
+n12907857	common tobacco, Nicotiana tabacum
+n12908093	wild tobacco, Indian tobacco, Nicotiana rustica
+n12908645	cupflower, nierembergia
+n12908854	whitecup, Nierembergia repens, Nierembergia rivularis
+n12909421	petunia
+n12909614	large white petunia, Petunia axillaris
+n12909759	violet-flowered petunia, Petunia integrifolia
+n12909917	hybrid petunia, Petunia hybrida
+n12911079	cape gooseberry, purple ground cherry, Physalis peruviana
+n12911264	strawberry tomato, dwarf cape gooseberry, Physalis pruinosa
+n12911440	tomatillo, jamberry, Mexican husk tomato, Physalis ixocarpa
+n12911673	tomatillo, miltomate, purple ground cherry, jamberry, Physalis philadelphica
+n12911914	yellow henbane, Physalis viscosa
+n12912274	cock's eggs, Salpichroa organifolia, Salpichroa rhomboidea
+n12912670	salpiglossis
+n12912801	painted tongue, Salpiglossis sinuata
+n12913144	butterfly flower, poor man's orchid, schizanthus
+n12913524	Scopolia carniolica
+n12913791	chalice vine, trumpet flower, cupflower, Solandra guttata
+n12914923	verbena, vervain
+n12915140	lantana
+n12915568	black mangrove, Avicennia marina
+n12915811	white mangrove, Avicennia officinalis
+n12916179	black mangrove, Aegiceras majus
+n12916511	teak, Tectona grandis
+n12917901	spurge
+n12918609	sun spurge, wartweed, wartwort, devil's milk, Euphorbia helioscopia
+n12918810	petty spurge, devil's milk, Euphorbia peplus
+n12918991	medusa's head, Euphorbia medusae, Euphorbia caput-medusae
+n12919195	wild spurge, flowering spurge, tramp's spurge, Euphorbia corollata
+n12919403	snow-on-the-mountain, snow-in-summer, ghost weed, Euphorbia marginata
+n12919646	cypress spurge, Euphorbia cyparissias
+n12919847	leafy spurge, wolf's milk, Euphorbia esula
+n12920043	hairy spurge, Euphorbia hirsuta
+n12920204	poinsettia, Christmas star, Christmas flower, lobster plant, Mexican flameleaf, painted leaf, Euphorbia pulcherrima
+n12920521	Japanese poinsettia, mole plant, paint leaf, Euphorbia heterophylla
+n12920719	fire-on-the-mountain, painted leaf, Mexican fire plant, Euphorbia cyathophora
+n12920955	wood spurge, Euphorbia amygdaloides
+n12921315	dwarf spurge, Euphorbia exigua
+n12921499	scarlet plume, Euphorbia fulgens
+n12921660	naboom, cactus euphorbia, Euphorbia ingens
+n12921868	crown of thorns, Christ thorn, Christ plant, Euphorbia milii
+n12922119	toothed spurge, Euphorbia dentata
+n12922458	three-seeded mercury, Acalypha virginica
+n12922763	croton, Croton tiglium
+n12923108	cascarilla, Croton eluteria
+n12923257	cascarilla bark, eleuthera bark, sweetwood bark
+n12924623	castor-oil plant, castor bean plant, palma christi, palma christ, Ricinus communis
+n12925179	spurge nettle, tread-softly, devil nettle, pica-pica, Cnidoscolus urens, Jatropha urens, Jatropha stimulosus
+n12925583	physic nut, Jatropha curcus
+n12926039	Para rubber tree, caoutchouc tree, Hevea brasiliensis
+n12926480	cassava, casava
+n12926689	bitter cassava, manioc, mandioc, mandioca, tapioca plant, gari, Manihot esculenta, Manihot utilissima
+n12927013	cassava, manioc
+n12927194	sweet cassava, Manihot dulcis
+n12927494	candlenut, varnish tree, Aleurites moluccana
+n12927758	tung tree, tung, tung-oil tree, Aleurites fordii
+n12928071	slipper spurge, slipper plant
+n12928307	candelilla, Pedilanthus bracteatus, Pedilanthus pavonis
+n12928491	Jewbush, Jew-bush, Jew bush, redbird cactus, redbird flower, Pedilanthus tithymaloides
+n12928819	jumping bean, jumping seed, Mexican jumping bean
+n12929403	camellia, camelia
+n12929600	japonica, Camellia japonica
+n12930778	umbellifer, umbelliferous plant
+n12930951	wild parsley
+n12931231	fool's parsley, lesser hemlock, Aethusa cynapium
+n12931542	dill, Anethum graveolens
+n12931906	angelica, angelique
+n12932173	garden angelica, archangel, Angelica Archangelica
+n12932365	wild angelica, Angelica sylvestris
+n12932706	chervil, beaked parsley, Anthriscus cereifolium
+n12932966	cow parsley, wild chervil, Anthriscus sylvestris
+n12933274	wild celery, Apium graveolens
+n12934036	astrantia, masterwort
+n12934174	greater masterwort, Astrantia major
+n12934479	caraway, Carum carvi
+n12934685	whorled caraway
+n12934985	water hemlock, Cicuta verosa
+n12935166	spotted cowbane, spotted hemlock, spotted water hemlock
+n12935609	hemlock, poison hemlock, poison parsley, California fern, Nebraska fern, winter fern, Conium maculatum
+n12936155	earthnut, Conopodium denudatum
+n12936826	cumin, Cuminum cyminum
+n12937130	wild carrot, Queen Anne's lace, Daucus carota
+n12938081	eryngo, eringo
+n12938193	sea holly, sea holm, sea eryngium, Eryngium maritimum
+n12938445	button snakeroot, Eryngium aquaticum
+n12938667	rattlesnake master, rattlesnake's master, button snakeroot, Eryngium yuccifolium
+n12939104	fennel
+n12939282	common fennel, Foeniculum vulgare
+n12939479	Florence fennel, Foeniculum dulce, Foeniculum vulgare dulce
+n12939874	cow parsnip, hogweed, Heracleum sphondylium
+n12940226	lovage, Levisticum officinale
+n12940609	sweet cicely, Myrrhis odorata
+n12941220	water fennel, Oenanthe aquatica
+n12941536	parsnip, Pastinaca sativa
+n12941717	cultivated parsnip
+n12942025	wild parsnip, madnep
+n12942395	parsley, Petroselinum crispum
+n12942572	Italian parsley, flat-leaf parsley, Petroselinum crispum neapolitanum
+n12942729	Hamburg parsley, turnip-rooted parsley, Petroselinum crispum tuberosum
+n12943049	anise, anise plant, Pimpinella anisum
+n12943443	sanicle, snakeroot
+n12943912	purple sanicle, Sanicula bipinnatifida
+n12944095	European sanicle, Sanicula Europaea
+n12945177	water parsnip, Sium suave
+n12945366	greater water parsnip, Sium latifolium
+n12945549	skirret, Sium sisarum
+n12946849	dogwood, dogwood tree, cornel
+n12947313	common white dogwood, eastern flowering dogwood, Cornus florida
+n12947544	red osier, red osier dogwood, red dogwood, American dogwood, redbrush, Cornus stolonifera
+n12947756	silky dogwood, Cornus obliqua
+n12947895	silky cornel, silky dogwood, Cornus amomum
+n12948053	common European dogwood, red dogwood, blood-twig, pedwood, Cornus sanguinea
+n12948251	bunchberry, dwarf cornel, crackerberry, pudding berry, Cornus canadensis
+n12948495	cornelian cherry, Cornus mas
+n12949160	puka, Griselinia lucida
+n12949361	kapuka, Griselinia littoralis
+n12950126	valerian
+n12950314	common valerian, garden heliotrope, Valeriana officinalis
+n12950796	common corn salad, lamb's lettuce, Valerianella olitoria, Valerianella locusta
+n12951146	red valerian, French honeysuckle, Centranthus ruber
+n12951835	filmy fern, film fern
+n12952165	bristle fern, filmy fern
+n12952469	hare's-foot bristle fern, Trichomanes boschianum
+n12952590	Killarney fern, Trichomanes speciosum
+n12952717	kidney fern, Trichomanes reniforme
+n12953206	flowering fern, osmund
+n12953484	royal fern, royal osmund, king fern, ditch fern, French bracken, Osmunda regalis
+n12953712	interrupted fern, Osmunda clatonia
+n12954353	crape fern, Prince-of-Wales fern, Prince-of-Wales feather, Prince-of-Wales plume, Leptopteris superba, Todea superba
+n12954799	crepe fern, king fern, Todea barbara
+n12955414	curly grass, curly grass fern, Schizaea pusilla
+n12955840	pine fern, Anemia adiantifolia
+n12956170	climbing fern
+n12956367	creeping fern, Hartford fern, Lygodium palmatum
+n12956588	climbing maidenhair, climbing maidenhair fern, snake fern, Lygodium microphyllum
+n12956922	scented fern, Mohria caffrorum
+n12957608	clover fern, pepperwort
+n12957803	nardoo, nardo, common nardoo, Marsilea drummondii
+n12957924	water clover, Marsilea quadrifolia
+n12958261	pillwort, Pilularia globulifera
+n12958615	regnellidium, Regnellidium diphyllum
+n12959074	floating-moss, Salvinia rotundifolia, Salvinia auriculata
+n12959538	mosquito fern, floating fern, Carolina pond fern, Azolla caroliniana
+n12960378	adder's tongue, adder's tongue fern
+n12960552	ribbon fern, Ophioglossum pendulum
+n12960863	grape fern
+n12961242	daisyleaf grape fern, daisy-leaved grape fern, Botrychium matricariifolium
+n12961393	leathery grape fern, Botrychium multifidum
+n12961536	rattlesnake fern, Botrychium virginianum
+n12961879	flowering fern, Helminthostachys zeylanica
+n12963628	powdery mildew
+n12964920	Dutch elm fungus, Ceratostomella ulmi
+n12965626	ergot, Claviceps purpurea
+n12965951	rye ergot
+n12966804	black root rot fungus, Xylaria mali
+n12966945	dead-man's-fingers, dead-men's-fingers, Xylaria polymorpha
+n12968136	sclerotinia
+n12968309	brown cup
+n12969131	earthball, false truffle, puffball, hard-skinned puffball
+n12969425	Scleroderma citrinum, Scleroderma aurantium
+n12969670	Scleroderma flavidium, star earthball
+n12969927	Scleroderma bovista, smooth earthball
+n12970193	Podaxaceae
+n12970293	stalked puffball
+n12970733	stalked puffball
+n12971400	false truffle
+n12971804	Rhizopogon idahoensis
+n12972136	Truncocolumella citrina
+n12973443	mucor
+n12973791	rhizopus
+n12973937	bread mold, Rhizopus nigricans
+n12974987	slime mold, slime mould
+n12975804	true slime mold, acellular slime mold, plasmodial slime mold, myxomycete
+n12976198	cellular slime mold
+n12976554	dictostylium
+n12978076	pond-scum parasite
+n12979316	potato wart fungus, Synchytrium endobioticum
+n12979829	white fungus, Saprolegnia ferax
+n12980080	water mold
+n12980840	downy mildew, false mildew
+n12981086	blue mold fungus, Peronospora tabacina
+n12981301	onion mildew, Peronospora destructor
+n12981443	tobacco mildew, Peronospora hyoscyami
+n12981954	white rust
+n12982468	pythium
+n12982590	damping off fungus, Pythium debaryanum
+n12982915	Phytophthora citrophthora
+n12983048	Phytophthora infestans
+n12983654	clubroot fungus, Plasmodiophora brassicae
+n12983873	Geglossaceae
+n12983961	Sarcosomataceae
+n12984267	Rufous rubber cup
+n12984489	devil's cigar
+n12984595	devil's urn
+n12985420	truffle, earthnut, earth-ball
+n12985773	club fungus
+n12985857	coral fungus
+n12986227	tooth fungus
+n12987056	lichen
+n12987423	ascolichen
+n12987535	basidiolichen
+n12988158	lecanora
+n12988341	manna lichen
+n12988572	archil, orchil
+n12989007	roccella, Roccella tinctoria
+n12989938	beard lichen, beard moss, Usnea barbata
+n12990597	horsehair lichen, horsetail lichen
+n12991184	reindeer moss, reindeer lichen, arctic moss, Cladonia rangiferina
+n12991837	crottle, crottal, crotal
+n12992177	Iceland moss, Iceland lichen, Cetraria islandica
+n12992868	fungus
+n12994892	promycelium
+n12995601	true fungus
+n12997654	basidiomycete, basidiomycetous fungi
+n12997919	mushroom
+n12998815	agaric
+n13000891	mushroom
+n13001041	mushroom
+n13001206	toadstool
+n13001366	horse mushroom, Agaricus arvensis
+n13001529	meadow mushroom, field mushroom, Agaricus campestris
+n13001930	shiitake, shiitake mushroom, Chinese black mushroom, golden oak mushroom, Oriental black mushroom, Lentinus edodes
+n13002209	scaly lentinus, Lentinus lepideus
+n13002750	royal agaric, Caesar's agaric, Amanita caesarea
+n13002925	false deathcap, Amanita mappa
+n13003061	fly agaric, Amanita muscaria
+n13003254	death cap, death cup, death angel, destroying angel, Amanita phalloides
+n13003522	blushing mushroom, blusher, Amanita rubescens
+n13003712	destroying angel, Amanita verna
+n13004423	chanterelle, chantarelle, Cantharellus cibarius
+n13004640	floccose chanterelle, Cantharellus floccosus
+n13004826	pig's ears, Cantharellus clavatus
+n13004992	cinnabar chanterelle, Cantharellus cinnabarinus
+n13005329	jack-o-lantern fungus, jack-o-lantern, jack-a-lantern, Omphalotus illudens
+n13005984	inky cap, inky-cap mushroom, Coprinus atramentarius
+n13006171	shaggymane, shaggy cap, shaggymane mushroom, Coprinus comatus
+n13006631	milkcap, Lactarius delicioso
+n13006894	fairy-ring mushroom, Marasmius oreades
+n13007034	fairy ring, fairy circle
+n13007417	oyster mushroom, oyster fungus, oyster agaric, Pleurotus ostreatus
+n13007629	olive-tree agaric, Pleurotus phosphoreus
+n13008157	Pholiota astragalina
+n13008315	Pholiota aurea, golden pholiota
+n13008485	Pholiota destruens
+n13008689	Pholiota flammans
+n13008839	Pholiota flavida
+n13009085	nameko, viscid mushroom, Pholiota nameko
+n13009244	Pholiota squarrosa-adiposa
+n13009429	Pholiota squarrosa, scaly pholiota
+n13009656	Pholiota squarrosoides
+n13010694	Stropharia ambigua
+n13010951	Stropharia hornemannii
+n13011221	Stropharia rugoso-annulata
+n13011595	gill fungus
+n13012253	Entoloma lividum, Entoloma sinuatum
+n13012469	Entoloma aprile
+n13012973	Chlorophyllum molybdites
+n13013534	lepiota
+n13013764	parasol mushroom, Lepiota procera
+n13013965	poisonous parasol, Lepiota morgani
+n13014097	Lepiota naucina
+n13014265	Lepiota rhacodes
+n13014409	American parasol, Lepiota americana
+n13014581	Lepiota rubrotincta
+n13014741	Lepiota clypeolaria
+n13014879	onion stem, Lepiota cepaestipes
+n13015509	pink disease fungus, Corticium salmonicolor
+n13015688	bottom rot fungus, Corticium solani
+n13016076	potato fungus, Pellicularia filamentosa, Rhizoctinia solani
+n13016289	coffee fungus, Pellicularia koleroga
+n13017102	blewits, Clitocybe nuda
+n13017240	sandy mushroom, Tricholoma populinum
+n13017439	Tricholoma pessundatum
+n13017610	Tricholoma sejunctum
+n13017789	man-on-a-horse, Tricholoma flavovirens
+n13017979	Tricholoma venenata
+n13018088	Tricholoma pardinum
+n13018232	Tricholoma vaccinum
+n13018407	Tricholoma aurantium
+n13018906	Volvaria bombycina
+n13019496	Pluteus aurantiorugosus
+n13019643	Pluteus magnus, sawdust mushroom
+n13019835	deer mushroom, Pluteus cervinus
+n13020191	straw mushroom, Chinese mushroom, Volvariella volvacea
+n13020481	Volvariella bombycina
+n13020964	Clitocybe clavipes
+n13021166	Clitocybe dealbata
+n13021332	Clitocybe inornata
+n13021543	Clitocybe robusta, Clytocybe alba
+n13021689	Clitocybe irina, Tricholoma irinum, Lepista irina
+n13021867	Clitocybe subconnexa
+n13022210	winter mushroom, Flammulina velutipes
+n13022709	mycelium
+n13022903	sclerotium
+n13023134	sac fungus
+n13024012	ascomycete, ascomycetous fungus
+n13024500	Clavicipitaceae, grainy club mushrooms
+n13024653	grainy club
+n13025647	yeast
+n13025854	baker's yeast, brewer's yeast, Saccharomyces cerevisiae
+n13026015	wine-maker's yeast, Saccharomyces ellipsoides
+n13027557	Aspergillus fumigatus
+n13027879	brown root rot fungus, Thielavia basicola
+n13028611	discomycete, cup fungus
+n13028937	Leotia lubrica
+n13029122	Mitrula elegans
+n13029326	Sarcoscypha coccinea, scarlet cup
+n13029610	Caloscypha fulgens
+n13029760	Aleuria aurantia, orange peel fungus
+n13030337	elf cup
+n13030616	Peziza domicilina
+n13030852	blood cup, fairy cup, Peziza coccinea
+n13031193	Urnula craterium, urn fungus
+n13031323	Galiella rufa
+n13031474	Jafnea semitosta
+n13032115	morel
+n13032381	common morel, Morchella esculenta, sponge mushroom, sponge morel
+n13032618	Disciotis venosa, cup morel
+n13032923	Verpa, bell morel
+n13033134	Verpa bohemica, early morel
+n13033396	Verpa conica, conic Verpa
+n13033577	black morel, Morchella conica, conic morel, Morchella angusticeps, narrowhead morel
+n13033879	Morchella crassipes, thick-footed morel
+n13034062	Morchella semilibera, half-free morel, cow's head
+n13034555	Wynnea americana
+n13034788	Wynnea sparassoides
+n13035241	false morel
+n13035389	lorchel
+n13035707	helvella
+n13035925	Helvella crispa, miter mushroom
+n13036116	Helvella acetabulum
+n13036312	Helvella sulcata
+n13036804	discina
+n13037406	gyromitra
+n13037585	Gyromitra californica, California false morel
+n13037805	Gyromitra sphaerospora, round-spored gyromitra
+n13038068	Gyromitra esculenta, brain mushroom, beefsteak morel
+n13038376	Gyromitra infula, saddled-shaped false morel
+n13038577	Gyromitra fastigiata, Gyromitra brunnea
+n13038744	Gyromitra gigas
+n13039349	gasteromycete, gastromycete
+n13040303	stinkhorn, carrion fungus
+n13040629	common stinkhorn, Phallus impudicus
+n13040796	Phallus ravenelii
+n13041312	dog stinkhorn, Mutinus caninus
+n13041943	Calostoma lutescens
+n13042134	Calostoma cinnabarina
+n13042316	Calostoma ravenelii
+n13042982	stinky squid, Pseudocolus fusiformis
+n13043926	puffball, true puffball
+n13044375	giant puffball, Calvatia gigantea
+n13044778	earthstar
+n13045210	Geastrum coronatum
+n13045594	Radiigera fuscogleba
+n13045975	Astreus pteridis
+n13046130	Astreus hygrometricus
+n13046669	bird's-nest fungus
+n13047862	Gastrocybe lateritia
+n13048447	Macowanites americanus
+n13049953	polypore, pore fungus, pore mushroom
+n13050397	bracket fungus, shelf fungus
+n13050705	Albatrellus dispansus
+n13050940	Albatrellus ovinus, sheep polypore
+n13051346	Neolentinus ponderosus
+n13052014	Oligoporus leucospongia
+n13052248	Polyporus tenuiculus
+n13052670	hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa
+n13052931	Polyporus squamosus, scaly polypore
+n13053608	beefsteak fungus, Fistulina hepatica
+n13054073	agaric, Fomes igniarius
+n13054560	bolete
+n13055423	Boletus chrysenteron
+n13055577	Boletus edulis
+n13055792	Frost's bolete, Boletus frostii
+n13055949	Boletus luridus
+n13056135	Boletus mirabilis
+n13056349	Boletus pallidus
+n13056607	Boletus pulcherrimus
+n13056799	Boletus pulverulentus
+n13057054	Boletus roxanae
+n13057242	Boletus subvelutipes
+n13057422	Boletus variipes
+n13057639	Boletus zelleri
+n13058037	Fuscoboletinus paluster
+n13058272	Fuscoboletinus serotinus
+n13058608	Leccinum fibrillosum
+n13059298	Suillus albivelatus
+n13059657	old-man-of-the-woods, Strobilomyces floccopus
+n13060017	Boletellus russellii
+n13060190	jelly fungus
+n13061172	snow mushroom, Tremella fuciformis
+n13061348	witches' butter, Tremella lutescens
+n13061471	Tremella foliacea
+n13061704	Tremella reticulata
+n13062421	Jew's-ear, Jew's-ears, ear fungus, Auricularia auricula
+n13063269	rust, rust fungus
+n13063514	aecium
+n13064111	flax rust, flax rust fungus, Melampsora lini
+n13064457	blister rust, Cronartium ribicola
+n13065089	wheat rust, Puccinia graminis
+n13065514	apple rust, cedar-apple rust, Gymnosporangium juniperi-virginianae
+n13066129	smut, smut fungus
+n13066448	covered smut
+n13066979	loose smut
+n13067191	cornsmut, corn smut
+n13067330	boil smut, Ustilago maydis
+n13067532	Sphacelotheca, genus Sphacelotheca
+n13067672	head smut, Sphacelotheca reiliana
+n13068255	bunt, Tilletia caries
+n13068434	bunt, stinking smut, Tilletia foetida
+n13068735	onion smut, Urocystis cepulae
+n13068917	flag smut fungus
+n13069224	wheat flag smut, Urocystis tritici
+n13069773	felt fungus, Septobasidium pseudopedicellatum
+n13070308	waxycap
+n13070875	Hygrocybe acutoconica, conic waxycap
+n13071371	Hygrophorus borealis
+n13071553	Hygrophorus caeruleus
+n13071815	Hygrophorus inocybiformis
+n13072031	Hygrophorus kauffmanii
+n13072209	Hygrophorus marzuolus
+n13072350	Hygrophorus purpurascens
+n13072528	Hygrophorus russula
+n13072706	Hygrophorus sordidus
+n13072863	Hygrophorus tennesseensis
+n13073055	Hygrophorus turundus
+n13073703	Neohygrophorus angelesianus
+n13074619	Cortinarius armillatus
+n13074814	Cortinarius atkinsonianus
+n13075020	Cortinarius corrugatus
+n13075272	Cortinarius gentilis
+n13075441	Cortinarius mutabilis, purple-staining Cortinarius
+n13075684	Cortinarius semisanguineus
+n13075847	Cortinarius subfoetidus
+n13076041	Cortinarius violaceus
+n13076405	Gymnopilus spectabilis
+n13076643	Gymnopilus validipes
+n13076831	Gymnopilus ventricosus
+n13077033	mold, mould
+n13077295	mildew
+n13078021	verticillium
+n13079073	monilia
+n13079419	candida
+n13079567	Candida albicans, Monilia albicans
+n13080306	blastomycete
+n13080866	yellow spot fungus, Cercospora kopkei
+n13081229	green smut fungus, Ustilaginoidea virens
+n13081999	dry rot
+n13082568	rhizoctinia
+n13083023	houseplant
+n13083461	bedder, bedding plant
+n13084184	succulent
+n13084834	cultivar
+n13085113	weed
+n13085747	wort
+n13090018	brier
+n13090871	aril
+n13091620	sporophyll, sporophyl
+n13091774	sporangium, spore case, spore sac
+n13091982	sporangiophore
+n13092078	ascus
+n13092240	ascospore
+n13092385	arthrospore
+n13092987	eusporangium
+n13093275	tetrasporangium
+n13093629	gametangium
+n13094145	sorus
+n13094273	sorus
+n13095013	partial veil
+n13096779	lignum
+n13098515	vascular ray, medullary ray
+n13098962	phloem, bast
+n13099833	evergreen, evergreen plant
+n13099999	deciduous plant
+n13100156	poisonous plant
+n13100677	vine
+n13102648	creeper
+n13102775	tendril
+n13103023	root climber
+n13103660	lignosae
+n13103750	arborescent plant
+n13103877	snag
+n13104059	tree
+n13107694	timber tree
+n13107807	treelet
+n13107891	arbor
+n13108131	bean tree
+n13108323	pollard
+n13108481	sapling
+n13108545	shade tree
+n13108662	gymnospermous tree
+n13108841	conifer, coniferous tree
+n13109733	angiospermous tree, flowering tree
+n13110915	nut tree
+n13111174	spice tree
+n13111340	fever tree
+n13111504	stump, tree stump
+n13111881	bonsai
+n13112035	ming tree
+n13112201	ming tree
+n13118330	undershrub
+n13118707	subshrub, suffrutex
+n13119870	bramble
+n13120211	liana
+n13120958	geophyte
+n13121104	desert plant, xerophyte, xerophytic plant, xerophile, xerophilous plant
+n13121349	mesophyte, mesophytic plant
+n13122364	marsh plant, bog plant, swamp plant
+n13123309	hemiepiphyte, semiepiphyte
+n13123431	strangler, strangler tree
+n13123841	lithophyte, lithophytic plant
+n13124358	saprobe
+n13124654	autophyte, autophytic plant, autotroph, autotrophic organism
+n13125117	root
+n13126050	taproot
+n13126856	prop root
+n13127001	prophyll
+n13127303	rootstock
+n13127666	quickset
+n13127843	stolon, runner, offset
+n13128278	tuberous plant
+n13128582	rhizome, rootstock, rootstalk
+n13128976	rachis
+n13129078	caudex
+n13130014	cladode, cladophyll, phylloclad, phylloclade
+n13130161	receptacle
+n13130726	scape, flower stalk
+n13131028	umbel
+n13131618	petiole, leafstalk
+n13132034	peduncle
+n13132156	pedicel, pedicle
+n13132338	flower cluster
+n13132486	raceme
+n13132656	panicle
+n13132756	thyrse, thyrsus
+n13132940	cyme
+n13133140	cymule
+n13133233	glomerule
+n13133316	scorpioid cyme
+n13133613	ear, spike, capitulum
+n13133932	spadix
+n13134302	bulbous plant
+n13134531	bulbil, bulblet
+n13134844	cormous plant
+n13134947	fruit
+n13135692	fruitlet
+n13135832	seed
+n13136316	bean
+n13136556	nut
+n13136781	nutlet
+n13137010	kernel, meat
+n13137225	syconium
+n13137409	berry
+n13137672	aggregate fruit, multiple fruit, syncarp
+n13137951	simple fruit, bacca
+n13138155	acinus
+n13138308	drupe, stone fruit
+n13138658	drupelet
+n13138842	pome, false fruit
+n13139055	pod, seedpod
+n13139321	loment
+n13139482	pyxidium, pyxis
+n13139647	husk
+n13139837	cornhusk
+n13140049	pod, cod, seedcase
+n13140367	accessory fruit, pseudocarp
+n13141141	buckthorn
+n13141415	buckthorn berry, yellow berry
+n13141564	cascara buckthorn, bearberry, bearwood, chittamwood, chittimwood, Rhamnus purshianus
+n13141797	cascara, cascara sagrada, chittam bark, chittem bark
+n13141972	Carolina buckthorn, indian cherry, Rhamnus carolinianus
+n13142182	coffeeberry, California buckthorn, California coffee, Rhamnus californicus
+n13142504	redberry, red-berry, Rhamnus croceus
+n13142907	nakedwood
+n13143285	jujube, jujube bush, Christ's-thorn, Jerusalem thorn, Ziziphus jujuba
+n13143758	Christ's-thorn, Jerusalem thorn, Paliurus spina-christi
+n13144084	hazel, hazel tree, Pomaderris apetala
+n13145040	fox grape, Vitis labrusca
+n13145250	muscadine, Vitis rotundifolia
+n13145444	vinifera, vinifera grape, common grape vine, Vitis vinifera
+n13146403	Pinot blanc
+n13146583	Sauvignon grape
+n13146928	Sauvignon blanc
+n13147153	Muscadet
+n13147270	Riesling
+n13147386	Zinfandel
+n13147532	Chenin blanc
+n13147689	malvasia
+n13147918	Verdicchio
+n13148208	Boston ivy, Japanese ivy, Parthenocissus tricuspidata
+n13148384	Virginia creeper, American ivy, woodbine, Parthenocissus quinquefolia
+n13149296	true pepper, pepper vine
+n13149970	betel, betel pepper, Piper betel
+n13150378	cubeb
+n13150592	schizocarp
+n13150894	peperomia
+n13151082	watermelon begonia, Peperomia argyreia, Peperomia sandersii
+n13152339	yerba mansa, Anemopsis californica
+n13154388	pinna, pinnule
+n13154494	frond
+n13154841	bract
+n13155095	bracteole, bractlet
+n13155305	involucre
+n13155611	glume
+n13156986	palmate leaf
+n13157137	pinnate leaf
+n13157346	bijugate leaf, bijugous leaf, twice-pinnate
+n13157481	decompound leaf
+n13157684	acuminate leaf
+n13157971	deltoid leaf
+n13158167	ensiform leaf
+n13158512	linear leaf, elongate leaf
+n13158605	lyrate leaf
+n13158714	obtuse leaf
+n13158815	oblanceolate leaf
+n13159357	pandurate leaf, panduriform leaf
+n13159691	reniform leaf
+n13159890	spatulate leaf
+n13160116	even-pinnate leaf, abruptly-pinnate leaf
+n13160254	odd-pinnate leaf
+n13160365	pedate leaf
+n13160604	crenate leaf
+n13160831	dentate leaf
+n13160938	denticulate leaf
+n13161151	erose leaf
+n13161254	runcinate leaf
+n13161904	prickly-edged leaf
+n13163553	deadwood
+n13163649	haulm, halm
+n13163991	branchlet, twig, sprig
+n13164501	osier
+n13170840	giant scrambling fern, Diplopterygium longissimum
+n13171210	umbrella fern, fan fern, Sticherus flabellatus, Gleichenia flabellata
+n13171797	floating fern, water sprite, Ceratopteris pteridioides
+n13172923	polypody
+n13173132	licorice fern, Polypodium glycyrrhiza
+n13173259	grey polypody, gray polypody, resurrection fern, Polypodium polypodioides
+n13173488	leatherleaf, leathery polypody, coast polypody, Polypodium scouleri
+n13173697	rock polypody, rock brake, American wall fern, Polypodium virgianum
+n13173882	common polypody, adder's fern, wall fern, golden maidenhair, golden polypody, sweet fern, Polypodium vulgare
+n13174354	bear's-paw fern, Aglaomorpha meyeniana
+n13174670	strap fern
+n13174823	Florida strap fern, cow-tongue fern, hart's-tongue fern
+n13175682	basket fern, Drynaria rigidula
+n13176363	snake polypody, Microgramma-piloselloides
+n13176714	climbing bird's nest fern, Microsorium punctatum
+n13177048	golden polypody, serpent fern, rabbit's-foot fern, Phlebodium aureum, Polypodium aureum
+n13177529	staghorn fern
+n13177768	South American staghorn, Platycerium andinum
+n13177884	common staghorn fern, elkhorn fern, Platycerium bifurcatum, Platycerium alcicorne
+n13178284	felt fern, tongue fern, Pyrrosia lingua, Cyclophorus lingua
+n13178707	potato fern, Solanopteris bifrons
+n13179056	myrmecophyte
+n13179804	grass fern, ribbon fern, Vittaria lineata
+n13180534	spleenwort
+n13180875	black spleenwort, Asplenium adiantum-nigrum
+n13181055	bird's nest fern, Asplenium nidus
+n13181244	ebony spleenwort, Scott's Spleenwort, Asplenium platyneuron
+n13181406	black-stem spleenwort, black-stemmed spleenwort, little ebony spleenwort
+n13181811	walking fern, walking leaf, Asplenium rhizophyllum, Camptosorus rhizophyllus
+n13182164	green spleenwort, Asplenium viride
+n13182338	mountain spleenwort, Asplenium montanum
+n13182799	lobed spleenwort, Asplenium pinnatifidum
+n13182937	lanceolate spleenwort, Asplenium billotii
+n13183056	hart's-tongue, hart's-tongue fern, Asplenium scolopendrium, Phyllitis scolopendrium
+n13183489	scale fern, scaly fern, Asplenium ceterach, Ceterach officinarum
+n13184394	scolopendrium
+n13185269	deer fern, Blechnum spicant
+n13185658	doodia, rasp fern
+n13186388	chain fern
+n13186546	Virginia chain fern, Woodwardia virginica
+n13187367	silver tree fern, sago fern, black tree fern, Cyathea medullaris
+n13188096	davallia
+n13188268	hare's-foot fern
+n13188462	Canary Island hare's foot fern, Davallia canariensis
+n13188767	squirrel's-foot fern, ball fern, Davalia bullata, Davalia bullata mariesii, Davallia Mariesii
+n13190060	bracken, Pteridium esculentum
+n13190747	soft tree fern, Dicksonia antarctica
+n13191148	Scythian lamb, Cibotium barometz
+n13191620	false bracken, Culcita dubia
+n13191884	thyrsopteris, Thyrsopteris elegans
+n13192625	shield fern, buckler fern
+n13193143	broad buckler-fern, Dryopteris dilatata
+n13193269	fragrant cliff fern, fragrant shield fern, fragrant wood fern, Dryopteris fragrans
+n13193466	Goldie's fern, Goldie's shield fern, goldie's wood fern, Dryopteris goldiana
+n13193642	wood fern, wood-fern, woodfern
+n13193856	male fern, Dryopteris filix-mas
+n13194036	marginal wood fern, evergreen wood fern, leatherleaf wood fern, Dryopteris marginalis
+n13194212	mountain male fern, Dryopteris oreades
+n13194572	lady fern, Athyrium filix-femina
+n13194758	Alpine lady fern, Athyrium distentifolium
+n13194918	silvery spleenwort, glade fern, narrow-leaved spleenwort, Athyrium pycnocarpon, Diplazium pycnocarpon
+n13195341	holly fern, Cyrtomium aculeatum, Polystichum aculeatum
+n13195761	bladder fern
+n13196003	brittle bladder fern, brittle fern, fragile fern, Cystopteris fragilis
+n13196234	mountain bladder fern, Cystopteris montana
+n13196369	bulblet fern, bulblet bladder fern, berry fern, Cystopteris bulbifera
+n13196738	silvery spleenwort, Deparia acrostichoides, Athyrium thelypteroides
+n13197274	oak fern, Gymnocarpium dryopteris, Thelypteris dryopteris
+n13197507	limestone fern, northern oak fern, Gymnocarpium robertianum
+n13198054	ostrich fern, shuttlecock fern, fiddlehead, Matteuccia struthiopteris, Pteretis struthiopteris, Onoclea struthiopteris
+n13198482	hart's-tongue, hart's-tongue fern, Olfersia cervina, Polybotrya cervina, Polybotria cervina
+n13198914	sensitive fern, bead fern, Onoclea sensibilis
+n13199717	Christmas fern, canker brake, dagger fern, evergreen wood fern, Polystichum acrostichoides
+n13199970	holly fern
+n13200193	Braun's holly fern, prickly shield fern, Polystichum braunii
+n13200542	western holly fern, Polystichum scopulinum
+n13200651	soft shield fern, Polystichum setiferum
+n13200986	leather fern, leatherleaf fern, ten-day fern, Rumohra adiantiformis, Polystichum adiantiformis
+n13201423	button fern, Tectaria cicutaria
+n13201566	Indian button fern, Tectaria macrodonta
+n13201969	woodsia
+n13202125	rusty woodsia, fragrant woodsia, oblong woodsia, Woodsia ilvensis
+n13202355	Alpine woodsia, northern woodsia, flower-cup fern, Woodsia alpina
+n13202602	smooth woodsia, Woodsia glabella
+n13205058	Boston fern, Nephrolepis exaltata, Nephrolepis exaltata bostoniensis
+n13205249	basket fern, toothed sword fern, Nephrolepis pectinata
+n13206178	golden fern, leather fern, Acrostichum aureum
+n13206817	maidenhair, maidenhair fern
+n13207094	common maidenhair, Venushair, Venus'-hair fern, southern maidenhair, Venus maidenhair, Adiantum capillus-veneris
+n13207335	American maidenhair fern, five-fingered maidenhair fern, Adiantum pedatum
+n13207572	Bermuda maidenhair, Bermuda maidenhair fern, Adiantum bellum
+n13207736	brittle maidenhair, brittle maidenhair fern, Adiantum tenerum
+n13207923	Farley maidenhair, Farley maidenhair fern, Barbados maidenhair, glory fern, Adiantum tenerum farleyense
+n13208302	annual fern, Jersey fern, Anogramma leptophylla
+n13208705	lip fern, lipfern
+n13208965	smooth lip fern, Alabama lip fern, Cheilanthes alabamensis
+n13209129	lace fern, Cheilanthes gracillima
+n13209270	wooly lip fern, hairy lip fern, Cheilanthes lanosa
+n13209460	southwestern lip fern, Cheilanthes eatonii
+n13209808	bamboo fern, Coniogramme japonica
+n13210350	American rock brake, American parsley fern, Cryptogramma acrostichoides
+n13210597	European parsley fern, mountain parsley fern, Cryptogramma crispa
+n13211020	hand fern, Doryopteris pedata
+n13211790	cliff brake, cliff-brake, rock brake
+n13212025	coffee fern, Pellaea andromedifolia
+n13212175	purple rock brake, Pellaea atropurpurea
+n13212379	bird's-foot fern, Pellaea mucronata, Pellaea ornithopus
+n13212559	button fern, Pellaea rotundifolia
+n13213066	silver fern, Pityrogramma argentea
+n13213397	golden fern, Pityrogramma calomelanos aureoflava
+n13213577	gold fern, Pityrogramma chrysophylla
+n13214217	Pteris cretica
+n13214340	spider brake, spider fern, Pteris multifida
+n13214485	ribbon fern, spider fern, Pteris serrulata
+n13215258	potato fern, Marattia salicina
+n13215586	angiopteris, giant fern, Angiopteris evecta
+n13217005	skeleton fork fern, Psilotum nudum
+n13219422	horsetail
+n13219833	common horsetail, field horsetail, Equisetum arvense
+n13219976	swamp horsetail, water horsetail, Equisetum fluviatile
+n13220122	scouring rush, rough horsetail, Equisetum hyemale, Equisetum hyemale robustum, Equisetum robustum
+n13220355	marsh horsetail, Equisetum palustre
+n13220525	wood horsetail, Equisetum Sylvaticum
+n13220663	variegated horsetail, variegated scouring rush, Equisetum variegatum
+n13221529	club moss, club-moss, lycopod
+n13222877	shining clubmoss, Lycopodium lucidulum
+n13222985	alpine clubmoss, Lycopodium alpinum
+n13223090	fir clubmoss, mountain clubmoss, little clubmoss, Lycopodium selago
+n13223588	ground cedar, staghorn moss, Lycopodium complanatum
+n13223710	ground fir, princess pine, tree clubmoss, Lycopodium obscurum
+n13223843	foxtail grass, Lycopodium alopecuroides
+n13224673	spikemoss, spike moss, little club moss
+n13224922	meadow spikemoss, basket spikemoss, Selaginella apoda
+n13225244	desert selaginella, Selaginella eremophila
+n13225365	resurrection plant, rose of Jericho, Selaginella lepidophylla
+n13225617	florida selaginella, Selaginella eatonii
+n13226320	quillwort
+n13226871	earthtongue, earth-tongue
+n13228017	snuffbox fern, meadow fern, Thelypteris palustris pubescens, Dryopteris thelypteris pubescens
+n13228536	christella
+n13229543	mountain fern, Oreopteris limbosperma, Dryopteris oreopteris
+n13229951	New York fern, Parathelypteris novae-boracensis, Dryopteris noveboracensis
+n13230190	Massachusetts fern, Parathelypteris simulata, Thelypteris simulata
+n13230662	beech fern
+n13230843	broad beech fern, southern beech fern, Phegopteris hexagonoptera, Dryopteris hexagonoptera, Thelypteris hexagonoptera
+n13231078	long beech fern, narrow beech fern, northern beech fern, Phegopteris connectilis, Dryopteris phegopteris, Thelypteris phegopteris
+n13231678	shoestring fungus
+n13231919	Armillaria caligata, booted armillaria
+n13232106	Armillaria ponderosa, white matsutake
+n13232363	Armillaria zelleri
+n13232779	honey mushroom, honey fungus, Armillariella mellea
+n13233727	milkweed, silkweed
+n13234114	white milkweed, Asclepias albicans
+n13234519	poke milkweed, Asclepias exaltata
+n13234678	swamp milkweed, Asclepias incarnata
+n13234857	Mead's milkweed, Asclepias meadii, Asclepia meadii
+n13235011	purple silkweed, Asclepias purpurascens
+n13235159	showy milkweed, Asclepias speciosa
+n13235319	poison milkweed, horsetail milkweed, Asclepias subverticillata
+n13235503	butterfly weed, orange milkweed, chigger flower, chiggerflower, pleurisy root, tuber root, Indian paintbrush, Asclepias tuberosa
+n13235766	whorled milkweed, Asclepias verticillata
+n13236100	cruel plant, Araujia sericofera
+n13237188	wax plant, Hoya carnosa
+n13237508	silk vine, Periploca graeca
+n13238375	stapelia, carrion flower, starfish flower
+n13238654	Stapelias asterias
+n13238988	stephanotis
+n13239177	Madagascar jasmine, waxflower, Stephanotis floribunda
+n13239736	negro vine, Vincetoxicum hirsutum, Vincetoxicum negrum
+n13239921	zygospore
+n13240362	tree of knowledge
+n13252672	orangery
+n13354021	pocketbook
+n13555775	shit, dump
+n13579829	cordage
+n13650447	yard, pace
+n13653902	extremum, peak
+n13862407	leaf shape, leaf form
+n13862552	equilateral
+n13862780	figure
+n13863020	pencil
+n13863186	plane figure, two-dimensional figure
+n13863473	solid figure, three-dimensional figure
+n13863771	line
+n13864035	bulb
+n13864153	convex shape, convexity
+n13864965	concave shape, concavity, incurvation, incurvature
+n13865298	cylinder
+n13865483	round shape
+n13865904	heart
+n13866144	polygon, polygonal shape
+n13866626	convex polygon
+n13866827	concave polygon
+n13867005	reentrant polygon, reentering polygon
+n13867492	amorphous shape
+n13868248	closed curve
+n13868371	simple closed curve, Jordan curve
+n13868515	S-shape
+n13868944	wave, undulation
+n13869045	extrados
+n13869547	hook, crotchet
+n13869788	envelope
+n13869896	bight
+n13871717	diameter
+n13872592	cone, conoid, cone shape
+n13872822	funnel, funnel shape
+n13873361	oblong
+n13873502	circle
+n13873917	circle
+n13874073	equator
+n13874558	scallop, crenation, crenature, crenel, crenelle
+n13875392	ring, halo, annulus, doughnut, anchor ring
+n13875571	loop
+n13875884	bight
+n13876561	helix, spiral
+n13877547	element of a cone
+n13877667	element of a cylinder
+n13878306	ellipse, oval
+n13879049	quadrate
+n13879320	triangle, trigon, trilateral
+n13879816	acute triangle, acute-angled triangle
+n13880199	isosceles triangle
+n13880415	obtuse triangle, obtuse-angled triangle
+n13880551	right triangle, right-angled triangle
+n13880704	scalene triangle
+n13880994	parallel
+n13881512	trapezoid
+n13881644	star
+n13882201	pentagon
+n13882276	hexagon
+n13882487	heptagon
+n13882563	octagon
+n13882639	nonagon
+n13882713	decagon
+n13882961	rhombus, rhomb, diamond
+n13883603	spherical polygon
+n13883763	spherical triangle
+n13884261	convex polyhedron
+n13884384	concave polyhedron
+n13884930	cuboid
+n13885011	quadrangular prism
+n13886260	bell, bell shape, campana
+n13888491	angular distance
+n13889066	true anomaly
+n13889331	spherical angle
+n13891547	angle of refraction
+n13891937	acute angle
+n13893786	groove, channel
+n13894154	rut
+n13894434	bulge, bump, hump, swelling, gibbosity, gibbousness, jut, prominence, protuberance, protrusion, extrusion, excrescence
+n13895262	belly
+n13896100	bow, arc
+n13896217	crescent
+n13897198	ellipsoid
+n13897528	hypotenuse
+n13897996	balance, equilibrium, equipoise, counterbalance
+n13898207	conformation
+n13898315	symmetry, proportion
+n13898645	spheroid, ellipsoid of revolution
+n13899735	spherule
+n13900287	toroid
+n13900422	column, tower, pillar
+n13901211	barrel, drum
+n13901321	pipe, tube
+n13901423	pellet
+n13901490	bolus
+n13901858	dewdrop
+n13902048	ridge
+n13902336	rim
+n13902793	taper
+n13903079	boundary, edge, bound
+n13905121	incisure, incisura
+n13905275	notch
+n13905792	wrinkle, furrow, crease, crinkle, seam, line
+n13906484	dermatoglyphic
+n13906669	frown line
+n13906767	line of life, life line, lifeline
+n13906936	line of heart, heart line, love line, mensal line
+n13907272	crevice, cranny, crack, fissure, chap
+n13908201	cleft
+n13908580	roulette, line roulette
+n13911045	node
+n13912260	tree, tree diagram
+n13912540	stemma
+n13914141	brachium
+n13914265	fork, crotch
+n13914608	block, cube
+n13915023	ovoid
+n13915113	tetrahedron
+n13915209	pentahedron
+n13915305	hexahedron
+n13915999	regular polyhedron, regular convex solid, regular convex polyhedron, Platonic body, Platonic solid, ideal solid
+n13916363	polyhedral angle
+n13916721	cube, regular hexahedron
+n13917690	truncated pyramid
+n13917785	truncated cone
+n13918274	tail, tail end
+n13918387	tongue, knife
+n13918717	trapezohedron
+n13919547	wedge, wedge shape, cuneus
+n13919919	keel
+n13926786	place, shoes
+n14131950	herpes
+n14175579	chlamydia
+n14564779	wall
+n14582716	micronutrient
+n14583400	chyme
+n14585392	ragweed pollen
+n14592309	pina cloth
+n14603798	chlorobenzylidenemalononitrile, CS gas
+n14633206	carbon, C, atomic number 6
+n14685296	charcoal, wood coal
+n14696793	rock, stone
+n14698884	gravel, crushed rock
+n14714645	aflatoxin
+n14720833	alpha-tocopheral
+n14765422	leopard
+n14785065	bricks and mortar
+n14786943	lagging
+n14804958	hydraulic cement, Portland cement
+n14810561	choline
+n14820180	concrete
+n14821852	glass wool
+n14844693	soil, dirt
+n14853210	high explosive
+n14858292	litter
+n14867545	fish meal
+n14891255	Greek fire
+n14899328	culture medium, medium
+n14900184	agar, nutrient agar
+n14900342	blood agar
+n14908027	hip tile, hipped tile
+n14909584	hyacinth, jacinth
+n14914945	hydroxide ion, hydroxyl ion
+n14915184	ice, water ice
+n14919819	inositol
+n14938389	linoleum, lino
+n14941787	lithia water
+n14942411	lodestone, loadstone
+n14973585	pantothenic acid, pantothen
+n14974264	paper
+n14975598	papyrus
+n14976759	pantile
+n14976871	blacktop, blacktopping
+n14977188	tarmacadam, tarmac
+n14977504	paving, pavement, paving material
+n14992287	plaster
+n14993378	poison gas
+n15005577	ridge tile
+n15006012	roughcast
+n15019030	sand
+n15048888	spackle, spackling compound
+n15060326	render
+n15060688	wattle and daub
+n15062057	stucco
+n15067877	tear gas, teargas, lacrimator, lachrymator
+n15075141	toilet tissue, toilet paper, bathroom tissue
+n15086247	linseed, flaxseed
+n15089258	vitamin
+n15089472	fat-soluble vitamin
+n15089645	water-soluble vitamin
+n15089803	vitamin A, antiophthalmic factor, axerophthol, A
+n15090065	vitamin A1, retinol
+n15090238	vitamin A2, dehydroretinol
+n15090742	B-complex vitamin, B complex, vitamin B complex, vitamin B, B vitamin, B
+n15091129	vitamin B1, thiamine, thiamin, aneurin, antiberiberi factor
+n15091304	vitamin B12, cobalamin, cyanocobalamin, antipernicious anemia factor
+n15091473	vitamin B2, vitamin G, riboflavin, lactoflavin, ovoflavin, hepatoflavin
+n15091669	vitamin B6, pyridoxine, pyridoxal, pyridoxamine, adermin
+n15091846	vitamin Bc, vitamin M, folate, folic acid, folacin, pteroylglutamic acid, pteroylmonoglutamic acid
+n15092059	niacin, nicotinic acid
+n15092227	vitamin D, calciferol, viosterol, ergocalciferol, cholecalciferol, D
+n15092409	vitamin E, tocopherol, E
+n15092650	biotin, vitamin H
+n15092751	vitamin K, naphthoquinone, antihemorrhagic factor
+n15092942	vitamin K1, phylloquinone, phytonadione
+n15093049	vitamin K3, menadione
+n15093137	vitamin P, bioflavinoid, citrin
+n15093298	vitamin C, C, ascorbic acid
+n15102359	planking
+n15102455	chipboard, hardboard
+n15102894	knothole
diff --git a/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/README.md b/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/README.md
new file mode 100644
index 00000000000..4fa176c3852
--- /dev/null
+++ b/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/README.md
@@ -0,0 +1,65 @@
+Step-by-Step
+============
+
+This document is used to enable Tensorflow Keras inception_v3 model quantization and benchmark using Intel® Neural Compressor.
+This example can run on Intel CPUs and GPUs.
+
+
+# Prerequisite
+
+## 1. Environment
+
+### Installation
+```shell
+# Install Intel® Neural Compressor
+pip install neural-compressor
+```
+
+### Install Requirements
+The Tensorflow and intel-extension-for-tensorflow is mandatory to be installed to run this example.
+The Intel Extension for Tensorflow for Intel CPUs is installed as default.
+```shell
+pip install -r requirements.txt
+```
+> Note: Validated TensorFlow [Version](/docs/source/installation_guide.md#validated-software-environment).
+
+## 2. Prepare Pretrained model
+
+The pretrained model is provided by [Keras Applications](https://keras.io/api/applications/). prepare the model, Run as follow: 
+ ```
+python prepare_model.py --output_model=/path/to/model
+ ```
+`--output_model ` the model should be saved as SavedModel format or H5 format.
+
+
+## 3. Prepare Dataset
+
+  TensorFlow [models](https://github.com/tensorflow/models) repo provides [scripts and instructions](https://github.com/tensorflow/models/tree/master/research/slim#an-automated-script-for-processing-imagenet-data) to download, process and convert the ImageNet dataset to the TF records format.
+  We also prepared related scripts in `imagenet_prepare` directory. To download the raw images, the user must create an account with image-net.org. If you have downloaded the raw data and preprocessed the validation data by moving the images into the appropriate sub-directory based on the label (synset) of the image. we can use below command ro convert it to tf records format.
+
+  ```shell
+  cd examples/keras/image_recognition/
+  # convert validation subset
+  bash prepare_dataset.sh --output_dir=/inception_v3/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
+  # convert train subset
+  bash prepare_dataset.sh --output_dir=/inception_v3/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/train/ --subset=train
+  cd inception_v3/quantization/ptq
+  ```
+> **Note**: 
+> The raw ImageNet dataset resides in JPEG files should be in the following directory structure. Taking validation set as an example:<br>
+> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/PATH/TO/img_raw/val/n01440764/ILSVRC2012_val_00000293.JPEG<br>
+> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/PATH/TO/img_raw/val/n01440764/ILSVRC2012_val_00000543.JPEG<br>
+> where 'n01440764' is the unique synset label associated with these images.
+
+# Run Command
+
+## Quantization
+  ```shell
+  bash run_quant.sh --input_model=./inception_v3_keras/ --output_model=./result --dataset_location=/path/to/evaluation/dataset
+  ```
+
+## Benchmark
+  ```shell
+  bash run_benchmark.sh --input_model=./result --dataset_location=/path/to/evaluation/dataset --mode=performance --batch_size=1
+  bash run_benchmark.sh --input_model=./result --dataset_location=/path/to/evaluation/dataset --mode=accuracy --batch_size=32
+  ```
diff --git a/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/data_process.py b/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/data_process.py
new file mode 100644
index 00000000000..b8cd01593c6
--- /dev/null
+++ b/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/data_process.py
@@ -0,0 +1,543 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2024 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import os
+import collections
+
+import numpy as np
+import tensorflow as tf
+
+from abc import abstractmethod
+from neural_compressor.common import logger
+from neural_compressor.tensorflow.utils.data import default_collate
+
+class ParseDecodeImagenet:
+    """Parse features in Example proto.
+
+    Returns:
+        tuple of parsed image and label
+    """
+
+    def __call__(self, sample):
+        """Parse features in example."""
+        # Dense features in Example proto.
+        feature_map = {
+            "image/encoded": tf.io.FixedLenFeature([], dtype=tf.string, default_value=""),
+            "image/class/label": tf.io.FixedLenFeature([1], dtype=tf.int64, default_value=-1),
+        }
+
+        sparse_float32 = tf.io.VarLenFeature(dtype=tf.float32)
+        # Sparse features in Example proto.
+        feature_map.update(
+            {
+                k: sparse_float32
+                for k in [
+                    "image/object/bbox/xmin",
+                    "image/object/bbox/ymin",
+                    "image/object/bbox/xmax",
+                    "image/object/bbox/ymax",
+                ]
+            }
+        )
+
+        features = tf.io.parse_single_example(serialized=sample, features=feature_map)
+        label = tf.cast(features["image/class/label"], dtype=tf.int32)
+        image = features["image/encoded"]
+        image = tf.image.decode_jpeg(image, channels=3, fancy_upscaling=False, dct_method="INTEGER_FAST")
+        return (image, label)
+
+
+class BilinearImagenetTransform(object):
+    """Combination of a series of transforms which is applicable to images in Imagenet.
+
+    Args:
+        height: Height of the result
+        width:Width of the result
+        central_fraction(float, default=0.875):fraction of size to crop
+        mean_value(list, default=[0.0,0.0,0.0]):means for each channel
+        scale(float, default=1.0):std value
+
+    Returns:
+        tuple of processed image and label
+    """
+
+    def __init__(self, height, width, central_fraction=0.875, mean_value=[0.0, 0.0, 0.0], scale=1.0):
+        """Initialize `BilinearImagenetTransform` class."""
+        self.height = height
+        self.width = width
+        self.mean_value = mean_value
+        self.scale = scale
+        self.central_fraction = central_fraction
+
+    # sample is (images, labels)
+    def __call__(self, sample):
+        """Convert `BilinearImagenetTransform` feature."""
+        image, label = sample
+        if image.dtype is not tf.float32:
+            image = tf.image.convert_image_dtype(image, dtype=tf.float32)
+        # Crop the central region of the image containing 87.5% area of the original image.
+        if self.central_fraction:
+            image = tf.image.central_crop(image, central_fraction=self.central_fraction)
+
+        if self.height and self.width:
+            # Resize the image to the specified height and width.
+            image = tf.expand_dims(image, 0)
+            image = tf.image.resize(image, [self.height, self.width], method=tf.image.ResizeMethod.BILINEAR)
+            image = tf.squeeze(image, [0])
+
+        image = tf.subtract(image, 0.5)
+        image = tf.multiply(image, 2.0)
+        means = tf.broadcast_to(self.mean_value, tf.shape(input=image))
+        image = (image - means) * self.scale
+        return (image, label)
+
+
+class ComposeTransform(object):
+    """Composes several transforms together.
+
+    Args:
+        transform_list (list of Transform objects):  list of transforms to compose
+
+    Returns:
+        sample (tuple): tuple of processed image and label
+    """
+
+    def __init__(self, transform_list):
+        """Initialize `ComposeTransform` class."""
+        self.transform_list = transform_list
+
+    def __call__(self, sample):
+        """Call transforms in transform_list."""
+        for transform in self.transform_list:
+            sample = transform(sample)
+        return sample
+
+
+class ShiftRescale(object):
+    """Label shift by 1 and rescale.
+
+    Returns:
+        tuple of processed image and label
+    """
+
+    def __call__(self, sample):
+        image, label = sample
+        label -= 1
+        image = (image - 127.5) / 127.5
+        return (image, label)
+
+
+class LabelShift(object):
+    """Convert label to label - label_shift.
+
+    Args:
+        label_shift(int, default=0): number of label shift
+
+    Returns:
+        tuple of processed image and label
+    """
+
+    def __init__(self, label_shift=0):
+        """Initialize `LabelShift` class."""
+        self.label_shift = label_shift
+
+    def __call__(self, sample):
+        """Convert label to label_shift."""
+        images, labels = sample
+        if isinstance(labels, np.ndarray):
+            labels = labels - self.label_shift
+        elif isinstance(labels, list):
+            if isinstance(labels[0], tuple):
+                labels = [tuple(np.array(label) - self.label_shift) for label in labels]
+            elif isinstance(labels[0], np.ndarray):
+                labels = [label - self.label_shift for label in labels]
+            else:
+                labels = np.array(labels) - self.label_shift
+                labels = labels.tolist()
+        else:
+            labels = np.array(labels) - self.label_shift
+        return images, labels
+
+
+class ImageRecordDataset(object):
+    """Tensorflow imageNet database in tf record format.
+
+    Please arrange data in this way:
+        root/validation-000-of-100
+        root/validation-001-of-100
+        ...
+        root/validation-099-of-100
+    The file name needs to follow this pattern: '* - * -of- *'
+
+    Args: root (str): Root directory of dataset.
+          transform (transform object, default=None):  transform to process input data.
+          filter (Filter objects, default=None): filter out examples according
+                                                 to specific conditions.
+    """
+
+    """Configuration for Imagenet dataset."""
+
+    def __new__(cls, root, transform=None, filter=None):
+        """Build a new object of TensorflowImageRecord class."""
+        from tensorflow.python.platform import gfile  # pylint: disable=no-name-in-module
+
+        glob_pattern = os.path.join(root, "*-*-of-*")
+        file_names = gfile.Glob(glob_pattern)
+        if not file_names:
+            raise ValueError("Found no files in --root matching: {}".format(glob_pattern))
+
+        # pylint: disable=no-name-in-module
+        from tensorflow.python.data.experimental import parallel_interleave
+
+        ds = tf.data.TFRecordDataset.list_files(file_names, shuffle=False)
+        ds = ds.apply(parallel_interleave(tf.data.TFRecordDataset, cycle_length=len(file_names)))
+
+        if transform is not None:
+            transform.transform_list.insert(0, ParseDecodeImagenet())
+        else:
+            transform = ParseDecodeImagenet()
+        ds = ds.map(transform, num_parallel_calls=None)
+        ds = ds.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)  # this number can be tuned
+        return ds
+
+
+class BaseMetric(object):
+    """The base class of Metric."""
+
+    def __init__(self, metric, single_output=False, hvd=None):
+        """Initialize the basic metric.
+
+        Args:
+            metric: The metric class.
+            single_output: Whether the output is single or not, defaults to False.
+            hvd: The Horovod class for distributed training, defaults to None.
+        """
+        self._metric_cls = metric
+        self._single_output = single_output
+        self._hvd = hvd
+
+    def __call__(self, *args, **kwargs):
+        """Evaluate the model predictions, and the reference.
+
+        Returns:
+            The class itself.
+        """
+        self._metric = self._metric_cls(*args, **kwargs)
+        return self
+
+    @abstractmethod
+    def update(self, preds, labels=None, sample_weight=None):
+        """Update the state that need to be evaluated.
+
+        Args:
+            preds: The prediction result.
+            labels: The reference. Defaults to None.
+            sample_weight: The sampling weight. Defaults to None.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def reset(self):
+        """Clear the predictions and labels.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def result(self):
+        """Evaluate the difference between predictions and labels.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @property
+    def metric(self):
+        """Return its metric class.
+
+        Returns:
+            The metric class.
+        """
+        return self._metric_cls
+
+    @property
+    def hvd(self):
+        """Return its hvd class.
+
+        Returns:
+            The hvd class.
+        """
+        return self._hvd
+
+    @hvd.setter
+    def hvd(self, hvd):
+        """Set its hvd.
+
+        Args:
+            hvd: The Horovod class for distributed training.
+        """
+        self._hvd = hvd
+
+
+class TopKMetric(BaseMetric):
+    """Compute Top-k Accuracy classification score for Tensorflow model.
+
+    This metric computes the number of times where the correct label is among
+    the top k labels predicted.
+
+    Attributes:
+        k (int): The number of most likely outcomes considered to find the correct label.
+        num_correct: The number of predictions that were correct classified.
+        num_sample: The total number of predictions.
+    """
+
+    def __init__(self, k=1):
+        """Initialize the k, number of samples and correct predictions.
+
+        Args:
+            k: The number of most likely outcomes considered to find the correct label.
+        """
+        self.k = k
+        self.num_correct = 0
+        self.num_sample = 0
+
+    def update(self, preds, labels, sample_weight=None):
+        """Add the predictions and labels.
+
+        Args:
+            preds: The predictions.
+            labels: The labels corresponding to the predictions.
+            sample_weight: The sample weight.
+        """
+        preds, labels = TopKMetric._topk_shape_validate(preds, labels)
+
+        labels = labels.reshape([len(labels)])
+        with tf.Graph().as_default() as acc_graph:
+            topk = tf.nn.in_top_k(
+                predictions=tf.constant(preds, dtype=tf.float32), targets=tf.constant(labels, dtype=tf.int32), k=self.k
+            )
+            fp32_topk = tf.cast(topk, tf.float32)
+            correct_tensor = tf.reduce_sum(input_tensor=fp32_topk)
+
+            with tf.compat.v1.Session() as acc_sess:
+                correct = acc_sess.run(correct_tensor)
+
+        self.num_sample += len(labels)
+        self.num_correct += correct
+
+    def reset(self):
+        """Reset the number of samples and correct predictions."""
+        self.num_correct = 0
+        self.num_sample = 0
+
+    def result(self):
+        """Compute the top-k score.
+
+        Returns:
+            The top-k score.
+        """
+        if self.num_sample == 0:
+            logger.warning("Sample num during evaluation is 0.")
+            return 0
+        elif getattr(self, "_hvd", None) is not None:  # pragma: no cover
+            allgather_num_correct = sum(self._hvd.allgather_object(self.num_correct))
+            allgather_num_sample = sum(self._hvd.allgather_object(self.num_sample))
+            return allgather_num_correct / allgather_num_sample
+        return self.num_correct / self.num_sample
+
+    @staticmethod
+    def _topk_shape_validate(preds, labels):
+        # preds shape can be Nxclass_num or class_num(N=1 by default)
+        # it's more suitable for 'Accuracy' with preds shape Nx1(or 1) output from argmax
+        if isinstance(preds, int):
+            preds = [preds]
+            preds = np.array(preds)
+        elif isinstance(preds, np.ndarray):
+            preds = np.array(preds)
+        elif isinstance(preds, list):
+            preds = np.array(preds)
+            preds = preds.reshape((-1, preds.shape[-1]))
+
+        # consider labels just int value 1x1
+        if isinstance(labels, int):
+            labels = [labels]
+            labels = np.array(labels)
+        elif isinstance(labels, tuple):
+            labels = np.array([labels])
+            labels = labels.reshape((labels.shape[-1], -1))
+        elif isinstance(labels, list):
+            if isinstance(labels[0], int):
+                labels = np.array(labels)
+                labels = labels.reshape((labels.shape[0], 1))
+            elif isinstance(labels[0], tuple):
+                labels = np.array(labels)
+                labels = labels.reshape((labels.shape[-1], -1))
+            else:
+                labels = np.array(labels)
+        # labels most have 2 axis, 2 cases: N(or Nx1 sparse) or Nxclass_num(one-hot)
+        # only support 2 dimension one-shot labels
+        # or 1 dimension one-hot class_num will confuse with N
+
+        if len(preds.shape) == 1:
+            N = 1
+            class_num = preds.shape[0]
+            preds = preds.reshape([-1, class_num])
+        elif len(preds.shape) >= 2:
+            N = preds.shape[0]
+            preds = preds.reshape([N, -1])
+            class_num = preds.shape[1]
+
+        label_N = labels.shape[0]
+        assert label_N == N, "labels batch size should same with preds"
+        labels = labels.reshape([N, -1])
+        # one-hot labels will have 2 dimension not equal 1
+        if labels.shape[1] != 1:
+            labels = labels.argsort()[..., -1:]
+        return preds, labels
+    
+
+class TFDataLoader(object):  # pragma: no cover
+    """Tensorflow dataloader class.
+
+    In tensorflow1.x dataloader is coupled with the graph, but it also support feed_dict
+    method to do session run, this dataloader is designed to satisfy the usage of feed dict
+    in tf1.x. Although it's a general dataloader and can be used in MXNet and PyTorch.
+
+    Args:
+        dataset: obj. wrapper of needed data.
+        batch_size: int. batch size
+    """
+
+    def __init__(self, dataset, batch_size=1, last_batch="rollover"):
+        """Initialize `TFDataDataLoader` class."""
+        self.dataset = dataset
+        self.last_batch = last_batch
+        self.batch_size = batch_size
+        dataset = dataset.batch(batch_size)
+
+    def batch(self, batch_size, last_batch="rollover"):
+        """Dataset return data per batch."""
+        drop_last = False if last_batch == "rollover" else True
+        self.batch_size = batch_size
+        self.dataset = self.dataset.batch(batch_size, drop_last)
+
+    def __iter__(self):
+        """Iterate dataloader."""
+        return self._generate_dataloader(
+            self.dataset,
+            batch_size=self.batch_size,
+            last_batch=self.last_batch,
+        )
+
+    def _generate_dataloader(
+        self,
+        dataset,
+        batch_size=1,
+        last_batch="rollover",
+        collate_fn=None,
+        sampler=None,
+        batch_sampler=None,
+        num_workers=None,
+        pin_memory=None,
+        distributed=False,
+    ):
+        """Yield data."""
+        drop_last = False if last_batch == "rollover" else True
+
+        def check_dynamic_shape(element_spec):
+            if isinstance(element_spec, collections.abc.Sequence):
+                return any([check_dynamic_shape(ele) for ele in element_spec])
+            elif isinstance(element_spec, tf.TensorSpec):
+                return True if element_spec.shape.num_elements() is None else False
+            else:
+                raise ValueError("unrecognized element spec...")
+
+        def squeeze_output(output):
+            if isinstance(output, collections.abc.Sequence):
+                return [squeeze_output(ele) for ele in output]
+            elif isinstance(output, np.ndarray):
+                return np.squeeze(output, axis=0)
+            else:
+                raise ValueError("not supported output format....")
+
+        if tf.executing_eagerly():
+            index = 0
+            outputs = []
+            for iter_tensors in dataset:
+                samples = []
+                iter_inputs, iter_labels = iter_tensors[0], iter_tensors[1]
+                if isinstance(iter_inputs, tf.Tensor):
+                    samples.append(iter_inputs.numpy())
+                else:
+                    samples.append(tuple(iter_input.numpy() for iter_input in iter_inputs))
+                if isinstance(iter_labels, tf.Tensor):
+                    samples.append(iter_labels.numpy())
+                else:
+                    samples.append([np.array(l) for l in iter_labels])
+                index += 1
+                outputs.append(samples)
+                if index == batch_size:
+                    outputs = default_collate(outputs)
+                    yield outputs
+                    outputs = []
+                    index = 0
+            if len(outputs) > 0:
+                outputs = default_collate(outputs)
+                yield outputs
+        else:
+            try_single_batch = check_dynamic_shape(dataset.element_spec)
+            dataset = dataset.batch(1 if try_single_batch else batch_size, drop_last)
+            ds_iterator = tf.compat.v1.data.make_one_shot_iterator(dataset)
+            iter_tensors = ds_iterator.get_next()
+            data_config = tf.compat.v1.ConfigProto()
+            data_config.use_per_session_threads = 1
+            data_config.intra_op_parallelism_threads = 1
+            data_config.inter_op_parallelism_threads = 16
+            data_sess = tf.compat.v1.Session(config=data_config)
+            # pylint: disable=no-name-in-module
+            from tensorflow.python.framework.errors_impl import OutOfRangeError
+
+            while True:
+                if not try_single_batch:
+                    try:
+                        outputs = data_sess.run(iter_tensors)
+                        yield outputs
+                    except OutOfRangeError:
+                        data_sess.close()
+                        return
+                else:
+                    try:
+                        outputs = []
+                        for i in range(0, batch_size):
+                            outputs.append(squeeze_output(data_sess.run(iter_tensors)))
+                        outputs = default_collate(outputs)
+                        yield outputs
+                    except OutOfRangeError:
+                        if len(outputs) == 0:
+                            data_sess.close()
+                            return
+                        else:
+                            outputs = default_collate(outputs)
+                            yield outputs
+                            data_sess.close()
+                            return
diff --git a/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/main.py b/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/main.py
new file mode 100644
index 00000000000..2a8ba68a6cf
--- /dev/null
+++ b/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/main.py
@@ -0,0 +1,143 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2018 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import time
+
+import numpy as np
+import tensorflow as tf
+
+from neural_compressor.utils import logger
+from data_process import (
+    ImageRecordDataset, 
+    ComposeTransform, 
+    BilinearImagenetTransform, 
+    TFDataLoader,
+    TopKMetric,
+    LabelShift,
+)
+
+tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
+
+flags = tf.compat.v1.flags
+FLAGS = flags.FLAGS
+
+## Required parameters
+flags.DEFINE_string(
+    'input_model', None, 'Run inference with specified keras model.')
+
+flags.DEFINE_string(
+    'output_model', None, 'The output quantized model.')
+
+flags.DEFINE_string(
+    'mode', 'performance', 'define benchmark mode for accuracy or performance')
+
+flags.DEFINE_bool(
+    'tune', False, 'whether to tune the model')
+
+flags.DEFINE_bool(
+    'benchmark', False, 'whether to benchmark the model')
+
+flags.DEFINE_string(
+    'calib_data', None, 'location of calibration dataset')
+
+flags.DEFINE_string(
+    'eval_data', None, 'location of evaluate dataset')
+
+flags.DEFINE_integer('batch_size', 32, 'batch_size')
+
+flags.DEFINE_integer(
+    'iters', 100, 'maximum iteration when evaluating performance')
+
+height = width = 299
+eval_dataset = ImageRecordDataset(root=FLAGS.eval_data, transform=ComposeTransform(transform_list= \
+                 [BilinearImagenetTransform(height=height, width=width)]))
+
+eval_dataloader = TFDataLoader(dataset=eval_dataset, batch_size=FLAGS.batch_size)
+
+if FLAGS.calib_data:
+    calib_dataset = ImageRecordDataset(root=FLAGS.calib_data, transform= \
+        ComposeTransform(transform_list= [BilinearImagenetTransform(height=height, width=width)]))
+    calib_dataloader = TFDataLoader(dataset=calib_dataset, batch_size=10)
+
+def evaluate(model):
+    """
+    Custom evaluate function to inference the model for specified metric on validation dataset.
+
+    Args:
+        model (tf.keras.Model): The input model will be the objection of tf.keras.Model.
+
+    Returns:
+        accuracy (float): evaluation result, the larger is better.
+    """
+    latency_list = []
+    metric = TopKMetric()
+    postprocess = LabelShift(label_shift=1)
+
+    def eval_func(dataloader, metric):
+        warmup = 5
+        iteration = None
+        if FLAGS.benchmark and FLAGS.mode == 'performance':
+            iteration = FLAGS.iters
+        for idx, (inputs, labels) in enumerate(dataloader):
+            start = time.time()
+            predictions = model.predict_on_batch(inputs)
+            end = time.time()
+            latency_list.append(end - start)
+            predictions, labels = postprocess((predictions, labels))
+            metric.update(predictions, labels)
+            if iteration and idx >= iteration:
+                break
+        latency = np.array(latency_list[warmup:]).mean() / eval_dataloader.batch_size
+        return latency
+
+    latency = eval_func(eval_dataloader, metric)
+    if FLAGS.benchmark:
+        logger.info("\n{} mode benchmark result:".format(FLAGS.mode))
+        for i, res in enumerate(latency_list):
+            logger.debug("Iteration {} result {}:".format(i, res))
+    if FLAGS.benchmark and FLAGS.mode == 'performance':
+        logger.info("Batch size = {}".format(eval_dataloader.batch_size))
+        logger.info("Latency: {:.3f} ms".format(latency * 1000))
+        logger.info("Throughput: {:.3f} images/sec".format(1. / latency))
+    acc = metric.result()
+    return acc
+
+def main(_):
+    if FLAGS.tune:
+        from neural_compressor.common import set_random_seed
+        from neural_compressor.tensorflow import StaticQuantConfig, quantize_model
+
+        set_random_seed(9527)
+        quant_config = StaticQuantConfig()
+        q_model = quantize_model(FLAGS.input_model, quant_config, calib_dataloader)
+        q_model.save(FLAGS.output_model)
+        
+
+    if FLAGS.benchmark:
+        from neural_compressor.tensorflow import Model
+
+        inc_model = Model(FLAGS.input_model)
+        if FLAGS.mode == 'performance':
+            evaluate(inc_model.model)
+        else:
+            accuracy = evaluate(inc_model.model)
+            logger.info('Batch size = %d' % FLAGS.batch_size)
+            logger.info("Accuracy: %.5f" % accuracy)
+
+
+if __name__ == "__main__":
+    tf.compat.v1.app.run()
diff --git a/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/prepare_model.py b/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/prepare_model.py
new file mode 100644
index 00000000000..abf63dc93b4
--- /dev/null
+++ b/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/prepare_model.py
@@ -0,0 +1,35 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2022 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import argparse
+from tensorflow.keras.applications.inception_v3 import InceptionV3
+def get_inception_v3_model(saved_path):
+    model = InceptionV3(weights='imagenet')
+    model.save(saved_path)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+    description='Export pretained keras model',
+    formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    parser.add_argument(
+        '--output_model',
+        type=str,
+        help='path to exported model file')
+
+    args = parser.parse_args()
+    get_inception_v3_model(args.output_model)
diff --git a/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/requirements.txt b/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/requirements.txt
new file mode 100644
index 00000000000..2f0697d8502
--- /dev/null
+++ b/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/requirements.txt
@@ -0,0 +1,2 @@
+tensorflow
+intel-extension-for-tensorflow[cpu]
diff --git a/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/run_benchmark.sh b/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/run_benchmark.sh
new file mode 100644
index 00000000000..43b1636c839
--- /dev/null
+++ b/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/run_benchmark.sh
@@ -0,0 +1,51 @@
+#!/bin/bash
+set -x
+
+function main {
+
+  init_params "$@"
+  run_benchmark
+
+}
+
+# init params
+function init_params {
+  batch_size=32
+  iters=100
+
+  for var in "$@"
+  do
+    case $var in
+      --input_model=*)
+          input_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --mode=*)
+          mode=$(echo $var |cut -f2 -d=)    
+      ;;
+      --dataset_location=*)
+          dataset_location=$(echo $var |cut -f2 -d=)    
+      ;;
+      --batch_size=*)
+          batch_size=$(echo $var |cut -f2 -d=)
+      ;;
+      --iters=*)
+          iters=$(echo $var |cut -f2 -d=)
+      ;;
+    esac
+  done
+
+}
+
+# run_tuning
+function run_benchmark {
+
+    python main.py \
+            --input_model ${input_model} \
+            --benchmark \
+            --mode ${mode} \
+            --eval_data ${dataset_location} \
+            --batch_size ${batch_size} \
+            --iters ${iters}
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/run_quant.sh b/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/run_quant.sh
new file mode 100644
index 00000000000..7e3ed727f71
--- /dev/null
+++ b/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/run_quant.sh
@@ -0,0 +1,40 @@
+#!/bin/bash
+set -x
+
+function main {
+  init_params "$@"
+  run_tuning
+
+}
+
+# init params
+function init_params {
+
+  for var in "$@"
+  do
+    case $var in
+      --input_model=*)
+          input_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --output_model=*)
+          output_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --dataset_location=*)
+          dataset_location=$(echo $var |cut -f2 -d=)
+      ;;
+    esac
+  done
+
+}
+
+# run_tuning
+function run_tuning {
+    python main.py \
+            --input_model ${input_model} \
+            --output_model ${output_model} \
+            --eval_data ${dataset_location} \
+            --calib_data ${dataset_location} \
+            --tune
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/README.md b/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/README.md
new file mode 100644
index 00000000000..cd7a564b505
--- /dev/null
+++ b/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/README.md
@@ -0,0 +1,75 @@
+Step-by-Step
+============
+
+This document is used to enable Tensorflow Keras model mobilenet_v2 quantization and benchmark using Intel® Neural Compressor.
+This example can run on Intel CPUs and GPUs.
+
+
+# Prerequisite
+
+## 1. Environment
+
+### Installation
+```shell
+# Install Intel® Neural Compressor
+pip install neural-compressor
+```
+
+### Install Requirements
+The Tensorflow and intel-extension-for-tensorflow is mandatory to be installed to run this example.
+The Intel Extension for Tensorflow for Intel CPUs is installed as default.
+```shell
+pip install -r requirements.txt
+```
+> Note: Validated TensorFlow [Version](/docs/source/installation_guide.md#validated-software-environment).
+
+## 2. Prepare Pretrained model
+
+The pretrained model is provided by [Keras Applications](https://keras.io/api/applications/). prepare the model, Run as follow: 
+ ```
+python prepare_model.py --output_model=/path/to/model
+ ```
+`--output_model ` the model should be saved as SavedModel format or H5 format.
+
+## 3. Prepare Dataset
+
+  TensorFlow [models](https://github.com/tensorflow/models) repo provides [scripts and instructions](https://github.com/tensorflow/models/tree/master/research/slim#an-automated-script-for-processing-imagenet-data) to download, process and convert the ImageNet dataset to the TF records format.
+  We also prepared related scripts in `imagenet_prepare` directory. To download the raw images, the user must create an account with image-net.org. If you have downloaded the raw data and preprocessed the validation data by moving the images into the appropriate sub-directory based on the label (synset) of the image. we can use below command ro convert it to tf records format.
+
+  ```shell
+  cd examples/keras/image_recognition/
+  # convert validation subset
+  bash prepare_dataset.sh --output_dir=/mobilenet_v2/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
+  # convert train subset
+  bash prepare_dataset.sh --output_dir=/mobilenet_v2/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/train/ --subset=train
+  cd mobilenet_v2/quantization/ptq
+  ```
+> **Note**: 
+> The raw ImageNet dataset resides in JPEG files should be in the following directory structure. Taking validation set as an example:<br>
+> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/PATH/TO/img_raw/val/n01440764/ILSVRC2012_val_00000293.JPEG<br>
+> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/PATH/TO/img_raw/val/n01440764/ILSVRC2012_val_00000543.JPEG<br>
+> where 'n01440764' is the unique synset label associated with these images.
+
+# Run Command
+
+## Quantization Config
+The Quantization Config class has default parameters setting for running on Intel CPUs. If running this example on Intel GPUs, the 'backend' parameter should be set to 'itex' and the 'device' parameter should be set to 'gpu'.
+
+```
+config = PostTrainingQuantConfig(
+    device="gpu",
+    backend="itex",
+    ...
+    )
+```
+
+## Quantization
+  ```shell
+  bash run_quant.sh --input_model=./mobilenet_v2_keras/ --output_model=./result --dataset_location=/path/to/evaluation/dataset
+  ```
+
+## Benchmark
+  ```shell
+  bash run_benchmark.sh --input_model=./result --mode=accuracy --dataset_location=/path/to/evaluation/dataset --batch_size=32
+  bash run_benchmark.sh --input_model=./result --mode=performance --dataset_location=/path/to/evaluation/dataset --batch_size=1
+  ```
diff --git a/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/data_process.py b/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/data_process.py
new file mode 100644
index 00000000000..a655a5ce9a8
--- /dev/null
+++ b/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/data_process.py
@@ -0,0 +1,543 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2024 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import os
+import collections
+
+import numpy as np
+import tensorflow as tf
+
+from abc import abstractmethod
+from neural_compressor.common import logger
+from neural_compressor.tensorflow.utils.data import default_collate
+
+class ParseDecodeImagenet:
+    """Parse features in Example proto.
+
+    Returns:
+        tuple of parsed image and label
+    """
+
+    def __call__(self, sample):
+        """Parse features in example."""
+        # Dense features in Example proto.
+        feature_map = {
+            "image/encoded": tf.io.FixedLenFeature([], dtype=tf.string, default_value=""),
+            "image/class/label": tf.io.FixedLenFeature([1], dtype=tf.int64, default_value=-1),
+        }
+
+        sparse_float32 = tf.io.VarLenFeature(dtype=tf.float32)
+        # Sparse features in Example proto.
+        feature_map.update(
+            {
+                k: sparse_float32
+                for k in [
+                    "image/object/bbox/xmin",
+                    "image/object/bbox/ymin",
+                    "image/object/bbox/xmax",
+                    "image/object/bbox/ymax",
+                ]
+            }
+        )
+
+        features = tf.io.parse_single_example(serialized=sample, features=feature_map)
+        label = tf.cast(features["image/class/label"], dtype=tf.int32)
+        image = features["image/encoded"]
+        image = tf.image.decode_jpeg(image, channels=3, fancy_upscaling=False, dct_method="INTEGER_FAST")
+        return (image, label)
+
+
+class BilinearImagenetTransform(object):
+    """Combination of a series of transforms which is applicable to images in Imagenet.
+
+    Args:
+        height: Height of the result
+        width:Width of the result
+        central_fraction(float, default=0.875):fraction of size to crop
+        mean_value(list, default=[0.0,0.0,0.0]):means for each channel
+        scale(float, default=1.0):std value
+
+    Returns:
+        tuple of processed image and label
+    """
+
+    def __init__(self, height, width, central_fraction=0.875, mean_value=[0.0, 0.0, 0.0], scale=1.0):
+        """Initialize `BilinearImagenetTransform` class."""
+        self.height = height
+        self.width = width
+        self.mean_value = mean_value
+        self.scale = scale
+        self.central_fraction = central_fraction
+
+    # sample is (images, labels)
+    def __call__(self, sample):
+        """Convert `BilinearImagenetTransform` feature."""
+        image, label = sample
+        if image.dtype is not tf.float32:
+            image = tf.image.convert_image_dtype(image, dtype=tf.float32)
+        # Crop the central region of the image containing 87.5% area of the original image.
+        if self.central_fraction:
+            image = tf.image.central_crop(image, central_fraction=self.central_fraction)
+
+        if self.height and self.width:
+            # Resize the image to the specified height and width.
+            image = tf.expand_dims(image, 0)
+            image = tf.image.resize(image, [self.height, self.width], method=tf.image.ResizeMethod.BILINEAR)
+            image = tf.squeeze(image, [0])
+
+        image = tf.subtract(image, 0.5)
+        image = tf.multiply(image, 2.0)
+        means = tf.broadcast_to(self.mean_value, tf.shape(input=image))
+        image = (image - means) * self.scale
+        return (image, label)
+
+
+class ComposeTransform(object):
+    """Composes several transforms together.
+
+    Args:
+        transform_list (list of Transform objects):  list of transforms to compose
+
+    Returns:
+        sample (tuple): tuple of processed image and label
+    """
+
+    def __init__(self, transform_list):
+        """Initialize `ComposeTransform` class."""
+        self.transform_list = transform_list
+
+    def __call__(self, sample):
+        """Call transforms in transform_list."""
+        for transform in self.transform_list:
+            sample = transform(sample)
+        return sample
+
+
+class LabelShift(object):
+    """Convert label to label - label_shift.
+
+    Args:
+        label_shift(int, default=0): number of label shift
+
+    Returns:
+        tuple of processed image and label
+    """
+
+    def __init__(self, label_shift=0):
+        """Initialize `LabelShift` class."""
+        self.label_shift = label_shift
+
+    def __call__(self, sample):
+        """Convert label to label_shift."""
+        images, labels = sample
+        if isinstance(labels, np.ndarray):
+            labels = labels - self.label_shift
+        elif isinstance(labels, list):
+            if isinstance(labels[0], tuple):
+                labels = [tuple(np.array(label) - self.label_shift) for label in labels]
+            elif isinstance(labels[0], np.ndarray):
+                labels = [label - self.label_shift for label in labels]
+            else:
+                labels = np.array(labels) - self.label_shift
+                labels = labels.tolist()
+        else:
+            labels = np.array(labels) - self.label_shift
+        return images, labels
+
+
+class ShiftRescale(object):
+    """Label shift by 1 and rescale.
+
+    Returns:
+        tuple of processed image and label
+    """
+
+    def __call__(self, sample):
+        image, label = sample
+        label -= 1
+        image = (image - 127.5) / 127.5
+        return (image, label)
+
+
+class ImageRecordDataset(object):
+    """Tensorflow imageNet database in tf record format.
+
+    Please arrange data in this way:
+        root/validation-000-of-100
+        root/validation-001-of-100
+        ...
+        root/validation-099-of-100
+    The file name needs to follow this pattern: '* - * -of- *'
+
+    Args: root (str): Root directory of dataset.
+          transform (transform object, default=None):  transform to process input data.
+          filter (Filter objects, default=None): filter out examples according
+                                                 to specific conditions.
+    """
+
+    """Configuration for Imagenet dataset."""
+
+    def __new__(cls, root, transform=None, filter=None):
+        """Build a new object of TensorflowImageRecord class."""
+        from tensorflow.python.platform import gfile  # pylint: disable=no-name-in-module
+
+        glob_pattern = os.path.join(root, "*-*-of-*")
+        file_names = gfile.Glob(glob_pattern)
+        if not file_names:
+            raise ValueError("Found no files in --root matching: {}".format(glob_pattern))
+
+        # pylint: disable=no-name-in-module
+        from tensorflow.python.data.experimental import parallel_interleave
+
+        ds = tf.data.TFRecordDataset.list_files(file_names, shuffle=False)
+        ds = ds.apply(parallel_interleave(tf.data.TFRecordDataset, cycle_length=len(file_names)))
+
+        if transform is not None:
+            transform.transform_list.insert(0, ParseDecodeImagenet())
+        else:
+            transform = ParseDecodeImagenet()
+        ds = ds.map(transform, num_parallel_calls=None)
+        ds = ds.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)  # this number can be tuned
+        return ds
+
+
+class BaseMetric(object):
+    """The base class of Metric."""
+
+    def __init__(self, metric, single_output=False, hvd=None):
+        """Initialize the basic metric.
+
+        Args:
+            metric: The metric class.
+            single_output: Whether the output is single or not, defaults to False.
+            hvd: The Horovod class for distributed training, defaults to None.
+        """
+        self._metric_cls = metric
+        self._single_output = single_output
+        self._hvd = hvd
+
+    def __call__(self, *args, **kwargs):
+        """Evaluate the model predictions, and the reference.
+
+        Returns:
+            The class itself.
+        """
+        self._metric = self._metric_cls(*args, **kwargs)
+        return self
+
+    @abstractmethod
+    def update(self, preds, labels=None, sample_weight=None):
+        """Update the state that need to be evaluated.
+
+        Args:
+            preds: The prediction result.
+            labels: The reference. Defaults to None.
+            sample_weight: The sampling weight. Defaults to None.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def reset(self):
+        """Clear the predictions and labels.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def result(self):
+        """Evaluate the difference between predictions and labels.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @property
+    def metric(self):
+        """Return its metric class.
+
+        Returns:
+            The metric class.
+        """
+        return self._metric_cls
+
+    @property
+    def hvd(self):
+        """Return its hvd class.
+
+        Returns:
+            The hvd class.
+        """
+        return self._hvd
+
+    @hvd.setter
+    def hvd(self, hvd):
+        """Set its hvd.
+
+        Args:
+            hvd: The Horovod class for distributed training.
+        """
+        self._hvd = hvd
+
+
+class TopKMetric(BaseMetric):
+    """Compute Top-k Accuracy classification score for Tensorflow model.
+
+    This metric computes the number of times where the correct label is among
+    the top k labels predicted.
+
+    Attributes:
+        k (int): The number of most likely outcomes considered to find the correct label.
+        num_correct: The number of predictions that were correct classified.
+        num_sample: The total number of predictions.
+    """
+
+    def __init__(self, k=1):
+        """Initialize the k, number of samples and correct predictions.
+
+        Args:
+            k: The number of most likely outcomes considered to find the correct label.
+        """
+        self.k = k
+        self.num_correct = 0
+        self.num_sample = 0
+
+    def update(self, preds, labels, sample_weight=None):
+        """Add the predictions and labels.
+
+        Args:
+            preds: The predictions.
+            labels: The labels corresponding to the predictions.
+            sample_weight: The sample weight.
+        """
+        preds, labels = TopKMetric._topk_shape_validate(preds, labels)
+
+        labels = labels.reshape([len(labels)])
+        with tf.Graph().as_default() as acc_graph:
+            topk = tf.nn.in_top_k(
+                predictions=tf.constant(preds, dtype=tf.float32), targets=tf.constant(labels, dtype=tf.int32), k=self.k
+            )
+            fp32_topk = tf.cast(topk, tf.float32)
+            correct_tensor = tf.reduce_sum(input_tensor=fp32_topk)
+
+            with tf.compat.v1.Session() as acc_sess:
+                correct = acc_sess.run(correct_tensor)
+
+        self.num_sample += len(labels)
+        self.num_correct += correct
+
+    def reset(self):
+        """Reset the number of samples and correct predictions."""
+        self.num_correct = 0
+        self.num_sample = 0
+
+    def result(self):
+        """Compute the top-k score.
+
+        Returns:
+            The top-k score.
+        """
+        if self.num_sample == 0:
+            logger.warning("Sample num during evaluation is 0.")
+            return 0
+        elif getattr(self, "_hvd", None) is not None:  # pragma: no cover
+            allgather_num_correct = sum(self._hvd.allgather_object(self.num_correct))
+            allgather_num_sample = sum(self._hvd.allgather_object(self.num_sample))
+            return allgather_num_correct / allgather_num_sample
+        return self.num_correct / self.num_sample
+
+    @staticmethod
+    def _topk_shape_validate(preds, labels):
+        # preds shape can be Nxclass_num or class_num(N=1 by default)
+        # it's more suitable for 'Accuracy' with preds shape Nx1(or 1) output from argmax
+        if isinstance(preds, int):
+            preds = [preds]
+            preds = np.array(preds)
+        elif isinstance(preds, np.ndarray):
+            preds = np.array(preds)
+        elif isinstance(preds, list):
+            preds = np.array(preds)
+            preds = preds.reshape((-1, preds.shape[-1]))
+
+        # consider labels just int value 1x1
+        if isinstance(labels, int):
+            labels = [labels]
+            labels = np.array(labels)
+        elif isinstance(labels, tuple):
+            labels = np.array([labels])
+            labels = labels.reshape((labels.shape[-1], -1))
+        elif isinstance(labels, list):
+            if isinstance(labels[0], int):
+                labels = np.array(labels)
+                labels = labels.reshape((labels.shape[0], 1))
+            elif isinstance(labels[0], tuple):
+                labels = np.array(labels)
+                labels = labels.reshape((labels.shape[-1], -1))
+            else:
+                labels = np.array(labels)
+        # labels most have 2 axis, 2 cases: N(or Nx1 sparse) or Nxclass_num(one-hot)
+        # only support 2 dimension one-shot labels
+        # or 1 dimension one-hot class_num will confuse with N
+
+        if len(preds.shape) == 1:
+            N = 1
+            class_num = preds.shape[0]
+            preds = preds.reshape([-1, class_num])
+        elif len(preds.shape) >= 2:
+            N = preds.shape[0]
+            preds = preds.reshape([N, -1])
+            class_num = preds.shape[1]
+
+        label_N = labels.shape[0]
+        assert label_N == N, "labels batch size should same with preds"
+        labels = labels.reshape([N, -1])
+        # one-hot labels will have 2 dimension not equal 1
+        if labels.shape[1] != 1:
+            labels = labels.argsort()[..., -1:]
+        return preds, labels
+    
+
+class TFDataLoader(object):  # pragma: no cover
+    """Tensorflow dataloader class.
+
+    In tensorflow1.x dataloader is coupled with the graph, but it also support feed_dict
+    method to do session run, this dataloader is designed to satisfy the usage of feed dict
+    in tf1.x. Although it's a general dataloader and can be used in MXNet and PyTorch.
+
+    Args:
+        dataset: obj. wrapper of needed data.
+        batch_size: int. batch size
+    """
+
+    def __init__(self, dataset, batch_size=1, last_batch="rollover"):
+        """Initialize `TFDataDataLoader` class."""
+        self.dataset = dataset
+        self.last_batch = last_batch
+        self.batch_size = batch_size
+        dataset = dataset.batch(batch_size)
+
+    def batch(self, batch_size, last_batch="rollover"):
+        """Dataset return data per batch."""
+        drop_last = False if last_batch == "rollover" else True
+        self.batch_size = batch_size
+        self.dataset = self.dataset.batch(batch_size, drop_last)
+
+    def __iter__(self):
+        """Iterate dataloader."""
+        return self._generate_dataloader(
+            self.dataset,
+            batch_size=self.batch_size,
+            last_batch=self.last_batch,
+        )
+
+    def _generate_dataloader(
+        self,
+        dataset,
+        batch_size=1,
+        last_batch="rollover",
+        collate_fn=None,
+        sampler=None,
+        batch_sampler=None,
+        num_workers=None,
+        pin_memory=None,
+        distributed=False,
+    ):
+        """Yield data."""
+        drop_last = False if last_batch == "rollover" else True
+
+        def check_dynamic_shape(element_spec):
+            if isinstance(element_spec, collections.abc.Sequence):
+                return any([check_dynamic_shape(ele) for ele in element_spec])
+            elif isinstance(element_spec, tf.TensorSpec):
+                return True if element_spec.shape.num_elements() is None else False
+            else:
+                raise ValueError("unrecognized element spec...")
+
+        def squeeze_output(output):
+            if isinstance(output, collections.abc.Sequence):
+                return [squeeze_output(ele) for ele in output]
+            elif isinstance(output, np.ndarray):
+                return np.squeeze(output, axis=0)
+            else:
+                raise ValueError("not supported output format....")
+
+        if tf.executing_eagerly():
+            index = 0
+            outputs = []
+            for iter_tensors in dataset:
+                samples = []
+                iter_inputs, iter_labels = iter_tensors[0], iter_tensors[1]
+                if isinstance(iter_inputs, tf.Tensor):
+                    samples.append(iter_inputs.numpy())
+                else:
+                    samples.append(tuple(iter_input.numpy() for iter_input in iter_inputs))
+                if isinstance(iter_labels, tf.Tensor):
+                    samples.append(iter_labels.numpy())
+                else:
+                    samples.append([np.array(l) for l in iter_labels])
+                index += 1
+                outputs.append(samples)
+                if index == batch_size:
+                    outputs = default_collate(outputs)
+                    yield outputs
+                    outputs = []
+                    index = 0
+            if len(outputs) > 0:
+                outputs = default_collate(outputs)
+                yield outputs
+        else:
+            try_single_batch = check_dynamic_shape(dataset.element_spec)
+            dataset = dataset.batch(1 if try_single_batch else batch_size, drop_last)
+            ds_iterator = tf.compat.v1.data.make_one_shot_iterator(dataset)
+            iter_tensors = ds_iterator.get_next()
+            data_config = tf.compat.v1.ConfigProto()
+            data_config.use_per_session_threads = 1
+            data_config.intra_op_parallelism_threads = 1
+            data_config.inter_op_parallelism_threads = 16
+            data_sess = tf.compat.v1.Session(config=data_config)
+            # pylint: disable=no-name-in-module
+            from tensorflow.python.framework.errors_impl import OutOfRangeError
+
+            while True:
+                if not try_single_batch:
+                    try:
+                        outputs = data_sess.run(iter_tensors)
+                        yield outputs
+                    except OutOfRangeError:
+                        data_sess.close()
+                        return
+                else:
+                    try:
+                        outputs = []
+                        for i in range(0, batch_size):
+                            outputs.append(squeeze_output(data_sess.run(iter_tensors)))
+                        outputs = default_collate(outputs)
+                        yield outputs
+                    except OutOfRangeError:
+                        if len(outputs) == 0:
+                            data_sess.close()
+                            return
+                        else:
+                            outputs = default_collate(outputs)
+                            yield outputs
+                            data_sess.close()
+                            return
diff --git a/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/main.py b/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/main.py
new file mode 100644
index 00000000000..0e8767eb0a9
--- /dev/null
+++ b/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/main.py
@@ -0,0 +1,141 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2018 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import time
+
+import numpy as np
+import tensorflow as tf
+
+from neural_compressor.utils import logger
+from data_process import (
+    ImageRecordDataset, 
+    ComposeTransform, 
+    BilinearImagenetTransform, 
+    TFDataLoader,
+    TopKMetric,
+    LabelShift,
+)
+
+tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
+
+flags = tf.compat.v1.flags
+FLAGS = flags.FLAGS
+
+## Required parameters
+flags.DEFINE_string(
+    'input_model', None, 'Run inference with specified keras model.')
+
+flags.DEFINE_string(
+    'output_model', None, 'The output quantized model.')
+
+flags.DEFINE_string(
+    'mode', 'performance', 'define benchmark mode for accuracy or performance')
+
+flags.DEFINE_bool(
+    'tune', False, 'whether to tune the model')
+
+flags.DEFINE_bool(
+    'benchmark', False, 'whether to benchmark the model')
+
+flags.DEFINE_string(
+    'calib_data', None, 'location of calibration dataset')
+
+flags.DEFINE_string(
+    'eval_data', None, 'location of evaluate dataset')
+
+flags.DEFINE_integer('batch_size', 32, 'batch_size')
+
+flags.DEFINE_integer(
+    'iters', 100, 'maximum iteration when evaluating performance')
+
+height = width = 224
+eval_dataset = ImageRecordDataset(root=FLAGS.eval_data, transform=ComposeTransform(transform_list= \
+                 [BilinearImagenetTransform(height=height, width=width)]))
+
+eval_dataloader = TFDataLoader(dataset=eval_dataset, batch_size=FLAGS.batch_size)
+
+if FLAGS.calib_data:
+    calib_dataset = ImageRecordDataset(root=FLAGS.calib_data, transform= \
+        ComposeTransform(transform_list= [BilinearImagenetTransform(height=height, width=width)]))
+    calib_dataloader = TFDataLoader(dataset=calib_dataset, batch_size=10)
+
+def evaluate(model):
+    """
+    Custom evaluate function to inference the model for specified metric on validation dataset.
+
+    Args:
+        model (tf.keras.Model): The input model will be the objection of tf.keras.Model.
+
+    Returns:
+        accuracy (float): evaluation result, the larger is better.
+    """
+    latency_list = []
+    metric = TopKMetric()
+    postprocess = LabelShift(label_shift=1)
+
+    def eval_func(dataloader, metric):
+        warmup = 5
+        iteration = None
+        if FLAGS.benchmark and FLAGS.mode == 'performance':
+            iteration = FLAGS.iters
+        for idx, (inputs, labels) in enumerate(dataloader):
+            start = time.time()
+            predictions = model.predict_on_batch(inputs)
+            end = time.time()
+            latency_list.append(end - start)
+            predictions, labels = postprocess((predictions, labels))
+            metric.update(predictions, labels)
+            if iteration and idx >= iteration:
+                break
+        latency = np.array(latency_list[warmup:]).mean() / eval_dataloader.batch_size
+        return latency
+
+    latency = eval_func(eval_dataloader, metric)
+    if FLAGS.benchmark:
+        logger.info("\n{} mode benchmark result:".format(FLAGS.mode))
+        for i, res in enumerate(latency_list):
+            logger.debug("Iteration {} result {}:".format(i, res))
+    if FLAGS.benchmark and FLAGS.mode == 'performance':
+        logger.info("Batch size = {}".format(eval_dataloader.batch_size))
+        logger.info("Latency: {:.3f} ms".format(latency * 1000))
+        logger.info("Throughput: {:.3f} images/sec".format(1. / latency))
+    acc = metric.result()
+    return acc
+
+def main(_):
+    if FLAGS.tune:
+        from neural_compressor.common import set_random_seed
+        from neural_compressor.tensorflow import StaticQuantConfig, quantize_model
+
+        set_random_seed(9527)
+        quant_config = StaticQuantConfig()
+        q_model = quantize_model(FLAGS.input_model, quant_config, calib_dataloader)
+        q_model.save(FLAGS.output_model)
+
+    if FLAGS.benchmark:
+        from neural_compressor.tensorflow import Model
+
+        inc_model = Model(FLAGS.input_model)
+        if FLAGS.mode == 'performance':
+            evaluate(inc_model.model)
+        else:
+            accuracy = evaluate(inc_model.model)
+            logger.info('Batch size = %d' % FLAGS.batch_size)
+            logger.info("Accuracy: %.5f" % accuracy)
+
+if __name__ == "__main__":
+    tf.compat.v1.app.run()
diff --git a/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/prepare_model.py b/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/prepare_model.py
new file mode 100644
index 00000000000..e31b3e83de0
--- /dev/null
+++ b/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/prepare_model.py
@@ -0,0 +1,35 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2022 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import argparse
+from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2
+def get_mobilenet_v2_model(saved_path):
+    model = MobileNetV2(weights='imagenet')
+    model.save(saved_path)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+    description='Export pretained keras model',
+    formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    parser.add_argument(
+        '--output_model',
+        type=str,
+        help='path to exported model file')
+
+    args = parser.parse_args()
+    get_mobilenet_v2_model(args.output_model)
diff --git a/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/requirements.txt b/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/requirements.txt
new file mode 100644
index 00000000000..8b7b47da969
--- /dev/null
+++ b/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/requirements.txt
@@ -0,0 +1,2 @@
+tensorflow>=2.11.1
+intel-extension-for-tensorflow[cpu]
diff --git a/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/run_benchmark.sh b/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/run_benchmark.sh
new file mode 100644
index 00000000000..43b1636c839
--- /dev/null
+++ b/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/run_benchmark.sh
@@ -0,0 +1,51 @@
+#!/bin/bash
+set -x
+
+function main {
+
+  init_params "$@"
+  run_benchmark
+
+}
+
+# init params
+function init_params {
+  batch_size=32
+  iters=100
+
+  for var in "$@"
+  do
+    case $var in
+      --input_model=*)
+          input_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --mode=*)
+          mode=$(echo $var |cut -f2 -d=)    
+      ;;
+      --dataset_location=*)
+          dataset_location=$(echo $var |cut -f2 -d=)    
+      ;;
+      --batch_size=*)
+          batch_size=$(echo $var |cut -f2 -d=)
+      ;;
+      --iters=*)
+          iters=$(echo $var |cut -f2 -d=)
+      ;;
+    esac
+  done
+
+}
+
+# run_tuning
+function run_benchmark {
+
+    python main.py \
+            --input_model ${input_model} \
+            --benchmark \
+            --mode ${mode} \
+            --eval_data ${dataset_location} \
+            --batch_size ${batch_size} \
+            --iters ${iters}
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/run_quant.sh b/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/run_quant.sh
new file mode 100644
index 00000000000..7e3ed727f71
--- /dev/null
+++ b/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/run_quant.sh
@@ -0,0 +1,40 @@
+#!/bin/bash
+set -x
+
+function main {
+  init_params "$@"
+  run_tuning
+
+}
+
+# init params
+function init_params {
+
+  for var in "$@"
+  do
+    case $var in
+      --input_model=*)
+          input_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --output_model=*)
+          output_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --dataset_location=*)
+          dataset_location=$(echo $var |cut -f2 -d=)
+      ;;
+    esac
+  done
+
+}
+
+# run_tuning
+function run_tuning {
+    python main.py \
+            --input_model ${input_model} \
+            --output_model ${output_model} \
+            --eval_data ${dataset_location} \
+            --calib_data ${dataset_location} \
+            --tune
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/keras/cv/prepare_dataset.sh b/examples/3.x_api/tensorflow/keras/cv/prepare_dataset.sh
new file mode 100644
index 00000000000..4aad5d69a3f
--- /dev/null
+++ b/examples/3.x_api/tensorflow/keras/cv/prepare_dataset.sh
@@ -0,0 +1,71 @@
+#!/bin/bash
+# set -x
+
+OUTPUT_DIR="./data"
+SUBSET="validation"
+SHARDS=1
+
+help()
+{
+    cat <<- EOF
+    Desc: Convert prepared raw imagnet dataset to tfrecord
+    -h --help                   help info
+    --output_dir                Output data directory
+                                default: './data'
+    --raw_dir                   Raw data directory
+    --shards                    Number of shards in TFRecord files.
+                                default: '1'
+    --subset                    Subset of imagenet, can be validation/train.
+                                default: 'validation'
+EOF
+    exit 0
+}
+
+function main {
+    init_params "$@"
+    convert_dataset
+}
+
+# init params
+function init_params {
+    for var in "$@"
+    do
+        case $var in
+            --output_dir=*)
+                OUTPUT_DIR=$(echo $var |cut -f2 -d=)
+            ;;
+            --raw_dir=*)
+                RAW_DIR=$(echo $var |cut -f2 -d=)
+            ;;
+            --shards=*)
+                SHARDS=$(echo $var |cut -f2 -d=)
+            ;;
+            --subset=*)
+                SUBSET=$(echo $var |cut -f2 -d=)
+            ;;
+            -h|--help) help
+            ;;
+            *)
+            echo "Error: No such parameter: ${var}"
+            exit 1
+            ;;
+        esac
+    done
+}
+
+# convert dataset
+function convert_dataset {
+    if [ ! -d ${OUTPUT_DIR} ]; then
+        mkdir ${OUTPUT_DIR}
+    fi
+    python imagenet_prepare/build_imagenet_data.py \
+        --imagenet_metadata_file "imagenet_prepare/imagenet_metadata.txt" \
+        --labels_file "imagenet_prepare/imagenet_lsvrc_2015_synsets.txt" \
+        --output_directory ${OUTPUT_DIR} \
+        --subset ${SUBSET} \
+        --raw_directory ${RAW_DIR} \
+        --shards ${SHARDS} 
+}
+
+main "$@"
+
diff --git a/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/README.md b/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/README.md
new file mode 100644
index 00000000000..54ab588faf4
--- /dev/null
+++ b/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/README.md
@@ -0,0 +1,76 @@
+Step-by-Step
+============
+
+This document is used to enable Tensorflow Keras models using Intel® Neural Compressor.
+This example can run on Intel CPUs and GPUs.
+
+
+# Prerequisite
+
+## 1. Environment
+
+### Installation
+```shell
+# Install Intel® Neural Compressor
+pip install neural-compressor
+```
+
+### Install Requirements
+The Tensorflow and intel-extension-for-tensorflow is mandatory to be installed to run this example.
+The Intel Extension for Tensorflow for Intel CPUs is installed as default.
+```shell
+pip install -r requirements.txt
+```
+> Note: Validated TensorFlow [Version](/docs/source/installation_guide.md#validated-software-environment).
+
+## 2. Prepare Pretrained model
+
+The pretrained model is provided by [Keras Applications](https://keras.io/api/applications/). prepare the model, Run as follow: 
+ ```
+python prepare_model.py --output_model=/path/to/model
+ ```
+`--output_model ` the model should be saved as SavedModel format or H5 format.
+
+## 3. Prepare Dataset
+
+  TensorFlow [models](https://github.com/tensorflow/models) repo provides [scripts and instructions](https://github.com/tensorflow/models/tree/master/research/slim#an-automated-script-for-processing-imagenet-data) to download, process and convert the ImageNet dataset to the TF records format.
+  We also prepared related scripts in `imagenet_prepare` directory. To download the raw images, the user must create an account with image-net.org. If you have downloaded the raw data and preprocessed the validation data by moving the images into the appropriate sub-directory based on the label (synset) of the image. we can use below command ro convert it to tf records format.
+
+  ```shell
+  cd examples/keras/image_recognition/
+  # convert validation subset
+  bash prepare_dataset.sh --output_dir=/resnetv2_50/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
+  # convert train subset
+  bash prepare_dataset.sh --output_dir=/resnetv2_50/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/train/ --subset=train
+  cd resnetv2_50/quantization/ptq
+  ```
+> **Note**: 
+> The raw ImageNet dataset resides in JPEG files should be in the following directory structure. Taking validation set as an example:<br>
+> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/PATH/TO/img_raw/val/n01440764/ILSVRC2012_val_00000293.JPEG<br>
+> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/PATH/TO/img_raw/val/n01440764/ILSVRC2012_val_00000543.JPEG<br>
+> where 'n01440764' is the unique synset label associated with these images.
+
+# Run Command
+
+## Quantization Config
+The Quantization Config class has default parameters setting for running on Intel CPUs. If running this example on Intel GPUs, the 'backend' parameter should be set to 'itex' and the 'device' parameter should be set to 'gpu'.
+
+```
+config = PostTrainingQuantConfig(
+    device="gpu",
+    backend="itex",
+    ...
+    )
+```
+
+## Quantization
+  ```shell
+  bash run_quant.sh --input_model=./resnetv2_50_keras/ --output_model=./result --dataset_location=/path/to/evaluation/dataset
+  ```
+
+## Benchmark
+  ```shell
+  bash run_benchmark.sh --input_model=./result --mode=accuracy --dataset_location=/path/to/evaluation/dataset --batch_size=32
+  bash run_benchmark.sh --input_model=./result --mode=performance --dataset_location=/path/to/evaluation/dataset --batch_size=1
+  ```
+
diff --git a/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/data_process.py b/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/data_process.py
new file mode 100644
index 00000000000..b8cd01593c6
--- /dev/null
+++ b/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/data_process.py
@@ -0,0 +1,543 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2024 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import os
+import collections
+
+import numpy as np
+import tensorflow as tf
+
+from abc import abstractmethod
+from neural_compressor.common import logger
+from neural_compressor.tensorflow.utils.data import default_collate
+
+class ParseDecodeImagenet:
+    """Parse features in Example proto.
+
+    Returns:
+        tuple of parsed image and label
+    """
+
+    def __call__(self, sample):
+        """Parse features in example."""
+        # Dense features in Example proto.
+        feature_map = {
+            "image/encoded": tf.io.FixedLenFeature([], dtype=tf.string, default_value=""),
+            "image/class/label": tf.io.FixedLenFeature([1], dtype=tf.int64, default_value=-1),
+        }
+
+        sparse_float32 = tf.io.VarLenFeature(dtype=tf.float32)
+        # Sparse features in Example proto.
+        feature_map.update(
+            {
+                k: sparse_float32
+                for k in [
+                    "image/object/bbox/xmin",
+                    "image/object/bbox/ymin",
+                    "image/object/bbox/xmax",
+                    "image/object/bbox/ymax",
+                ]
+            }
+        )
+
+        features = tf.io.parse_single_example(serialized=sample, features=feature_map)
+        label = tf.cast(features["image/class/label"], dtype=tf.int32)
+        image = features["image/encoded"]
+        image = tf.image.decode_jpeg(image, channels=3, fancy_upscaling=False, dct_method="INTEGER_FAST")
+        return (image, label)
+
+
+class BilinearImagenetTransform(object):
+    """Combination of a series of transforms which is applicable to images in Imagenet.
+
+    Args:
+        height: Height of the result
+        width:Width of the result
+        central_fraction(float, default=0.875):fraction of size to crop
+        mean_value(list, default=[0.0,0.0,0.0]):means for each channel
+        scale(float, default=1.0):std value
+
+    Returns:
+        tuple of processed image and label
+    """
+
+    def __init__(self, height, width, central_fraction=0.875, mean_value=[0.0, 0.0, 0.0], scale=1.0):
+        """Initialize `BilinearImagenetTransform` class."""
+        self.height = height
+        self.width = width
+        self.mean_value = mean_value
+        self.scale = scale
+        self.central_fraction = central_fraction
+
+    # sample is (images, labels)
+    def __call__(self, sample):
+        """Convert `BilinearImagenetTransform` feature."""
+        image, label = sample
+        if image.dtype is not tf.float32:
+            image = tf.image.convert_image_dtype(image, dtype=tf.float32)
+        # Crop the central region of the image containing 87.5% area of the original image.
+        if self.central_fraction:
+            image = tf.image.central_crop(image, central_fraction=self.central_fraction)
+
+        if self.height and self.width:
+            # Resize the image to the specified height and width.
+            image = tf.expand_dims(image, 0)
+            image = tf.image.resize(image, [self.height, self.width], method=tf.image.ResizeMethod.BILINEAR)
+            image = tf.squeeze(image, [0])
+
+        image = tf.subtract(image, 0.5)
+        image = tf.multiply(image, 2.0)
+        means = tf.broadcast_to(self.mean_value, tf.shape(input=image))
+        image = (image - means) * self.scale
+        return (image, label)
+
+
+class ComposeTransform(object):
+    """Composes several transforms together.
+
+    Args:
+        transform_list (list of Transform objects):  list of transforms to compose
+
+    Returns:
+        sample (tuple): tuple of processed image and label
+    """
+
+    def __init__(self, transform_list):
+        """Initialize `ComposeTransform` class."""
+        self.transform_list = transform_list
+
+    def __call__(self, sample):
+        """Call transforms in transform_list."""
+        for transform in self.transform_list:
+            sample = transform(sample)
+        return sample
+
+
+class ShiftRescale(object):
+    """Label shift by 1 and rescale.
+
+    Returns:
+        tuple of processed image and label
+    """
+
+    def __call__(self, sample):
+        image, label = sample
+        label -= 1
+        image = (image - 127.5) / 127.5
+        return (image, label)
+
+
+class LabelShift(object):
+    """Convert label to label - label_shift.
+
+    Args:
+        label_shift(int, default=0): number of label shift
+
+    Returns:
+        tuple of processed image and label
+    """
+
+    def __init__(self, label_shift=0):
+        """Initialize `LabelShift` class."""
+        self.label_shift = label_shift
+
+    def __call__(self, sample):
+        """Convert label to label_shift."""
+        images, labels = sample
+        if isinstance(labels, np.ndarray):
+            labels = labels - self.label_shift
+        elif isinstance(labels, list):
+            if isinstance(labels[0], tuple):
+                labels = [tuple(np.array(label) - self.label_shift) for label in labels]
+            elif isinstance(labels[0], np.ndarray):
+                labels = [label - self.label_shift for label in labels]
+            else:
+                labels = np.array(labels) - self.label_shift
+                labels = labels.tolist()
+        else:
+            labels = np.array(labels) - self.label_shift
+        return images, labels
+
+
+class ImageRecordDataset(object):
+    """Tensorflow imageNet database in tf record format.
+
+    Please arrange data in this way:
+        root/validation-000-of-100
+        root/validation-001-of-100
+        ...
+        root/validation-099-of-100
+    The file name needs to follow this pattern: '* - * -of- *'
+
+    Args: root (str): Root directory of dataset.
+          transform (transform object, default=None):  transform to process input data.
+          filter (Filter objects, default=None): filter out examples according
+                                                 to specific conditions.
+    """
+
+    """Configuration for Imagenet dataset."""
+
+    def __new__(cls, root, transform=None, filter=None):
+        """Build a new object of TensorflowImageRecord class."""
+        from tensorflow.python.platform import gfile  # pylint: disable=no-name-in-module
+
+        glob_pattern = os.path.join(root, "*-*-of-*")
+        file_names = gfile.Glob(glob_pattern)
+        if not file_names:
+            raise ValueError("Found no files in --root matching: {}".format(glob_pattern))
+
+        # pylint: disable=no-name-in-module
+        from tensorflow.python.data.experimental import parallel_interleave
+
+        ds = tf.data.TFRecordDataset.list_files(file_names, shuffle=False)
+        ds = ds.apply(parallel_interleave(tf.data.TFRecordDataset, cycle_length=len(file_names)))
+
+        if transform is not None:
+            transform.transform_list.insert(0, ParseDecodeImagenet())
+        else:
+            transform = ParseDecodeImagenet()
+        ds = ds.map(transform, num_parallel_calls=None)
+        ds = ds.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)  # this number can be tuned
+        return ds
+
+
+class BaseMetric(object):
+    """The base class of Metric."""
+
+    def __init__(self, metric, single_output=False, hvd=None):
+        """Initialize the basic metric.
+
+        Args:
+            metric: The metric class.
+            single_output: Whether the output is single or not, defaults to False.
+            hvd: The Horovod class for distributed training, defaults to None.
+        """
+        self._metric_cls = metric
+        self._single_output = single_output
+        self._hvd = hvd
+
+    def __call__(self, *args, **kwargs):
+        """Evaluate the model predictions, and the reference.
+
+        Returns:
+            The class itself.
+        """
+        self._metric = self._metric_cls(*args, **kwargs)
+        return self
+
+    @abstractmethod
+    def update(self, preds, labels=None, sample_weight=None):
+        """Update the state that need to be evaluated.
+
+        Args:
+            preds: The prediction result.
+            labels: The reference. Defaults to None.
+            sample_weight: The sampling weight. Defaults to None.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def reset(self):
+        """Clear the predictions and labels.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def result(self):
+        """Evaluate the difference between predictions and labels.
+
+        Raises:
+            NotImplementedError: The method should be implemented by subclass.
+        """
+        raise NotImplementedError
+
+    @property
+    def metric(self):
+        """Return its metric class.
+
+        Returns:
+            The metric class.
+        """
+        return self._metric_cls
+
+    @property
+    def hvd(self):
+        """Return its hvd class.
+
+        Returns:
+            The hvd class.
+        """
+        return self._hvd
+
+    @hvd.setter
+    def hvd(self, hvd):
+        """Set its hvd.
+
+        Args:
+            hvd: The Horovod class for distributed training.
+        """
+        self._hvd = hvd
+
+
+class TopKMetric(BaseMetric):
+    """Compute Top-k Accuracy classification score for Tensorflow model.
+
+    This metric computes the number of times where the correct label is among
+    the top k labels predicted.
+
+    Attributes:
+        k (int): The number of most likely outcomes considered to find the correct label.
+        num_correct: The number of predictions that were correct classified.
+        num_sample: The total number of predictions.
+    """
+
+    def __init__(self, k=1):
+        """Initialize the k, number of samples and correct predictions.
+
+        Args:
+            k: The number of most likely outcomes considered to find the correct label.
+        """
+        self.k = k
+        self.num_correct = 0
+        self.num_sample = 0
+
+    def update(self, preds, labels, sample_weight=None):
+        """Add the predictions and labels.
+
+        Args:
+            preds: The predictions.
+            labels: The labels corresponding to the predictions.
+            sample_weight: The sample weight.
+        """
+        preds, labels = TopKMetric._topk_shape_validate(preds, labels)
+
+        labels = labels.reshape([len(labels)])
+        with tf.Graph().as_default() as acc_graph:
+            topk = tf.nn.in_top_k(
+                predictions=tf.constant(preds, dtype=tf.float32), targets=tf.constant(labels, dtype=tf.int32), k=self.k
+            )
+            fp32_topk = tf.cast(topk, tf.float32)
+            correct_tensor = tf.reduce_sum(input_tensor=fp32_topk)
+
+            with tf.compat.v1.Session() as acc_sess:
+                correct = acc_sess.run(correct_tensor)
+
+        self.num_sample += len(labels)
+        self.num_correct += correct
+
+    def reset(self):
+        """Reset the number of samples and correct predictions."""
+        self.num_correct = 0
+        self.num_sample = 0
+
+    def result(self):
+        """Compute the top-k score.
+
+        Returns:
+            The top-k score.
+        """
+        if self.num_sample == 0:
+            logger.warning("Sample num during evaluation is 0.")
+            return 0
+        elif getattr(self, "_hvd", None) is not None:  # pragma: no cover
+            allgather_num_correct = sum(self._hvd.allgather_object(self.num_correct))
+            allgather_num_sample = sum(self._hvd.allgather_object(self.num_sample))
+            return allgather_num_correct / allgather_num_sample
+        return self.num_correct / self.num_sample
+
+    @staticmethod
+    def _topk_shape_validate(preds, labels):
+        # preds shape can be Nxclass_num or class_num(N=1 by default)
+        # it's more suitable for 'Accuracy' with preds shape Nx1(or 1) output from argmax
+        if isinstance(preds, int):
+            preds = [preds]
+            preds = np.array(preds)
+        elif isinstance(preds, np.ndarray):
+            preds = np.array(preds)
+        elif isinstance(preds, list):
+            preds = np.array(preds)
+            preds = preds.reshape((-1, preds.shape[-1]))
+
+        # consider labels just int value 1x1
+        if isinstance(labels, int):
+            labels = [labels]
+            labels = np.array(labels)
+        elif isinstance(labels, tuple):
+            labels = np.array([labels])
+            labels = labels.reshape((labels.shape[-1], -1))
+        elif isinstance(labels, list):
+            if isinstance(labels[0], int):
+                labels = np.array(labels)
+                labels = labels.reshape((labels.shape[0], 1))
+            elif isinstance(labels[0], tuple):
+                labels = np.array(labels)
+                labels = labels.reshape((labels.shape[-1], -1))
+            else:
+                labels = np.array(labels)
+        # labels most have 2 axis, 2 cases: N(or Nx1 sparse) or Nxclass_num(one-hot)
+        # only support 2 dimension one-shot labels
+        # or 1 dimension one-hot class_num will confuse with N
+
+        if len(preds.shape) == 1:
+            N = 1
+            class_num = preds.shape[0]
+            preds = preds.reshape([-1, class_num])
+        elif len(preds.shape) >= 2:
+            N = preds.shape[0]
+            preds = preds.reshape([N, -1])
+            class_num = preds.shape[1]
+
+        label_N = labels.shape[0]
+        assert label_N == N, "labels batch size should same with preds"
+        labels = labels.reshape([N, -1])
+        # one-hot labels will have 2 dimension not equal 1
+        if labels.shape[1] != 1:
+            labels = labels.argsort()[..., -1:]
+        return preds, labels
+    
+
+class TFDataLoader(object):  # pragma: no cover
+    """Tensorflow dataloader class.
+
+    In tensorflow1.x dataloader is coupled with the graph, but it also support feed_dict
+    method to do session run, this dataloader is designed to satisfy the usage of feed dict
+    in tf1.x. Although it's a general dataloader and can be used in MXNet and PyTorch.
+
+    Args:
+        dataset: obj. wrapper of needed data.
+        batch_size: int. batch size
+    """
+
+    def __init__(self, dataset, batch_size=1, last_batch="rollover"):
+        """Initialize `TFDataDataLoader` class."""
+        self.dataset = dataset
+        self.last_batch = last_batch
+        self.batch_size = batch_size
+        dataset = dataset.batch(batch_size)
+
+    def batch(self, batch_size, last_batch="rollover"):
+        """Dataset return data per batch."""
+        drop_last = False if last_batch == "rollover" else True
+        self.batch_size = batch_size
+        self.dataset = self.dataset.batch(batch_size, drop_last)
+
+    def __iter__(self):
+        """Iterate dataloader."""
+        return self._generate_dataloader(
+            self.dataset,
+            batch_size=self.batch_size,
+            last_batch=self.last_batch,
+        )
+
+    def _generate_dataloader(
+        self,
+        dataset,
+        batch_size=1,
+        last_batch="rollover",
+        collate_fn=None,
+        sampler=None,
+        batch_sampler=None,
+        num_workers=None,
+        pin_memory=None,
+        distributed=False,
+    ):
+        """Yield data."""
+        drop_last = False if last_batch == "rollover" else True
+
+        def check_dynamic_shape(element_spec):
+            if isinstance(element_spec, collections.abc.Sequence):
+                return any([check_dynamic_shape(ele) for ele in element_spec])
+            elif isinstance(element_spec, tf.TensorSpec):
+                return True if element_spec.shape.num_elements() is None else False
+            else:
+                raise ValueError("unrecognized element spec...")
+
+        def squeeze_output(output):
+            if isinstance(output, collections.abc.Sequence):
+                return [squeeze_output(ele) for ele in output]
+            elif isinstance(output, np.ndarray):
+                return np.squeeze(output, axis=0)
+            else:
+                raise ValueError("not supported output format....")
+
+        if tf.executing_eagerly():
+            index = 0
+            outputs = []
+            for iter_tensors in dataset:
+                samples = []
+                iter_inputs, iter_labels = iter_tensors[0], iter_tensors[1]
+                if isinstance(iter_inputs, tf.Tensor):
+                    samples.append(iter_inputs.numpy())
+                else:
+                    samples.append(tuple(iter_input.numpy() for iter_input in iter_inputs))
+                if isinstance(iter_labels, tf.Tensor):
+                    samples.append(iter_labels.numpy())
+                else:
+                    samples.append([np.array(l) for l in iter_labels])
+                index += 1
+                outputs.append(samples)
+                if index == batch_size:
+                    outputs = default_collate(outputs)
+                    yield outputs
+                    outputs = []
+                    index = 0
+            if len(outputs) > 0:
+                outputs = default_collate(outputs)
+                yield outputs
+        else:
+            try_single_batch = check_dynamic_shape(dataset.element_spec)
+            dataset = dataset.batch(1 if try_single_batch else batch_size, drop_last)
+            ds_iterator = tf.compat.v1.data.make_one_shot_iterator(dataset)
+            iter_tensors = ds_iterator.get_next()
+            data_config = tf.compat.v1.ConfigProto()
+            data_config.use_per_session_threads = 1
+            data_config.intra_op_parallelism_threads = 1
+            data_config.inter_op_parallelism_threads = 16
+            data_sess = tf.compat.v1.Session(config=data_config)
+            # pylint: disable=no-name-in-module
+            from tensorflow.python.framework.errors_impl import OutOfRangeError
+
+            while True:
+                if not try_single_batch:
+                    try:
+                        outputs = data_sess.run(iter_tensors)
+                        yield outputs
+                    except OutOfRangeError:
+                        data_sess.close()
+                        return
+                else:
+                    try:
+                        outputs = []
+                        for i in range(0, batch_size):
+                            outputs.append(squeeze_output(data_sess.run(iter_tensors)))
+                        outputs = default_collate(outputs)
+                        yield outputs
+                    except OutOfRangeError:
+                        if len(outputs) == 0:
+                            data_sess.close()
+                            return
+                        else:
+                            outputs = default_collate(outputs)
+                            yield outputs
+                            data_sess.close()
+                            return
diff --git a/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/main.py b/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/main.py
new file mode 100644
index 00000000000..97254a194c1
--- /dev/null
+++ b/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/main.py
@@ -0,0 +1,141 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2018 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import time
+
+import numpy as np
+import tensorflow as tf
+
+from neural_compressor.utils import logger
+from data_process import (
+    ImageRecordDataset, 
+    ComposeTransform, 
+    BilinearImagenetTransform, 
+    TFDataLoader,
+    TopKMetric,
+    LabelShift
+)
+
+tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
+
+flags = tf.compat.v1.flags
+FLAGS = flags.FLAGS
+
+## Required parameters
+flags.DEFINE_string(
+    'input_model', None, 'Run inference with specified keras model.')
+
+flags.DEFINE_string(
+    'output_model', None, 'The output quantized model.')
+
+flags.DEFINE_string(
+    'mode', 'performance', 'define benchmark mode for accuracy or performance')
+
+flags.DEFINE_bool(
+    'tune', False, 'whether to tune the model')
+
+flags.DEFINE_bool(
+    'benchmark', False, 'whether to benchmark the model')
+
+flags.DEFINE_string(
+    'calib_data', None, 'location of calibration dataset')
+
+flags.DEFINE_string(
+    'eval_data', None, 'location of evaluate dataset')
+
+flags.DEFINE_integer('batch_size', 32, 'batch_size')
+
+flags.DEFINE_integer(
+    'iters', 100, 'maximum iteration when evaluating performance')
+
+height = width = 224
+eval_dataset = ImageRecordDataset(root=FLAGS.eval_data, transform=ComposeTransform(transform_list= \
+                 [BilinearImagenetTransform(height=height, width=width)]))
+
+eval_dataloader = TFDataLoader(dataset=eval_dataset, batch_size=FLAGS.batch_size)
+
+if FLAGS.calib_data:
+    calib_dataset = ImageRecordDataset(root=FLAGS.calib_data, transform= \
+        ComposeTransform(transform_list= [BilinearImagenetTransform(height=height, width=width)]))
+    calib_dataloader = TFDataLoader(dataset=calib_dataset, batch_size=10)
+
+def evaluate(model):
+    """
+    Custom evaluate function to inference the model for specified metric on validation dataset.
+
+    Args:
+       model (tf.keras.Model): The input model will be the objection of tf.keras.Model.
+
+    Returns:
+        accuracy (float): evaluation result, the larger is better.
+    """
+    latency_list = []
+    metric = TopKMetric()
+    postprocess = LabelShift(label_shift=1)
+
+    def eval_func(dataloader, metric):
+        warmup = 5
+        iteration = None
+        if FLAGS.benchmark and FLAGS.mode == 'performance':
+            iteration = FLAGS.iters
+        for idx, (inputs, labels) in enumerate(dataloader):
+            start = time.time()
+            predictions = model.predict_on_batch(inputs)
+            end = time.time()
+            latency_list.append(end - start)
+            predictions, labels = postprocess((predictions, labels))
+            metric.update(predictions, labels)
+            if iteration and idx >= iteration:
+                break
+        latency = np.array(latency_list[warmup:]).mean() / eval_dataloader.batch_size
+        return latency
+
+    latency = eval_func(eval_dataloader, metric)
+    if FLAGS.benchmark:
+        logger.info("\n{} mode benchmark result:".format(FLAGS.mode))
+        for i, res in enumerate(latency_list):
+            logger.debug("Iteration {} result {}:".format(i, res))
+    if FLAGS.benchmark and FLAGS.mode == 'performance':
+        logger.info("Batch size = {}".format(eval_dataloader.batch_size))
+        logger.info("Latency: {:.3f} ms".format(latency * 1000))
+        logger.info("Throughput: {:.3f} images/sec".format(1. / latency))
+    acc = metric.result()
+    return acc
+
+def main(_):
+    if FLAGS.tune:
+        from neural_compressor.common import set_random_seed
+        from neural_compressor.tensorflow import StaticQuantConfig, quantize_model
+
+        set_random_seed(9527)
+        quant_config = StaticQuantConfig()
+        q_model = quantize_model(FLAGS.input_model, quant_config, calib_dataloader)
+        q_model.save(FLAGS.output_model)
+
+    if FLAGS.benchmark:
+        from neural_compressor.tensorflow import Model
+
+        inc_model = Model(FLAGS.input_model)
+        if FLAGS.mode == 'performance':
+            evaluate(inc_model.model)
+        else:
+            accuracy = evaluate(inc_model.model)
+            logger.info('Batch size = %d' % FLAGS.batch_size)
+            logger.info("Accuracy: %.5f" % accuracy)
+
+if __name__ == "__main__":
+    tf.compat.v1.app.run()
diff --git a/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/prepare_model.py b/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/prepare_model.py
new file mode 100644
index 00000000000..f8cd505f965
--- /dev/null
+++ b/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/prepare_model.py
@@ -0,0 +1,35 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2022 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import argparse
+import tensorflow as tf
+def get_resnet50_v2_model(saved_path):
+    model = tf.keras.applications.ResNet50V2(weights='imagenet')
+    model.save(saved_path)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+    description='Export pretained keras model',
+    formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    parser.add_argument(
+        '--output_model',
+        type=str,
+        help='path to exported model file')
+
+    args = parser.parse_args()
+    get_resnet50_v2_model(args.output_model)
diff --git a/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/requirements.txt b/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/requirements.txt
new file mode 100644
index 00000000000..8b7b47da969
--- /dev/null
+++ b/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/requirements.txt
@@ -0,0 +1,2 @@
+tensorflow>=2.11.1
+intel-extension-for-tensorflow[cpu]
diff --git a/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/run_benchmark.sh b/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/run_benchmark.sh
new file mode 100644
index 00000000000..d464b019f8e
--- /dev/null
+++ b/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/run_benchmark.sh
@@ -0,0 +1,50 @@
+#!/bin/bash
+set -x
+
+function main {
+
+  init_params "$@"
+  run_benchmark
+
+}
+
+# init params
+function init_params {
+  batch_size=32
+  iters=100
+
+  for var in "$@"
+  do
+    case $var in
+      --input_model=*)
+          input_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --mode=*)
+          mode=$(echo $var |cut -f2 -d=)    
+      ;;
+      --dataset_location=*)
+          dataset_location=$(echo $var |cut -f2 -d=)    
+      ;;
+      --batch_size=*)
+          batch_size=$(echo $var |cut -f2 -d=)
+      ;;
+      --iters=*)
+          iters=$(echo $var |cut -f2 -d=)
+    esac
+  done
+
+}
+
+# run_tuning
+function run_benchmark {
+
+    python main.py \
+            --input_model ${input_model} \
+            --benchmark \
+            --mode ${mode} \
+            --eval_data ${dataset_location} \
+            --batch_size ${batch_size} \
+            --iters ${iters}
+}
+
+main "$@"
diff --git a/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/run_quant.sh b/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/run_quant.sh
new file mode 100644
index 00000000000..7e3ed727f71
--- /dev/null
+++ b/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/run_quant.sh
@@ -0,0 +1,40 @@
+#!/bin/bash
+set -x
+
+function main {
+  init_params "$@"
+  run_tuning
+
+}
+
+# init params
+function init_params {
+
+  for var in "$@"
+  do
+    case $var in
+      --input_model=*)
+          input_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --output_model=*)
+          output_model=$(echo $var |cut -f2 -d=)
+      ;;
+      --dataset_location=*)
+          dataset_location=$(echo $var |cut -f2 -d=)
+      ;;
+    esac
+  done
+
+}
+
+# run_tuning
+function run_tuning {
+    python main.py \
+            --input_model ${input_model} \
+            --output_model ${output_model} \
+            --eval_data ${dataset_location} \
+            --calib_data ${dataset_location} \
+            --tune
+}
+
+main "$@"

From e82746f8fe473ad488b7f4e7343d4eabef1b5707 Mon Sep 17 00:00:00 2001
From: zehao-intel <zehao.huang@intel.com>
Date: Thu, 11 Jul 2024 14:30:14 +0800
Subject: [PATCH 03/14] fix import issues

Signed-off-by: zehao-intel <zehao.huang@intel.com>
---
 .../tensorflow/cv/resnet_v2_50/quantization/ptq/main.py   | 1 -
 .../nlp/transformer_lt/quantization/ptq/main.py           | 4 +---
 .../faster_rcnn_resnet50/quantization/ptq/main.py         | 2 +-
 .../mask_rcnn_inception_v2/quantization/ptq/main.py       | 2 +-
 .../ssd_mobilenet_v1/quantization/ptq/main.py             | 6 +++---
 .../wide_deep_large_ds/quantization/ptq/main.py           | 8 ++++----
 6 files changed, 10 insertions(+), 13 deletions(-)

diff --git a/examples/3.x_api/tensorflow/cv/resnet_v2_50/quantization/ptq/main.py b/examples/3.x_api/tensorflow/cv/resnet_v2_50/quantization/ptq/main.py
index 3b9595476e6..bb82476fced 100644
--- a/examples/3.x_api/tensorflow/cv/resnet_v2_50/quantization/ptq/main.py
+++ b/examples/3.x_api/tensorflow/cv/resnet_v2_50/quantization/ptq/main.py
@@ -118,7 +118,6 @@ def run(self):
             q_model.save(args.output_graph)
 
         if args.benchmark:
-            dataloader = create_dataloader('tensorflow', dataloader_args)
             dataset = ImageRecordDataset(
                 root=args.dataset_location, 
                 transform=ComposeTransform(transform_list= [
diff --git a/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/main.py b/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/main.py
index d4d1ff54055..99ac63d2c02 100644
--- a/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/main.py
+++ b/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/main.py
@@ -143,9 +143,7 @@ def eval_func(infer_graph, iteration=-1):
         'model/Transformer/strided_slice_19:0')
 
     ds = Dataset(FLAGS.inputs_file, FLAGS.reference_file, FLAGS.vocab_file)
-    dataloader = DataLoader(framework='tensorflow', dataset=ds,
-                                batch_size=FLAGS.batch_size, collate_fn=collate_fn)
-
+    dataloader = BaseDataLoader(dataset=ds, batch_size=FLAGS.batch_size, collate_fn=collate_fn)
     config = tf.compat.v1.ConfigProto()
     config.use_per_session_threads = 1
     config.inter_op_parallelism_threads = 1
diff --git a/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/main.py b/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/main.py
index 277028064dc..2493297e476 100644
--- a/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/main.py
+++ b/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/main.py
@@ -16,13 +16,13 @@
 # limitations under the License.
 #
 #
+from __future__ import division
 
 import time
 
 import numpy as np
 import tensorflow as tf
 
-from __future__ import division
 from argparse import ArgumentParser
 from data_process import(
     COCOmAPv2,
diff --git a/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/main.py b/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/main.py
index 7751c5dadc6..7031a257521 100644
--- a/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/main.py
+++ b/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/main.py
@@ -16,13 +16,13 @@
 # limitations under the License.
 #
 #
+from __future__ import division
 
 import time
 
 import numpy as np
 import tensorflow as tf
 
-from __future__ import division
 from argparse import ArgumentParser
 from data_process import(
     COCOmAPv2,
diff --git a/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/main.py b/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/main.py
index b217f63a3ec..cd9f943d374 100644
--- a/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/main.py
+++ b/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/main.py
@@ -16,13 +16,13 @@
 # limitations under the License.
 #
 #
+from __future__ import division
 
 import time
 
 import numpy as np
 import tensorflow as tf
 
-from __future__ import division
 from argparse import ArgumentParser
 from data_process import(
     COCOmAPv2,
@@ -93,7 +93,7 @@ def eval_func(dataloader):
 
     eval_dataset = COCORecordDataset(root=args.dataset_location, filter=None, \
             transform=ComposeTransform(transform_list=[ResizeTFTransform(size=300)]))
-    eval_dataloader=TFDataLoader(framework='tensorflow', dataset=eval_dataset, batch_size=args.batch_size)
+    eval_dataloader=TFDataLoader(dataset=eval_dataset, batch_size=args.batch_size)
     latency = eval_func(eval_dataloader)
     if args.benchmark and args.mode == 'performance':
         print("Batch size = {}".format(args.batch_size))
@@ -105,7 +105,7 @@ def eval_func(dataloader):
 def main(_):
     calib_dataset = COCORecordDataset(root=args.dataset_location, filter=None, \
             transform=ComposeTransform(transform_list=[ResizeTFTransform(size=300)]))
-    calib_dataloader = TFDataLoader(framework='tensorflow', dataset=calib_dataset, batch_size=args.batch_size)
+    calib_dataloader = TFDataLoader(dataset=calib_dataset, batch_size=args.batch_size)
 
     if args.tune:
         from neural_compressor.tensorflow import StaticQuantConfig, quantize_model, Model
diff --git a/examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/main.py b/examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/main.py
index 19ee3647f7e..7ea26e60ff1 100644
--- a/examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/main.py
+++ b/examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/main.py
@@ -18,6 +18,10 @@
 
 #
 
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 import sys
 import os
 import numpy as np
@@ -30,10 +34,6 @@
 
 import tensorflow as tf
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
 from tensorflow.python.framework import ops
 from tensorflow.core.framework import graph_pb2
 from google.protobuf import text_format

From 8c9076670c7c15c3b26a0111c9637e5f7b11275c Mon Sep 17 00:00:00 2001
From: zehao-intel <zehao.huang@intel.com>
Date: Thu, 11 Jul 2024 16:16:29 +0800
Subject: [PATCH 04/14] fix config

Signed-off-by: zehao-intel <zehao.huang@intel.com>
---
 .../.config/model_params_tensorflow_3x.json     | 10 +++++-----
 .../quantization/ptq/data_process.py            | 17 +++++++++++++++++
 .../quantization/ptq/main.py                    |  1 +
 3 files changed, 23 insertions(+), 5 deletions(-)

diff --git a/examples/.config/model_params_tensorflow_3x.json b/examples/.config/model_params_tensorflow_3x.json
index 69b5bb11059..1e7f8bdab1a 100644
--- a/examples/.config/model_params_tensorflow_3x.json
+++ b/examples/.config/model_params_tensorflow_3x.json
@@ -12,14 +12,14 @@
       "model_src_dir": "nlp/distilbert_base/quantization/ptq",
       "dataset_location": "/tf_dataset2/datasets/sst2_validation_dataset",
       "input_model": "/tf_dataset2/models/tensorflow/distilbert_base/fp32/distilbert_base_fp32.pb",
-      "main_script": "run_inference.py",
+      "main_script": "main.py",
       "batch_size": 128
     },
     "distilbert_base_sq": {
       "model_src_dir": "nlp/distilbert_base/quantization/ptq",
       "dataset_location": "/tf_dataset2/datasets/sst2_validation_dataset",
       "input_model": "/tf_dataset2/models/tensorflow/distilbert_base/fp32/distilbert_base_fp32.pb",
-      "main_script": "run_inference.py",
+      "main_script": "main.py",
       "batch_size": 128
     },
     "opt_125m_sq": {
@@ -139,7 +139,7 @@
       "model_src_dir": "recommendation/wide_deep_large_ds/quantization/ptq",
       "dataset_location": "/tf_dataset/tensorflow/wide_deep_large_ds/dataset",
       "input_model": "/tf_dataset/tensorflow/wide_deep_large_ds/fp32_optimized_graph.pb",
-      "main_script": "inference.py",
+      "main_script": "main.py",
       "batch_size": 256,
       "fp32_model_url": "https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_8/wide_deep_fp32_pretrained_model.pb"
     },
@@ -147,14 +147,14 @@
       "model_src_dir": "semantic_image_segmentation/3dunet-mlperf/quantization/ptq",
       "dataset_location": "/tf_dataset2/models/tensorflow/3dunet/build",
       "input_model": "/tf_dataset2/models/tensorflow/3dunet/3dunet_dynamic_ndhwc.pb",
-      "main_script": "run_accuracy.py",
+      "main_script": "main.py",
       "batch_size": 100
     },
     "style_transfer": {
       "model_src_dir": "style_transfer/arbitrary_style_transfer/quantization/ptq",
       "dataset_location": "style_images,content_images",
       "input_model": "/tf_dataset/tensorflow/style_transfer/arbitrary_style_transfer/model.ckpt",
-      "main_script": "style_tune.py",
+      "main_script": "main.py",
       "batch_size": 1
     }
   }
diff --git a/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/data_process.py b/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/data_process.py
index 32e55adb3fd..089227554a5 100644
--- a/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/data_process.py
+++ b/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/data_process.py
@@ -256,6 +256,23 @@ def hvd(self, hvd):
         self._hvd = hvd
 
 
+class LabelBalanceCOCORecordFilter(object):
+    """The label balance filter for COCO Record."""
+
+    def __init__(self, size=1):
+        """Initialize the attribute of class."""
+        self.size = size
+
+    def __call__(self, image, label):
+        """Execute the filter.
+
+        Args:
+            image: Not used.
+            label: label of a sample.
+        """
+        return tf.math.equal(len(label[0]), self.size)
+
+
 class COCOmAPv2(BaseMetric):
     """Compute mean average precision of the detection task."""
 
diff --git a/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/main.py b/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/main.py
index 7031a257521..fe30e45edba 100644
--- a/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/main.py
+++ b/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/main.py
@@ -30,6 +30,7 @@
     ComposeTransform,
     ResizeTFTransform,
     TFDataLoader,
+    LabelBalanceCOCORecordFilter,
 )
 
 arg_parser = ArgumentParser(description='Parse args')

From 60fc76c04d87cac6b30fab838e8f3ccb9840ecff Mon Sep 17 00:00:00 2001
From: zehao-intel <zehao.huang@intel.com>
Date: Thu, 11 Jul 2024 17:05:23 +0800
Subject: [PATCH 05/14] fix keras config

Signed-off-by: zehao-intel <zehao.huang@intel.com>
---
 .../cv/inception_v3/quantization/ptq/README.md      |  2 +-
 .../keras/cv/inception_v3/quantization/ptq/main.py  |  3 ++-
 .../cv/mobilenet_v2/quantization/ptq/README.md      | 13 +------------
 .../keras/cv/mobilenet_v2/quantization/ptq/main.py  |  3 ++-
 .../cv/resnet_v2_50/quantization/ptq/README.md      | 13 +------------
 .../keras/cv/resnet_v2_50/quantization/ptq/main.py  |  3 ++-
 6 files changed, 9 insertions(+), 28 deletions(-)

diff --git a/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/README.md b/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/README.md
index 4fa176c3852..b3dfbb967fa 100644
--- a/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/README.md
+++ b/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/README.md
@@ -38,7 +38,7 @@ python prepare_model.py --output_model=/path/to/model
   We also prepared related scripts in `imagenet_prepare` directory. To download the raw images, the user must create an account with image-net.org. If you have downloaded the raw data and preprocessed the validation data by moving the images into the appropriate sub-directory based on the label (synset) of the image. we can use below command ro convert it to tf records format.
 
   ```shell
-  cd examples/keras/image_recognition/
+  cd examples/3.x_api/tensorflow/keras/cv/
   # convert validation subset
   bash prepare_dataset.sh --output_dir=/inception_v3/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
   # convert train subset
diff --git a/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/main.py b/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/main.py
index 2a8ba68a6cf..abccf02d8f5 100644
--- a/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/main.py
+++ b/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/main.py
@@ -119,7 +119,8 @@ def eval_func(dataloader, metric):
 def main(_):
     if FLAGS.tune:
         from neural_compressor.common import set_random_seed
-        from neural_compressor.tensorflow import StaticQuantConfig, quantize_model
+        from neural_compressor.tensorflow import quantize_model
+        from neural_compressor.tensorflow.keras import StaticQuantConfig
 
         set_random_seed(9527)
         quant_config = StaticQuantConfig()
diff --git a/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/README.md b/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/README.md
index cd7a564b505..9c85a116ce6 100644
--- a/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/README.md
+++ b/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/README.md
@@ -37,7 +37,7 @@ python prepare_model.py --output_model=/path/to/model
   We also prepared related scripts in `imagenet_prepare` directory. To download the raw images, the user must create an account with image-net.org. If you have downloaded the raw data and preprocessed the validation data by moving the images into the appropriate sub-directory based on the label (synset) of the image. we can use below command ro convert it to tf records format.
 
   ```shell
-  cd examples/keras/image_recognition/
+  cd examples/3.x_api/tensorflow/keras/cv/
   # convert validation subset
   bash prepare_dataset.sh --output_dir=/mobilenet_v2/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
   # convert train subset
@@ -52,17 +52,6 @@ python prepare_model.py --output_model=/path/to/model
 
 # Run Command
 
-## Quantization Config
-The Quantization Config class has default parameters setting for running on Intel CPUs. If running this example on Intel GPUs, the 'backend' parameter should be set to 'itex' and the 'device' parameter should be set to 'gpu'.
-
-```
-config = PostTrainingQuantConfig(
-    device="gpu",
-    backend="itex",
-    ...
-    )
-```
-
 ## Quantization
   ```shell
   bash run_quant.sh --input_model=./mobilenet_v2_keras/ --output_model=./result --dataset_location=/path/to/evaluation/dataset
diff --git a/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/main.py b/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/main.py
index 0e8767eb0a9..b577b24892f 100644
--- a/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/main.py
+++ b/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/main.py
@@ -119,7 +119,8 @@ def eval_func(dataloader, metric):
 def main(_):
     if FLAGS.tune:
         from neural_compressor.common import set_random_seed
-        from neural_compressor.tensorflow import StaticQuantConfig, quantize_model
+        from neural_compressor.tensorflow import quantize_model
+        from neural_compressor.tensorflow.keras import StaticQuantConfig
 
         set_random_seed(9527)
         quant_config = StaticQuantConfig()
diff --git a/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/README.md b/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/README.md
index 54ab588faf4..251b13b97b3 100644
--- a/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/README.md
+++ b/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/README.md
@@ -37,7 +37,7 @@ python prepare_model.py --output_model=/path/to/model
   We also prepared related scripts in `imagenet_prepare` directory. To download the raw images, the user must create an account with image-net.org. If you have downloaded the raw data and preprocessed the validation data by moving the images into the appropriate sub-directory based on the label (synset) of the image. we can use below command ro convert it to tf records format.
 
   ```shell
-  cd examples/keras/image_recognition/
+  cd examples/3.x_api/tensorflow/keras/cv/
   # convert validation subset
   bash prepare_dataset.sh --output_dir=/resnetv2_50/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
   # convert train subset
@@ -52,17 +52,6 @@ python prepare_model.py --output_model=/path/to/model
 
 # Run Command
 
-## Quantization Config
-The Quantization Config class has default parameters setting for running on Intel CPUs. If running this example on Intel GPUs, the 'backend' parameter should be set to 'itex' and the 'device' parameter should be set to 'gpu'.
-
-```
-config = PostTrainingQuantConfig(
-    device="gpu",
-    backend="itex",
-    ...
-    )
-```
-
 ## Quantization
   ```shell
   bash run_quant.sh --input_model=./resnetv2_50_keras/ --output_model=./result --dataset_location=/path/to/evaluation/dataset
diff --git a/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/main.py b/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/main.py
index 97254a194c1..48f30a79fb4 100644
--- a/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/main.py
+++ b/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/main.py
@@ -119,7 +119,8 @@ def eval_func(dataloader, metric):
 def main(_):
     if FLAGS.tune:
         from neural_compressor.common import set_random_seed
-        from neural_compressor.tensorflow import StaticQuantConfig, quantize_model
+        from neural_compressor.tensorflow import quantize_model
+        from neural_compressor.tensorflow.keras import StaticQuantConfig
 
         set_random_seed(9527)
         quant_config = StaticQuantConfig()

From b78e20b0e9f80ef082b2ac38ba8376be5da6f0c5 Mon Sep 17 00:00:00 2001
From: zehao-intel <zehao.huang@intel.com>
Date: Fri, 12 Jul 2024 15:02:25 +0800
Subject: [PATCH 06/14] fix multiple issues

Signed-off-by: zehao-intel <zehao.huang@intel.com>
---
 .../quantization/ptq/README.md                |   2 +
 .../quantization/ptq/data_process.py          | 137 +++++++++++++++---
 .../quantization/ptq/main.py                  |   7 +-
 .../quantization/ptq/main.py                  |   5 +-
 .../3dunet-mlperf/quantization/ptq/README.md  |  17 ++-
 .../3dunet-mlperf/quantization/ptq/main.py    |   4 +-
 .../quantization/ptq/requirements.txt         |   1 +
 .../quantization/ptq/README.md                |  28 +---
 .../quantization/ptq/data_process.py          | 127 ----------------
 .../quantization/ptq/main.py                  |   7 +-
 .../algorithms/static_quant/keras.py          |   2 +
 11 files changed, 149 insertions(+), 188 deletions(-)

diff --git a/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/README.md b/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/README.md
index 3091b712180..c672d5e6148 100644
--- a/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/README.md
+++ b/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/README.md
@@ -103,6 +103,8 @@ Now we support both pb and ckpt formats.
 ## 2. Benchmark
   ```shell
   bash run_benchmark.sh --input_model=./tensorflow-mask_rcnn_inception_v2-tune.pb  --dataset_location=/path/to/dataset/coco_val.record --mode=performance
+
+  bash run_benchmark.sh --input_model=./tensorflow-mask_rcnn_inception_v2-tune.pb  --dataset_location=/path/to/dataset/coco_val.record --mode=accuracy
   ```
 
 Details of enabling Intel® Neural Compressor on mask_rcnn_inception_v2 for Tensorflow.
diff --git a/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/data_process.py b/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/data_process.py
index 089227554a5..8d0a074ee82 100644
--- a/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/data_process.py
+++ b/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/data_process.py
@@ -136,40 +136,135 @@ def __call__(self, sample):
         return sample
     
 
-class ResizeTFTransform(object):
-    """Resize the input image to the given size.
+class ResizeWithRatio():
+    """Resize image with aspect ratio and pad it to max shape(optional).
+
+    If the image is padded, the label will be processed at the same time.
+    The input image should be np.array.
 
     Args:
-        size (list or int): Size of the result
-        interpolation (str, default='bilinear'):Desired interpolation type,
-                                                support 'bilinear', 'nearest', 'bicubic'
+        min_dim (int, default=800):
+            Resizes the image such that its smaller dimension == min_dim
+        max_dim (int, default=1365):
+            Ensures that the image longest side doesn't exceed this value
+        padding (bool, default=False):
+            If true, pads image with zeros so its size is max_dim x max_dim
 
     Returns:
         tuple of processed image and label
     """
 
-    def __init__(self, size, interpolation="bilinear"):
-        """Initialize `ResizeTFTransform` class."""
-        if isinstance(size, int):
-            self.size = size, size
-        elif isinstance(size, list):
-            if len(size) == 1:
-                self.size = size[0], size[0]
-            elif len(size) == 2:
-                self.size = size[0], size[1]
-        self.interpolation = interpolation
+    def __init__(self, min_dim=800, max_dim=1365, padding=False, constant_value=0):
+        """Initialize `ResizeWithRatio` class."""
+        self.min_dim = min_dim
+        self.max_dim = max_dim
+        self.padding = padding
+        self.constant_value = constant_value
 
-        if self.interpolation not in ["bilinear", "nearest", "bicubic"]:
-            raise ValueError("Unsupported interpolation type!")
+    def __call__(self, sample):
+        """Resize the image with ratio in sample."""
+        image, label = sample
+        height, width = image.shape[:2]
+        scale = 1
+        if self.min_dim:
+            scale = max(1, self.min_dim / min(height, width))
+        if self.max_dim:
+            image_max = max(height, width)
+            if round(image_max * scale) > self.max_dim:
+                scale = self.max_dim / image_max
+        if scale != 1:
+            image = cv2.resize(image, (round(height * scale), round(width * scale)))
+
+        bbox, str_label, int_label, image_id = label
+
+        if self.padding:
+            h, w = image.shape[:2]
+            pad_param = [
+                [(self.max_dim - h) // 2, self.max_dim - h - (self.max_dim - h) // 2],
+                [(self.max_dim - w) // 2, self.max_dim - w - (self.max_dim - w) // 2],
+                [0, 0],
+            ]
+            if not isinstance(bbox, np.ndarray):
+                bbox = np.array(bbox)
+            resized_box = bbox * [height, width, height, width] * scale
+            moved_box = resized_box + [
+                (self.max_dim - h) // 2,
+                (self.max_dim - w) // 2,
+                (self.max_dim - h) // 2,
+                (self.max_dim - w) // 2,
+            ]
+            bbox = moved_box / [self.max_dim, self.max_dim, self.max_dim, self.max_dim]
+            image = np.pad(image, pad_param, mode="constant", constant_values=self.constant_value)
+        return image, (bbox, str_label, int_label, image_id)
+
+
+class TensorflowResizeWithRatio():
+    """Resize image with aspect ratio and pad it to max shape(optional).
+
+    If the image is padded, the label will be processed at the same time.
+    The input image should be np.array or tf.Tensor.
+
+    Args:
+        min_dim (int, default=800):
+            Resizes the image such that its smaller dimension == min_dim
+        max_dim (int, default=1365):
+            Ensures that the image longest side doesn't exceed this value
+        padding (bool, default=False):
+            If true, pads image with zeros so its size is max_dim x max_dim
+
+    Returns:
+        tuple of processed image and label
+    """
+
+    def __init__(self, min_dim=800, max_dim=1365, padding=False, constant_value=0):
+        """Initialize `TensorflowResizeWithRatio` class."""
+        self.min_dim = min_dim
+        self.max_dim = max_dim
+        self.padding = padding
+        self.constant_value = constant_value
 
     def __call__(self, sample):
-        """Resize the input image in sample to the given size."""
+        """Resize the image with ratio in sample."""
         image, label = sample
         if isinstance(image, tf.Tensor):
-            image = tf.image.resize(image, self.size, method=self.interpolation)
+            shape = tf.shape(input=image)
+            height = tf.cast(shape[0], dtype=tf.float32)
+            width = tf.cast(shape[1], dtype=tf.float32)
+            scale = 1
+            if self.min_dim:
+                scale = tf.maximum(1.0, tf.cast(self.min_dim / tf.math.minimum(height, width), dtype=tf.float32))
+            if self.max_dim:
+                image_max = tf.cast(tf.maximum(height, width), dtype=tf.float32)
+                scale = tf.cond(
+                    pred=tf.greater(tf.math.round(image_max * scale), self.max_dim),
+                    true_fn=lambda: self.max_dim / image_max,
+                    false_fn=lambda: scale,
+                )
+            image = tf.image.resize(image, (tf.math.round(height * scale), tf.math.round(width * scale)))
+            bbox, str_label, int_label, image_id = label
+
+            if self.padding:
+                shape = tf.shape(input=image)
+                h = tf.cast(shape[0], dtype=tf.float32)
+                w = tf.cast(shape[1], dtype=tf.float32)
+                pad_param = [
+                    [(self.max_dim - h) // 2, self.max_dim - h - (self.max_dim - h) // 2],
+                    [(self.max_dim - w) // 2, self.max_dim - w - (self.max_dim - w) // 2],
+                    [0, 0],
+                ]
+                resized_box = bbox * [height, width, height, width] * scale
+                moved_box = resized_box + [
+                    (self.max_dim - h) // 2,
+                    (self.max_dim - w) // 2,
+                    (self.max_dim - h) // 2,
+                    (self.max_dim - w) // 2,
+                ]
+                bbox = moved_box / [self.max_dim, self.max_dim, self.max_dim, self.max_dim]
+                image = tf.pad(image, pad_param, constant_values=self.constant_value)
         else:
-            image = cv2.resize(image, self.size, interpolation=interpolation_map[self.interpolation])
-        return (image, label)
+            transform = ResizeWithRatio(self.min_dim, self.max_dim, self.padding)
+            image, (bbox, str_label, int_label, image_id) = transform(sample)
+        return image, (bbox, str_label, int_label, image_id)
 
 
 class BaseMetric(object):
diff --git a/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/main.py b/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/main.py
index fe30e45edba..06498ca56e9 100644
--- a/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/main.py
+++ b/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/main.py
@@ -28,9 +28,9 @@
     COCOmAPv2,
     COCORecordDataset,
     ComposeTransform,
-    ResizeTFTransform,
     TFDataLoader,
     LabelBalanceCOCORecordFilter,
+    TensorflowResizeWithRatio,
 )
 
 arg_parser = ArgumentParser(description='Parse args')
@@ -92,11 +92,12 @@ def eval_func(dataloader):
         latency = np.array(latency_list[warmup:]).mean() / args.batch_size
         return latency
 
+    use_padding = True if args.mode == 'performance' else False
     eval_dataset = COCORecordDataset(root=args.dataset_location, filter=None, \
             transform=ComposeTransform(transform_list=[TensorflowResizeWithRatio(
-                                        min_dim=800, max_dim=1356, padding=False)]))
+                                        min_dim=800, max_dim=1356, padding=use_padding)]))
     batch_size = 1 if args.mode == 'accuracy' else args.batch_size
-    eval_dataloader=TFDataLoader(dataset=eval_dataset, batch_size=args.batch_size)
+    eval_dataloader=TFDataLoader(dataset=eval_dataset, batch_size=batch_size)
 
     latency = eval_func(eval_dataloader)
     if args.benchmark and args.mode == 'performance':
diff --git a/examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/main.py b/examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/main.py
index 7ea26e60ff1..a89efd25537 100644
--- a/examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/main.py
+++ b/examples/3.x_api/tensorflow/recommendation/wide_deep_large_ds/quantization/ptq/main.py
@@ -111,7 +111,7 @@ def _parse_function(proto):
     return dataset
 
 def evaluation_func(model, measurer=None):
-    evaluate_opt_graph.eval_inference(model)
+    return evaluate_opt_graph.eval_inference(model)
 
 class eval_classifier_optimized_graph:
     """Evaluate image classifier with optimized TensorFlow graph"""
@@ -294,8 +294,7 @@ def eval_inference(self, infer_graph):
         print('Throughput: %.3f records/sec' % throughput)
         print('--------------------------------------------------')
 
-        if self.args.accuracy:
-            return accuracy
+        return accuracy
 
     def run(self):
         """ This is neural_compressor function include tuning and benchmark option """
diff --git a/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/README.md b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/README.md
index 5747ba5b4ac..e4618e7604c 100644
--- a/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/README.md
+++ b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/README.md
@@ -14,9 +14,9 @@ This example can run on Intel CPUs and GPUs.
 pip install neural-compressor
 ```
 
-### Install Intel Tensorflow
+### Install requirements
 ```shell
-pip install intel-tensorflow
+pip install -r requirements.txt
 ```
 > Note: Validated TensorFlow [Version](/docs/source/installation_guide.md#validated-software-environment).
 
@@ -59,15 +59,18 @@ pip install --upgrade intel-extension-for-tensorflow[cpu]
 
 
 # Run command
+Please set the following environment variables before running quantization or benchmark commands:
+
+* `export nnUNet_preprocessed=<path/to/build>/build/preprocessed_data`
+* `export nnUNet_raw_data_base=<path/to/build>/build/raw_data`
+* `export RESULTS_FOLDER=<path/to/build>/build/result`
 
 ## Quantization
 
+`bash run_quant.sh --input_model=3dunet_dynamic_ndhwc.pb --dataset_location=<path/to/build>/build --output_model=3dunet_dynamic_ndhwc_int8.pb`
 
 ## Benchmark
 
-* `export nnUNet_preprocessed=<path/to/build>/build/preprocessed_data`
-* `export nnUNet_raw_data_base=<path/to/build>/build/raw_data`
-* `export RESULTS_FOLDER=<path/to/build>/build/result`
-* `pip install -r requirements.txt`
-* `python run_accuracy.py --input-model=<path/to/model_file> --data-location=<path/to/dataset> --calib-preprocess=<path/to/calibrationset> --iters=100 --batch-size=1 --mode=benchmark --bfloat16 0`
+`bash run_benchmark.sh --input_model=3dunet_dynamic_ndhwc_int8.pb --dataset_location=<path/to/build>/build --batch_size=100 --mode=benchmark`
 
+`bash run_benchmark.sh --input_model=3dunet_dynamic_ndhwc_int8.pb --dataset_location=<path/to/build>/build --batch_size=1 --mode=accuracy`
diff --git a/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/main.py b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/main.py
index 5ba82875420..bc8ce8edc07 100644
--- a/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/main.py
+++ b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/main.py
@@ -51,7 +51,7 @@ def get_args():
                                 help="One of three options: 'benchmark'/'accuracy'/'tune'.")
         arg_parser.add_argument('-n', "--iters",
                                 help='The number of iteration. shall > warmup num(10)',
-                                type=int, default=20)
+                                type=int, default=100)
         arg_parser.add_argument('-e', "--num-inter-threads",
                                 help='The number of inter-thread.',
                                 dest='num_inter_threads', type=int, default=0)
@@ -209,7 +209,7 @@ def __len__(self):
 
         set_random_seed(9527)
         quant_config = StaticQuantConfig()
-        calib_dataloader=BaseDataloader(dataset=CalibrationDL())
+        calib_dataloader=BaseDataLoader(dataset=CalibrationDL())
         q_model = quantize_model(graph, quant_config, calib_dataloader)
         try:
             q_model.save(args.output_model)
diff --git a/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/requirements.txt b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/requirements.txt
index 4e85853747e..d5069f8038f 100644
--- a/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/requirements.txt
+++ b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/requirements.txt
@@ -1 +1,2 @@
 nnunet
+tensorflow
\ No newline at end of file
diff --git a/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/README.md b/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/README.md
index 2802279a8c3..6fa291d0b36 100644
--- a/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/README.md
+++ b/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/README.md
@@ -61,7 +61,7 @@ optional arguments:
 
 ```shell
 wget https://storage.googleapis.com/download.magenta.tensorflow.org/models/arbitrary_style_transfer.tar.gz
-tar -xvzf arbitrary_style_transfer.tar.gz ./model
+tar -xvzf arbitrary_style_transfer.tar.gz
 ```
 
 ### 3. Prepare Dataset
@@ -70,22 +70,12 @@ There are two folders named style_images and content_images in current folder. P
 
 # Run Command
   ```shell
-  python style_tune.py --output_dir=./result --style_images_paths=./style_images --content_images_paths=./content_images --input_model=./model/model.ckpt
+  python main.py --output_dir=./result --style_images_paths=./style_images --content_images_paths=./content_images --input_model=./model/model.ckpt
   ```
 
 
 ## Quantization Config
 
-The Quantization Config class has default parameters setting for running on Intel CPUs. If running this example on Intel GPUs, the 'backend' parameter should be set to 'itex' and the 'device' parameter should be set to 'gpu'.
-
-```
-config = PostTrainingQuantConfig(
-    device="gpu",
-    backend="itex",
-    ...
-    )
-```
-
 ## Quantization
   ```shell
   bash run_quant.sh --dataset_location=style_images/,content_images/ --input_model=./model/model.ckpt --output_model=saved_model
@@ -119,13 +109,9 @@ Here we set the input tensor and output tensors name into *inputs* and *outputs*
 
 After prepare step is done, we just need add 2 lines to get the quantized model.
 ```python
-from neural_compressor import quantization
-from neural_compressor.config import PostTrainingQuantConfig
-conf = PostTrainingQuantConfig(inputs=['style_input', 'content_input'],
-                                outputs=['transformer/expand/conv3/conv/Sigmoid'],
-                                calibration_sampling_size=[50, 100])
-quantized_model = quantization.fit(args.input_graph, conf=conf, calib_dataloader=dataloader,
-            eval_dataloader==dataloader)
-```
+from neural_compressor.tensorflow import StaticQuantConfig, quantize_model
 
-The Intel® Neural Compressor quantizer.fit() function will return a best quantized model during timeout constrain.
+quant_config = StaticQuantConfig()
+q_model = quantize_model(graph, quant_config, calib_dataloader)
+q_model.save(FLAGS.output_model)
+```
diff --git a/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/data_process.py b/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/data_process.py
index e71f6577cac..d49c262bbec 100644
--- a/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/data_process.py
+++ b/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/data_process.py
@@ -360,130 +360,3 @@ def _topk_shape_validate(preds, labels):
         if labels.shape[1] != 1:
             labels = labels.argsort()[..., -1:]
         return preds, labels
-    
-
-class TFDataLoader(object):  # pragma: no cover
-    """Tensorflow dataloader class.
-
-    In tensorflow1.x dataloader is coupled with the graph, but it also support feed_dict
-    method to do session run, this dataloader is designed to satisfy the usage of feed dict
-    in tf1.x. Although it's a general dataloader and can be used in MXNet and PyTorch.
-
-    Args:
-        dataset: obj. wrapper of needed data.
-        batch_size: int. batch size
-    """
-
-    def __init__(self, dataset, batch_size=1, last_batch="rollover"):
-        """Initialize `TFDataDataLoader` class."""
-        self.dataset = dataset
-        self.last_batch = last_batch
-        self.batch_size = batch_size
-        dataset = dataset.batch(batch_size)
-
-    def batch(self, batch_size, last_batch="rollover"):
-        """Dataset return data per batch."""
-        drop_last = False if last_batch == "rollover" else True
-        self.batch_size = batch_size
-        self.dataset = self.dataset.batch(batch_size, drop_last)
-
-    def __iter__(self):
-        """Iterate dataloader."""
-        return self._generate_dataloader(
-            self.dataset,
-            batch_size=self.batch_size,
-            last_batch=self.last_batch,
-        )
-
-    def _generate_dataloader(
-        self,
-        dataset,
-        batch_size=1,
-        last_batch="rollover",
-        collate_fn=None,
-        sampler=None,
-        batch_sampler=None,
-        num_workers=None,
-        pin_memory=None,
-        distributed=False,
-    ):
-        """Yield data."""
-        drop_last = False if last_batch == "rollover" else True
-
-        def check_dynamic_shape(element_spec):
-            if isinstance(element_spec, collections.abc.Sequence):
-                return any([check_dynamic_shape(ele) for ele in element_spec])
-            elif isinstance(element_spec, tf.TensorSpec):
-                return True if element_spec.shape.num_elements() is None else False
-            else:
-                raise ValueError("unrecognized element spec...")
-
-        def squeeze_output(output):
-            if isinstance(output, collections.abc.Sequence):
-                return [squeeze_output(ele) for ele in output]
-            elif isinstance(output, np.ndarray):
-                return np.squeeze(output, axis=0)
-            else:
-                raise ValueError("not supported output format....")
-
-        if tf.executing_eagerly():
-            index = 0
-            outputs = []
-            for iter_tensors in dataset:
-                samples = []
-                iter_inputs, iter_labels = iter_tensors[0], iter_tensors[1]
-                if isinstance(iter_inputs, tf.Tensor):
-                    samples.append(iter_inputs.numpy())
-                else:
-                    samples.append(tuple(iter_input.numpy() for iter_input in iter_inputs))
-                if isinstance(iter_labels, tf.Tensor):
-                    samples.append(iter_labels.numpy())
-                else:
-                    samples.append([np.array(l) for l in iter_labels])
-                index += 1
-                outputs.append(samples)
-                if index == batch_size:
-                    outputs = default_collate(outputs)
-                    yield outputs
-                    outputs = []
-                    index = 0
-            if len(outputs) > 0:
-                outputs = default_collate(outputs)
-                yield outputs
-        else:
-            try_single_batch = check_dynamic_shape(dataset.element_spec)
-            dataset = dataset.batch(1 if try_single_batch else batch_size, drop_last)
-            ds_iterator = tf.compat.v1.data.make_one_shot_iterator(dataset)
-            iter_tensors = ds_iterator.get_next()
-            data_config = tf.compat.v1.ConfigProto()
-            data_config.use_per_session_threads = 1
-            data_config.intra_op_parallelism_threads = 1
-            data_config.inter_op_parallelism_threads = 16
-            data_sess = tf.compat.v1.Session(config=data_config)
-            # pylint: disable=no-name-in-module
-            from tensorflow.python.framework.errors_impl import OutOfRangeError
-
-            while True:
-                if not try_single_batch:
-                    try:
-                        outputs = data_sess.run(iter_tensors)
-                        yield outputs
-                    except OutOfRangeError:
-                        data_sess.close()
-                        return
-                else:
-                    try:
-                        outputs = []
-                        for i in range(0, batch_size):
-                            outputs.append(squeeze_output(data_sess.run(iter_tensors)))
-                        outputs = default_collate(outputs)
-                        yield outputs
-                    except OutOfRangeError:
-                        if len(outputs) == 0:
-                            data_sess.close()
-                            return
-                        else:
-                            outputs = default_collate(outputs)
-                            yield outputs
-                            data_sess.close()
-                            return
diff --git a/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/main.py b/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/main.py
index 577e9094209..440b0cee4af 100644
--- a/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/main.py
+++ b/examples/3.x_api/tensorflow/style_transfer/arbitrary_style_transfer/quantization/ptq/main.py
@@ -26,8 +26,8 @@
 from PIL import Image
 import time
 
+from neural_compressor.tensorflow.utils import BaseDataLoader, DummyDatasetV2
 from data_process import (
-    TFDataLoader,
     StyleTransferDataset,
     ComposeTransform,
     ParseDecodeVocTransform,
@@ -137,7 +137,7 @@ def main(args=None):
                         ]
                     )
                 )
-                calib_dataloader = TFDataLoader(dataset=dataset, batch_size=FLAGS.batch_size)
+                calib_dataloader = BaseDataLoader(dataset=dataset, batch_size=FLAGS.batch_size)
                 
                 quant_config = StaticQuantConfig()
                 q_model = quantize_model(graph, quant_config, calib_dataloader)
@@ -156,10 +156,9 @@ def main(args=None):
                     resize_shape=(256, 256)
                 )
         else: 
-            from neural_compressor.tensorflow.utils import DummyDatasetV2
             dataset = DummyDatasetV2(input_shape=[(256, 256, 3), (256, 256, 3)], label_shape=(1, ))
 
-        dataloader = TFDataLoader(dataset=dataset, batch_size=FLAGS.batch_size)
+        dataloader = BaseDataLoader(dataset=dataset, batch_size=FLAGS.batch_size)
         tf.import_graph_def(frozen_graph, name='')
         style_transfer(sess, dataloader)
 
diff --git a/neural_compressor/tensorflow/algorithms/static_quant/keras.py b/neural_compressor/tensorflow/algorithms/static_quant/keras.py
index 004393c8c27..6e8df825df2 100644
--- a/neural_compressor/tensorflow/algorithms/static_quant/keras.py
+++ b/neural_compressor/tensorflow/algorithms/static_quant/keras.py
@@ -204,6 +204,8 @@ def _fuse_bn_keras2(self, fuse_conv_bn, fp32_layers):  # pragma: no cover
                     else:
                         for bound_node in layer._inbound_nodes:
                             inbound_layer = bound_node.inbound_layers
+                            if isinstance(inbound_layer, list) and len(inbound_layer) == 0:
+                                continue
                             if inbound_layer in self.bn_weights.keys():
                                 for bn_inbound_node in inbound_layer._inbound_nodes:
                                     bn_inbound_layer = bn_inbound_node.inbound_layers

From 88d86002cdcf73f71dd787c7cf1e43a415151d47 Mon Sep 17 00:00:00 2001
From: zehao-intel <zehao.huang@intel.com>
Date: Mon, 15 Jul 2024 16:51:23 +0800
Subject: [PATCH 07/14] fix transformer_lt

Signed-off-by: zehao-intel <zehao.huang@intel.com>
---
 .../transformer_lt/quantization/ptq/README.md | 22 +++++++++----------
 .../transformer_lt/quantization/ptq/main.py   |  5 ++---
 .../algorithms/static_quant/keras.py          |  2 +-
 3 files changed, 13 insertions(+), 16 deletions(-)

diff --git a/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/README.md b/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/README.md
index 9aad1dda2a9..544e954371e 100644
--- a/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/README.md
+++ b/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/README.md
@@ -58,22 +58,22 @@ bash prepare_dataset_model.sh
 ```
 
 ## Run Command
+### Quantization
 
 ```shell
-python main.py --input_graph=/path/to/fp32_graphdef.pb --inputs_file=/path/to/newstest2014.en --reference_file=/path/to/newstest2014.de --vocab_file=/path/to/vocab.txt --tune
+bash run_quant.sh --input_model=./model/fp32_graphdef.pb --dataset_location=./data --output_model=./model/int8_graphdef.pb
+```
+### Benchmark
+```shell
+bash run_benchmark.sh --input_model=./model/int8_graphdef.pb --dataset_location=./data --mode=performance
+
+bash run_benchmark.sh --input_model=./model/int8_graphdef.pb --dataset_location=./data --mode=accuracy --batch_size=1
 ```
 
 Details of enabling Intel® Neural Compressor on transformer-lt for Tensorflow.
 =========================
 
 This is a tutorial of how to enable transformer-lt model with Intel® Neural Compressor.
-## User Code Analysis
-1. User specifies fp32 *model*, calibration dataset *q_dataloader*, evaluation dataset *eval_dataloader* and metric in tuning.metric field of model-specific yaml config file.
-
-2. User specifies fp32 *model*, calibration dataset *q_dataloader* and a custom *eval_func* which encapsulates the evaluation dataset and metric by itself.
-
-For transformer-lt, we applied the latter one because we don't have dataset and metric for transformer-lt. The task is to implement the *q_dataloader* and *eval_func*.
-
 
 ### q_dataloader Part Adaption
 Below dataset class uses getitem to provide the model with input.
@@ -124,9 +124,7 @@ After prepare step is done, we add tune code to generate quantized model.
     if FLAGS.benchmark:
         assert FLAGS.mode == 'performance' or FLAGS.mode == 'accuracy', \
         "Benchmark only supports performance or accuracy mode."
-        eval_func(graph)
-    elif FLAGS.mode == 'accuracy':
         acc = eval_func(graph)
-        print('Accuracy is {:.3f}'.format(acc))
+        if FLAGS.mode == 'accuracy':
+            print('Accuracy is {:.3f}'.format(acc))
 ```
-The Intel® Neural Compressor quantization.fit() function will return a best quantized model under time constraint.
diff --git a/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/main.py b/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/main.py
index 99ac63d2c02..58a93090e7a 100644
--- a/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/main.py
+++ b/examples/3.x_api/tensorflow/nlp/transformer_lt/quantization/ptq/main.py
@@ -250,10 +250,9 @@ def main(_):
     if FLAGS.benchmark:
         assert FLAGS.mode == 'performance' or FLAGS.mode == 'accuracy', \
         "Benchmark only supports performance or accuracy mode."
-        eval_func(graph)
-    elif FLAGS.mode == 'accuracy':
         acc = eval_func(graph)
-        print('Accuracy is {:.3f}'.format(acc))
+        if FLAGS.mode == 'accuracy':
+            print('Accuracy is {:.3f}'.format(acc))
 
 if __name__ == "__main__":
     tf.compat.v1.app.run()
diff --git a/neural_compressor/tensorflow/algorithms/static_quant/keras.py b/neural_compressor/tensorflow/algorithms/static_quant/keras.py
index 6e8df825df2..32700e4c977 100644
--- a/neural_compressor/tensorflow/algorithms/static_quant/keras.py
+++ b/neural_compressor/tensorflow/algorithms/static_quant/keras.py
@@ -87,7 +87,7 @@ def __init__(self, framework_specific_info):
         self.fold_conv = []
         self.keras3 = True if version1_gte_version2(tf.__version__, "2.16.1") else False
         if not os.path.exists(DEFAULT_WORKSPACE):
-            os.mkdir(DEFAULT_WORKSPACE)
+            os.makedirs(DEFAULT_WORKSPACE)
         self.tmp_dir = (DEFAULT_WORKSPACE + "tmp_model.keras") if self.keras3 else (DEFAULT_WORKSPACE + "tmp_model")
 
     def _set_weights(self, qmodel, layer_weights):

From b92543c8d682c516e2cf31eb54c6072b03676e02 Mon Sep 17 00:00:00 2001
From: zehao-intel <zehao.huang@intel.com>
Date: Mon, 15 Jul 2024 22:35:32 +0800
Subject: [PATCH 08/14] fix keras

Signed-off-by: zehao-intel <zehao.huang@intel.com>
---
 .../inception_v3/quantization/ptq/README.md   |  8 +++---
 .../mobilenet_v2/quantization/ptq/README.md   |  8 +++---
 .../resnet_v2_50/quantization/ptq/README.md   |  8 +++---
 .../algorithms/static_quant/keras.py          | 27 ++++++++++---------
 4 files changed, 26 insertions(+), 25 deletions(-)

diff --git a/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/README.md b/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/README.md
index b3dfbb967fa..a9275ab13ce 100644
--- a/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/README.md
+++ b/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/README.md
@@ -27,7 +27,7 @@ pip install -r requirements.txt
 
 The pretrained model is provided by [Keras Applications](https://keras.io/api/applications/). prepare the model, Run as follow: 
  ```
-python prepare_model.py --output_model=/path/to/model
+python prepare_model.py --output_model=./inception_v3_keras
  ```
 `--output_model ` the model should be saved as SavedModel format or H5 format.
 
@@ -40,9 +40,9 @@ python prepare_model.py --output_model=/path/to/model
   ```shell
   cd examples/3.x_api/tensorflow/keras/cv/
   # convert validation subset
-  bash prepare_dataset.sh --output_dir=/inception_v3/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
+  bash prepare_dataset.sh --output_dir=./inception_v3/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
   # convert train subset
-  bash prepare_dataset.sh --output_dir=/inception_v3/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/train/ --subset=train
+  bash prepare_dataset.sh --output_dir=./inception_v3/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/train/ --subset=train
   cd inception_v3/quantization/ptq
   ```
 > **Note**: 
@@ -55,7 +55,7 @@ python prepare_model.py --output_model=/path/to/model
 
 ## Quantization
   ```shell
-  bash run_quant.sh --input_model=./inception_v3_keras/ --output_model=./result --dataset_location=/path/to/evaluation/dataset
+  bash run_quant.sh --input_model=./inception_v3_keras --output_model=./result --dataset_location=/path/to/evaluation/dataset
   ```
 
 ## Benchmark
diff --git a/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/README.md b/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/README.md
index 9c85a116ce6..5f7da9d595f 100644
--- a/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/README.md
+++ b/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/README.md
@@ -27,7 +27,7 @@ pip install -r requirements.txt
 
 The pretrained model is provided by [Keras Applications](https://keras.io/api/applications/). prepare the model, Run as follow: 
  ```
-python prepare_model.py --output_model=/path/to/model
+python prepare_model.py --output_model=./mobilenet_v2_keras
  ```
 `--output_model ` the model should be saved as SavedModel format or H5 format.
 
@@ -39,9 +39,9 @@ python prepare_model.py --output_model=/path/to/model
   ```shell
   cd examples/3.x_api/tensorflow/keras/cv/
   # convert validation subset
-  bash prepare_dataset.sh --output_dir=/mobilenet_v2/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
+  bash prepare_dataset.sh --output_dir=./mobilenet_v2/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
   # convert train subset
-  bash prepare_dataset.sh --output_dir=/mobilenet_v2/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/train/ --subset=train
+  bash prepare_dataset.sh --output_dir=./mobilenet_v2/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/train/ --subset=train
   cd mobilenet_v2/quantization/ptq
   ```
 > **Note**: 
@@ -54,7 +54,7 @@ python prepare_model.py --output_model=/path/to/model
 
 ## Quantization
   ```shell
-  bash run_quant.sh --input_model=./mobilenet_v2_keras/ --output_model=./result --dataset_location=/path/to/evaluation/dataset
+  bash run_quant.sh --input_model=./mobilenet_v2_keras --output_model=./result --dataset_location=/path/to/evaluation/dataset
   ```
 
 ## Benchmark
diff --git a/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/README.md b/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/README.md
index 251b13b97b3..a276ef7cd0d 100644
--- a/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/README.md
+++ b/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/README.md
@@ -27,7 +27,7 @@ pip install -r requirements.txt
 
 The pretrained model is provided by [Keras Applications](https://keras.io/api/applications/). prepare the model, Run as follow: 
  ```
-python prepare_model.py --output_model=/path/to/model
+python prepare_model.py --output_model=./resnetv2_50_keras
  ```
 `--output_model ` the model should be saved as SavedModel format or H5 format.
 
@@ -39,9 +39,9 @@ python prepare_model.py --output_model=/path/to/model
   ```shell
   cd examples/3.x_api/tensorflow/keras/cv/
   # convert validation subset
-  bash prepare_dataset.sh --output_dir=/resnetv2_50/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
+  bash prepare_dataset.sh --output_dir=./resnetv2_50/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
   # convert train subset
-  bash prepare_dataset.sh --output_dir=/resnetv2_50/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/train/ --subset=train
+  bash prepare_dataset.sh --output_dir=./resnetv2_50/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/train/ --subset=train
   cd resnetv2_50/quantization/ptq
   ```
 > **Note**: 
@@ -54,7 +54,7 @@ python prepare_model.py --output_model=/path/to/model
 
 ## Quantization
   ```shell
-  bash run_quant.sh --input_model=./resnetv2_50_keras/ --output_model=./result --dataset_location=/path/to/evaluation/dataset
+  bash run_quant.sh --input_model=./resnetv2_50_keras --output_model=./result --dataset_location=/path/to/evaluation/dataset
   ```
 
 ## Benchmark
diff --git a/neural_compressor/tensorflow/algorithms/static_quant/keras.py b/neural_compressor/tensorflow/algorithms/static_quant/keras.py
index 32700e4c977..f6803c03604 100644
--- a/neural_compressor/tensorflow/algorithms/static_quant/keras.py
+++ b/neural_compressor/tensorflow/algorithms/static_quant/keras.py
@@ -203,19 +203,20 @@ def _fuse_bn_keras2(self, fuse_conv_bn, fp32_layers):  # pragma: no cover
                         fuse_layers.append(layer)
                     else:
                         for bound_node in layer._inbound_nodes:
-                            inbound_layer = bound_node.inbound_layers
-                            if isinstance(inbound_layer, list) and len(inbound_layer) == 0:
-                                continue
-                            if inbound_layer in self.bn_weights.keys():
-                                for bn_inbound_node in inbound_layer._inbound_nodes:
-                                    bn_inbound_layer = bn_inbound_node.inbound_layers
-                                    if bn_inbound_layer.name in self.conv_weights.keys():
-                                        new_bound_nodes.append(bn_inbound_node)
-                                    else:
-                                        if bound_node not in new_bound_nodes:
-                                            new_bound_nodes.append(bound_node)
-                            else:
-                                new_bound_nodes.append(bound_node)
+                            inbound_layers = bound_node.inbound_layers
+                            if not isinstance(inbound_layers, list):
+                                inbound_layers = [inbound_layers]
+                            for inbound_layer in inbound_layers:
+                                if inbound_layer in self.bn_weights.keys():
+                                    for bn_inbound_node in inbound_layer._inbound_nodes:
+                                        bn_inbound_layer = bn_inbound_node.inbound_layers
+                                        if bn_inbound_layer.name in self.conv_weights.keys():
+                                            new_bound_nodes.append(bn_inbound_node)
+                                        else:
+                                            if bound_node not in new_bound_nodes:
+                                                new_bound_nodes.append(bound_node)
+                                else:
+                                    new_bound_nodes.append(bound_node)
 
                         layer._inbound_nodes.clear()
                         for bound_node in new_bound_nodes:

From 6c5ae4c8a5489d4d42ee99a089fb26c8577c18d7 Mon Sep 17 00:00:00 2001
From: zehao-intel <zehao.huang@intel.com>
Date: Tue, 16 Jul 2024 12:01:44 +0800
Subject: [PATCH 09/14] remove distillbert

Signed-off-by: zehao-intel <zehao.huang@intel.com>
---
 .../quantization/ptq/README.md                | 187 ----------
 .../quantization/ptq/download_dataset.py      |  38 --
 .../distilbert_base/quantization/ptq/main.py  | 324 ------------------
 .../quantization/ptq/requirements.txt         |   7 -
 .../quantization/ptq/run_benchmark.sh         |  88 -----
 .../quantization/ptq/run_quant.sh             |  83 -----
 .../3dunet-mlperf/quantization/ptq/README.md  |   2 +-
 .../quantization/ptq/run_benchmark.sh         |   4 +
 .../tensorflow/utils/model_wrappers.py        |   1 +
 9 files changed, 6 insertions(+), 728 deletions(-)
 delete mode 100644 examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/README.md
 delete mode 100644 examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/download_dataset.py
 delete mode 100644 examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/main.py
 delete mode 100644 examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/requirements.txt
 delete mode 100644 examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/run_benchmark.sh
 delete mode 100644 examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/run_quant.sh

diff --git a/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/README.md b/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/README.md
deleted file mode 100644
index 83d17508bb0..00000000000
--- a/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/README.md
+++ /dev/null
@@ -1,187 +0,0 @@
-Step-by-Step
-============
-
-This document is used to list steps of reproducing TensorFlow Intel® Neural Compressor tuning zoo result of DistilBERT base. This example can be run on Intel CPUs and GPUs.
-
-## Model Details
-This DistilBERT base model is based on the paper [*DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter*](https://arxiv.org/abs/1910.01108). \
-The [pretrained-model](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english?text=I+like+you.+I+love+you) thus used, was taken from [Hugging face model repository](https://huggingface.co/models). \
-The frozen model pb can be found at [Model Zoo for Intel® Architecture](https://github.com/IntelAI/models/tree/master/models/language_modeling/tensorflow/distilbert_base/inference).
-
-## Dataset Details
-We use a part of Stanford Sentiment Treebank corpus for our task. Specifically, the validation split present in the SST2 dataset in the hugging face [repository](https://huggingface.co/datasets/sst2). It contains 872 labeled English sentences. The details for downloading the dataset are given below. 
-
-## Prerequisite
-
-### 1. Install Intel® Neural Compressor
-```shell
-pip install neural-compressor
-```
-
-### 2. Install TensorFlow 2.11.dev202242
-Build a TensorFlow pip package from [intel-tensorflow spr_ww42 branch](https://github.com/Intel-tensorflow/tensorflow/tree/spr_ww42) and install it. How to build a TensorFlow pip package from source please refer to this [tutorial](https://www.tensorflow.org/install/source).
-
-### 3. Install Requirements
-```shell
-pip install -r requirements.txt
-```
-
-### 4. Install Intel® Extension for TensorFlow
-
-#### Quantizing the model on Intel GPU(Mandatory to install ITEX)
-Intel Extension for Tensorflow is mandatory to be installed for quantizing the model on Intel GPUs.
-
-```shell
-pip install --upgrade intel-extension-for-tensorflow[xpu]
-```
-Please refer to the [Installation Guides](https://dgpu-docs.intel.com/installation-guides/ubuntu/ubuntu-focal-dc.html) for latest Intel GPU driver installation.
-For any more details, please follow the procedure in [install-gpu-drivers](https://github.com/intel/intel-extension-for-tensorflow/blob/main/docs/install/install_for_xpu.md#install-gpu-drivers).
-
-#### Quantizing the model on Intel CPU(Optional to install ITEX)
-Intel Extension for Tensorflow for Intel CPUs is experimental currently. It's not mandatory for quantizing the model on Intel CPUs.
-
-```shell
-pip install --upgrade intel-extension-for-tensorflow[cpu]
-```
-
-> **Note**: 
-> The version compatibility of stock Tensorflow and ITEX can be checked [here](https://github.com/intel/intel-extension-for-tensorflow#compatibility-table). Please make sure you have installed compatible Tensorflow and ITEX.
-
-### 5. Download Dataset
-```shell
-python download_dataset.py --path_to_save_dataset <enter path to save dataset>
-```
-
-### 6. Download Model
-Download Frozen graph:
-```shell
-wget https://storage.googleapis.com/intel-optimized-tensorflow/models/2_10_0/distilbert_frozen_graph_fp32_final.pb
-```
-
-## Run Command
-### Run Tuning:
-```shell
-bash run_quant.sh \
-    --input_model=$INPUT_MODEL \
-    --dataset_location=$DATASET_DIR \
-    --output_model=$OUTPUT_MODEL \
-    --batch_size=$BATCH_SIZE \
-    --max_seq_length=$MAX_SEQ \
-    --warmup_steps=$WARMUPS \
-    --num_inter=$INTER_THREADS \
-    --num_intra=$INTRA_THREADS
-```
-### Run Benchmark:
-```shell
-# performance mode: get performance
-bash run_benchmark.sh \
-    --input_model=$INPUT_MODEL \
-    --dataset_location=$DATASET_DIR \
-    --mode=performance \
-    --batch_size=$BATCH_SIZE \
-    --max_seq_length=$MAX_SEQ \
-    --iters=$ITERS \
-    --warmup_steps=$WARMUPS \
-    --num_inter=$INTER_THREADS \
-    --num_intra=$INTRA_THREADS
-```
-
-```shell
-# accuracy mode: get accuracy
-bash run_benchmark.sh \
-    --input_model=$INPUT_MODEL \
-    --dataset_location=$DATASET_DIR \
-    --mode=accuracy \
-    --batch_size=$BATCH_SIZE \
-    --max_seq_length=$MAX_SEQ \
-    --warmup_steps=$WARMUPS \
-    --num_inter=$INTER_THREADS \
-    --num_intra=$INTRA_THREADS
-```
-
-Where (Default values are shown in the square brackets):
-   * $INPUT_MODEL ["./distilbert_base_fp32.pb"]-- The path to input FP32 frozen model .pb file to load
-   * $DATASET_DIR ["./sst2_validation_dataset"]-- The path to input dataset directory
-   * $OUTPUT_MODEL ["./output_distilbert_base_int8.pb"]-- The user-specified export path to the output INT8 quantized model
-   * $BATCH_SIZE [128]-- The batch size for model inference
-   * $MAX_SEQ [128]-- The maximum total sequence length after tokenization
-   * $ITERS [872]-- The number of iterations to run in benchmark mode, maximum value is 872
-   * $WARMUPS [10]-- The number of warmup steps before benchmarking the model, maximum value is 22
-   * $INTER_THREADS [2]-- The number of inter op parallelism thread to use, which can be set to the number of sockets
-   * $INTRA_THREADS [28]-- The number of intra op parallelism thread to use, which can be set to the number of physical core per socket
-
-
-### Run Smooth Quant to improve int8 accuracy
-
-#### Tuning
-```shell
-bash run_quant.sh \
-    --input_model=$INPUT_MODEL \
-    --dataset_location=$DATASET_DIR \
-    --output_model=$OUTPUT_MODEL \
-    --batch_size=$BATCH_SIZE \
-    --max_seq_length=$MAX_SEQ \
-    --warmup_steps=$WARMUPS \
-    --num_inter=$INTER_THREADS \
-    --num_intra=$INTRA_THREADS \
-    --sq=True
-```
-
-
-Details of enabling Intel® Neural Compressor on DistilBERT base for TensorFlow
-=========================
-
-This is a tutorial of how to enable DistilBERT base model with Intel® Neural Compressor.
-## User Code Analysis
-1. User specifies fp32 *model*, calibration dataloader *q_dataloader*, evaluation dataloader *eval_dataloader* and metric.
-
-2. User specifies fp32 *model*, calibration dataloader *q_dataloader* and a custom *eval_func* which encapsulates the evaluation dataloader and metric by itself.
-
-For DistilBERT base, we applied the latter one. The task is to implement the *q_dataloader* and *eval_func*.
-
-
-### q_dataloader Part Adaption
-Below dataloader class uses generator function to provide the model with input.
-
-```python
-class Dataloader(object):
-    def __init__(self, data_location, batch_size, steps):
-        self.batch_size = batch_size
-        self.data_location = data_location
-        self.num_batch = math.ceil(steps / batch_size)
-
-    def __iter__(self):
-        return self.generate_dataloader(self.data_location).__iter__()
-
-    def __len__(self):
-        return self.num_batch
-
-    def generate_dataloader(self, data_location):
-        dataset = load_dataset(data_location)
-        for batch_id in range(self.num_batch):
-            feed_dict, labels = create_feed_dict_and_labels(dataset, batch_id, self.num_batch)
-            yield feed_dict, labels
-```
-
-### Code Update
-After prepare step is done, we add the code for quantization tuning to generate quantized model.
-
-#### Tune
-```python
-    from neural_compressor.tensorflow import StaticQuantConfig, SmoothQuantConfig, quantize_model
-
-    quant_config = SmoothQuantConfig(alpha=0.6) if ARGS.sq else StaticQuantConfig()
-    q_model = quantize_model(graph, quant_config, self.dataloader)
-    try:
-        q_model.save(ARGS.output_graph)
-    except Exception as e:
-        tf.compat.v1.logging.error("Failed to save model due to {}".format(str(e)))
-```
-#### Benchmark
-```python
-    if ARGS.mode == 'performance':
-        self.eval_func(graph)
-    elif ARGS.mode == 'accuracy':
-        accuracy = self.eval_func(graph)
-        logger.info("Accuracy: {:.4f}".format(accuracy))
-```
diff --git a/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/download_dataset.py b/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/download_dataset.py
deleted file mode 100644
index b92cf39874f..00000000000
--- a/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/download_dataset.py
+++ /dev/null
@@ -1,38 +0,0 @@
-#
-# -*- coding: utf-8 -*-
-#
-# Copyright (c) 2022 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-''' 
-Script to download and save dataset
-'''
-from datasets import load_dataset
-from argparse import ArgumentParser
-import os
-
-def main():
-    arg_parser = ArgumentParser(description="Download and save dataset")
-    arg_parser.add_argument("-p", "--path_to_save_dataset", type=str,
-                             help="path to save the dataset",
-                             default="./")
-    args = arg_parser.parse_args()
-    dataset = load_dataset("glue", "sst2", split= "validation")
-    path = os.path.join(args.path_to_save_dataset, "sst2_validation_dataset")
-    dataset.save_to_disk(path)
-    print("Dataset saved in location: {}".format(path))
-
-if __name__ == "__main__":
-    main()
diff --git a/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/main.py b/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/main.py
deleted file mode 100644
index e0fce11736e..00000000000
--- a/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/main.py
+++ /dev/null
@@ -1,324 +0,0 @@
-
-#
-# -*- coding: utf-8 -*-
-#
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-'''DistilBERT base inference, implementation adapted from Hugging Face Library https://huggingface.co/'''
-import time
-import os
-import math
-
-import tensorflow as tf
-import numpy as np
-
-from argparse import ArgumentParser
-from transformers import AutoTokenizer
-from datasets import load_from_disk
-from tensorflow.core.protobuf import saved_model_pb2
-from tensorflow.python.client import timeline
-
-from neural_compressor.common import logger
-from neural_compressor.tensorflow.utils import dump_elapsed_time
-
-
-
-def boolean_string(s):
-    if s not in {'False', 'True'}:
-        raise ValueError('Not a valid boolean string')
-    return s == 'True'
-
-arg_parser = ArgumentParser(description="Distilbert inference")
-arg_parser.add_argument("--task-name", type=str,
-                        help="Name of the task to run benchmark.",
-                        dest="task_name",
-                        default="sst2"
-                        )
-arg_parser.add_argument("-c", "--config", type=str,
-                        help="Quantization configuration file to load.",
-                        dest="config",
-                        default="distilbert_base.yaml"
-                        )
-arg_parser.add_argument("-g", "--in-graph", type=str,
-                        help="Full path to the input graph.",
-                        dest="input_graph",
-                        default=None
-                        )
-arg_parser.add_argument("--data-location", type=str,
-                        help="Path to the dataset.",
-                        dest="data_location"
-                        )
-arg_parser.add_argument("-o", "--output-graph", type=str,
-                        help="The output path of quantized graph.",
-                        dest="output_graph",
-                        default="output_distilbert_base_int8.pb"
-                        )
-arg_parser.add_argument("-m", "--mode", type=str,
-                        choices=['performance', 'accuracy'],
-                        help="One of two options: 'performance'/'accuracy'.",
-                        dest="mode",
-                        default="performance"
-                        )
-arg_parser.add_argument("--tune", type=boolean_string,
-                        help="whether to apply quantization",
-                        dest="tune",
-                        default=False
-                        )
-arg_parser.add_argument('--sq', type=boolean_string, dest='sq', help='smooth quantization', default=False)
-arg_parser.add_argument("--benchmark", type=boolean_string,
-                        help="whether to do benchmark",
-                        dest="benchmark",
-                        default=False
-                        )
-arg_parser.add_argument('-e', "--num-inter-threads", type=int,
-                        help="The number of inter-thread.",
-                        dest="num_inter_threads",
-                        default=2
-                        )
-arg_parser.add_argument('-a', "--num-intra-threads", type=int,
-                        help="The number of intra-thread.",
-                        dest="num_intra_threads",
-                        default=28
-                        )
-arg_parser.add_argument("--pad-to-max-length", type=boolean_string,
-                        help="Padding option.",
-                        dest="pad_to_max_length",
-                        default=True
-                        )
-arg_parser.add_argument("--warmup-steps", type=int,
-                        help="Number of warmup steps.",
-                        dest="warmup_steps",
-                        default=10
-                        )
-arg_parser.add_argument("--max-seq-length", type=int,
-                        help="Maximum total sequence length after tokenization.",
-                        dest="max_seq_length",
-                        default=128
-                        )
-arg_parser.add_argument("--steps", type=int,
-                        help="Number of steps.",
-                        dest="steps",
-                        default=872
-                        )
-arg_parser.add_argument("--batch-size", type=int,
-                        help="Inference batch-size.",
-                        dest="batch_size",
-                        default=128
-                        )
-arg_parser.add_argument("--profile", dest='profile',
-                        type=boolean_string, help="profile",
-                        default=False)
-
-ARGS = arg_parser.parse_args()
-MAX_STEPS = 872
-MAX_WARMUP_STEPS = 22
-
-def create_feed_dict_and_labels(dataset, batch_id= None, num_batch= None, idx= None):
-    """Return the input dictionary for the given batch."""
-    if idx is None:
-        start_idx = batch_id * ARGS.batch_size
-        if batch_id == num_batch - 1:
-            end_idx = ARGS.steps
-        else:
-            end_idx = start_idx + ARGS.batch_size
-        input_ids = np.array(dataset["input_ids"])[start_idx:end_idx, :]
-        attention_mask = np.array(dataset["attention_mask"])[start_idx:end_idx, :]
-        feed_dict = {"input_ids:0": input_ids,
-                     "attention_mask:0": attention_mask,
-        }
-        labels = np.array(dataset["label"])[start_idx: end_idx]
-    else:
-        input_ids = np.array(dataset["input_ids"])[idx, :].reshape(1, -1)
-        attention_mask = np.array(dataset["attention_mask"])[idx, :].reshape(1, -1)
-        feed_dict = {"input_ids:0": input_ids,
-                     "attention_mask:0": attention_mask,
-        }
-        labels = np.array(dataset["label"])[idx]
-    return feed_dict, labels
-
-def load_dataset(data_location):
-    def preprocess_function(examples):
-        """Tokenize the texts."""
-        sentence1_key, sentence2_key = "sentence", None
-        args = (
-            (examples[sentence1_key],) if sentence2_key is None
-            else (examples[sentence1_key], examples[sentence2_key])
-        )
-        result = tokenizer(*args, padding="max_length",
-                            max_length=ARGS.max_seq_length,
-                            truncation=True
-        )
-        return result
-
-    # Load dataset (only validation split for inference)
-    dataset = load_from_disk(data_location)
-    # Load tokenizer
-    tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
-    # Set max sequence length
-    if ARGS.max_seq_length > tokenizer.model_max_length:
-        logger.info(f"The max sequence length passed ({ARGS.max_seq_length}) \
-                    is larger than the max supported by model \
-                   ({tokenizer.model_max_length}).Using max_seq_length =  \
-                   {tokenizer.model_max_length}")
-    ARGS.max_seq_length = min(ARGS.max_seq_length, tokenizer.model_max_length)
-    # Tokenize the dataset
-    dataset = dataset.map(preprocess_function, batched=True)
-    return dataset
-
-class Dataloader(object):
-    def __init__(self, data_location, batch_size, steps):
-        self.batch_size = batch_size
-        self.data_location = data_location
-        self.num_batch = math.ceil(steps / batch_size)
-
-    def __iter__(self):
-        return self.generate_dataloader(self.data_location).__iter__()
-
-    def __len__(self):
-        return self.num_batch
-
-    def generate_dataloader(self, data_location):
-        dataset = load_dataset(data_location)
-        for batch_id in range(self.num_batch):
-            feed_dict, labels = create_feed_dict_and_labels(dataset, batch_id, self.num_batch)
-            yield feed_dict, labels
-
-class Distilbert_base(object):
-    def __init__(self):
-        self.validate_args()
-        self.dataset = load_dataset(ARGS.data_location)
-        self.dataloader = Dataloader(ARGS.data_location, ARGS.batch_size, ARGS.steps)
-
-    def validate_args(self):
-        if ARGS.warmup_steps > MAX_WARMUP_STEPS:
-            logger.warning("Warmup steps greater than max possible value of 22." + \
-                           " Setting to max value of ", MAX_WARMUP_STEPS)
-            ARGS.warmup_steps = MAX_WARMUP_STEPS
-        if ARGS.tune or ARGS.sq or (ARGS.benchmark and ARGS.mode == "accuracy"):
-            ARGS.steps = MAX_STEPS
-        elif ARGS.benchmark:
-            if ARGS.steps > (MAX_STEPS - MAX_WARMUP_STEPS):
-                logger.warning("Steps greater than max possible value of {}.".format(MAX_STEPS - MAX_WARMUP_STEPS))
-                logger.warning("Setting to max value of {}".format(MAX_STEPS - MAX_WARMUP_STEPS))
-                ARGS.steps = MAX_STEPS - MAX_WARMUP_STEPS
-        if not ARGS.data_location:
-            raise SystemExit("Missing dataset path.")
-
-    def load_graph(self):
-        """Load the frozen model."""
-        graph_def = tf.compat.v1.GraphDef()
-        sm = saved_model_pb2.SavedModel()
-        with tf.io.gfile.GFile(ARGS.input_graph, "rb") as f:
-            try:
-                content = f.read()
-                graph_def.ParseFromString(content)
-            except Exception:
-                sm.ParseFromString(content)
-                graph_def = sm.meta_graphs[0].graph_def
-        with tf.Graph().as_default() as graph:
-            tf.import_graph_def(graph_def, name="")
-        logger.info("Loaded graph from: " + ARGS.input_graph)
-        return graph
-
-    def get_correct_predictions(self, preds, label_ids):
-        """Evaluate the predictions.
-
-        return the total number of correct predictions.
-        """
-        preds = np.argmax(preds, axis=1)
-        correct_preds = 0
-        for pred, label in zip(preds, label_ids):
-            if pred == label:
-                correct_preds += 1
-        return correct_preds
-
-    @dump_elapsed_time(customized_msg="Customized eval_func")
-    def eval_func(self, graph):
-        # Set the config for running
-        config = tf.compat.v1.ConfigProto()
-        config.intra_op_parallelism_threads=ARGS.num_intra_threads
-        config.inter_op_parallelism_threads=ARGS.num_inter_threads
-        run_options = tf.compat.v1.RunOptions(trace_level=tf.compat.v1.RunOptions.FULL_TRACE)
-        run_metadata = tf.compat.v1.RunMetadata()
-
-        output = graph.get_tensor_by_name('Identity:0')
-        total_time = 0
-        accuracy = 0
-        logger.info("Started warmup for {} steps...".format(ARGS.warmup_steps))
-        start_step_idx = MAX_STEPS - MAX_WARMUP_STEPS
-        with tf.compat.v1.Session(graph=graph, config=config) as sess:
-            # Warm up
-            for step in range(start_step_idx, start_step_idx + ARGS.warmup_steps):
-                feed_dict, _ = create_feed_dict_and_labels(self.dataset, idx=step)
-                _ = sess.run(output, feed_dict= feed_dict)
-            logger.info("Warmup completed.")
-            # Inference
-            logger.info("Starting inference for {} steps...".format(ARGS.steps))
-            total_correct_predictions = 0
-            iter = 0
-            for feed_dict, labels in self.dataloader:
-                iter += 1
-                start_time = time.time()
-                if ARGS.profile:
-                    pred = sess.run(output, feed_dict=feed_dict, options=run_options, run_metadata=run_metadata)
-                else:
-                    pred = sess.run(output, feed_dict=feed_dict)
-                run_time = time.time() - start_time
-                if ARGS.tune or ARGS.sq or (ARGS.benchmark and ARGS.mode == "accuracy"):
-                    total_correct_predictions += self.get_correct_predictions(pred, labels)
-                total_time += run_time
-                # save profiling file
-                if ARGS.profile and iter == int(self.dataloader.num_batch / 2):
-                        trace = timeline.Timeline(step_stats=run_metadata.step_stats)
-                        model_dir = str(os.path.dirname(os.path.realpath(__file__))) + '/timeline'
-                        if not os.path.exists(model_dir):
-                            try:
-                                os.makedirs(model_dir)
-                            except:
-                                pass
-                        profiling_file = model_dir + '/timeline-' + str(iter + 1) + '-' + str(os.getpid()) + '.json'
-                        with open(profiling_file, 'w') as trace_file:
-                            trace_file.write(trace.generate_chrome_trace_format(show_memory=False))
-        time_per_batch = total_time / float(ARGS.steps / ARGS.batch_size)
-        accuracy = total_correct_predictions / ARGS.steps
-        if ARGS.benchmark and ARGS.mode == 'performance':
-            logger.info("Latency: {:.4f} ms".format(time_per_batch * 1000))
-            logger.info("Throughput: {:.4f} sentences/sec".format(self.dataloader.batch_size / time_per_batch))
-        return accuracy
-
-    def run(self):
-        graph = self.load_graph()
-        if ARGS.tune or ARGS.sq:
-            from neural_compressor.tensorflow import StaticQuantConfig, SmoothQuantConfig, quantize_model
-
-            quant_config = SmoothQuantConfig(alpha=0.6) if ARGS.sq else StaticQuantConfig()
-            q_model = quantize_model(graph, quant_config, self.dataloader)
-            try:
-                q_model.save(ARGS.output_graph)
-            except Exception as e:
-                tf.compat.v1.logging.error("Failed to save model due to {}".format(str(e)))
-        elif ARGS.benchmark:
-            assert ARGS.mode == 'performance' or ARGS.mode == 'accuracy', \
-            "Benchmark only supports performance or accuracy mode."
-            if ARGS.mode == 'performance':
-                self.eval_func(graph)
-            elif ARGS.mode == 'accuracy':
-                accuracy = self.eval_func(graph)
-                logger.info("Accuracy: {:.4f}".format(accuracy))
-
-if __name__ == "__main__":
-    distilbert_ob = Distilbert_base()
-    distilbert_ob.run()
diff --git a/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/requirements.txt b/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/requirements.txt
deleted file mode 100644
index e60cdb323dd..00000000000
--- a/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/requirements.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-datasets>=2.6.1
-scikit-learn>=1.1.2
-scipy>=1.9.3
-sklearn==0.0
-tokenizers==0.13.1
-transformers>=4.31.0
-intel-tensorflow>=2.12.0
diff --git a/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/run_benchmark.sh b/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/run_benchmark.sh
deleted file mode 100644
index fed5b300182..00000000000
--- a/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/run_benchmark.sh
+++ /dev/null
@@ -1,88 +0,0 @@
-#!/bin/bash
-# set -x
-
-function main {
-
-  init_params "$@"
-
-  run_benchmark
-
-}
-
-# init params
-function init_params {
-  # set default value
-  input_model="./distilbert_base_fp32.pb"
-  dataset_location="./sst2_validation_dataset"
-  mode="performance"
-  batch_size=128
-  max_seq_length=128
-  iters=872
-  warmup_steps=10
-  num_inter=2
-  num_intra=28
-  benchmark=True
-  profile=False
-
-  for var in "$@"
-  do
-    case $var in
-      --input_model=*)
-          input_model=$(echo "$var" |cut -f2 -d=)
-      ;;
-      --dataset_location=*)
-          dataset_location=$(echo "$var" |cut -f2 -d=)
-      ;;
-      --mode=*)
-          mode=$(echo $var |cut -f2 -d=)
-      ;;
-      --batch_size=*)
-          batch_size=$(echo $var |cut -f2 -d=)
-      ;;
-      --max_seq_length=*)
-          max_seq_length=$(echo ${var} |cut -f2 -d=)
-      ;;
-      --iters=*)
-          iters=$(echo ${var} |cut -f2 -d=)
-      ;;
-      --warmup_steps=*)
-          warmup_steps=$(echo ${var} |cut -f2 -d=)
-      ;;
-      --num_inter=*)
-         num_inter=$(echo ${var} |cut -f2 -d=)
-      ;;
-      --num_intra=*)
-         num_intra=$(echo ${var} |cut -f2 -d=)
-      ;;
-      --benchmark=*)
-         benchmark=$(echo ${var} |cut -f2 -d=)
-      ;;
-      --profile=*)
-         profile=$(echo ${var} |cut -f2 -d=)
-      ;;
-    esac
-  done
-
-}
-
-# run_benchmark
-function run_benchmark {
-    cmd="
-        python main.py \
-            --in-graph=${input_model} \
-            --data-location=${dataset_location} \
-            --benchmark=${benchmark} \
-            --profile=${profile} \
-            --mode=${mode} \
-            --steps=${iters} \
-            --warmup-steps=${warmup_steps} \
-            --batch-size=${batch_size} \
-            --max-seq-length=${max_seq_length} \
-            --num-inter-threads=${num_inter} \
-            --num-intra-threads=${num_intra}
-        "
-    echo $cmd
-    eval $cmd
-}
-
-main "$@"
diff --git a/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/run_quant.sh b/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/run_quant.sh
deleted file mode 100644
index 31571c5ff4c..00000000000
--- a/examples/3.x_api/tensorflow/nlp/distilbert_base/quantization/ptq/run_quant.sh
+++ /dev/null
@@ -1,83 +0,0 @@
-#!/bin/bash
-# set -x
-
-function main {
-
-  init_params "$@"
-
-  run_tuning
-
-}
-
-# init params
-function init_params {
-  # set default value
-  input_model="./distilbert_base_fp32.pb"
-  dataset_location="./sst2_validation_dataset"
-  output_model="./output_distilbert_base_int8.pb"
-  batch_size=128
-  max_seq_length=128
-  warmup_steps=10
-  num_inter=2
-  num_intra=28
-  tune=True
-  sq=False
-
-  for var in "$@"
-  do
-    case $var in
-      --input_model=*)
-          input_model=$(echo ${var} |cut -f2 -d=)
-      ;;
-      --dataset_location=*)
-          dataset_location=$(echo ${var} |cut -f2 -d=)
-      ;;
-      --output_model=*)
-          output_model=$(echo ${var} |cut -f2 -d=)
-      ;;
-      --batch_size=*)
-          batch_size=$(echo ${var} |cut -f2 -d=)
-      ;;
-      --max_seq_length=*)
-          max_seq_length=$(echo ${var} |cut -f2 -d=)
-      ;;
-      --warmup_steps=*)
-          warmup_steps=$(echo ${var} |cut -f2 -d=)
-      ;;
-      --num_inter=*)
-         num_inter=$(echo ${var} |cut -f2 -d=)
-      ;;
-      --num_intra=*)
-         num_intra=$(echo ${var} |cut -f2 -d=)
-      ;;
-      --tune=*)
-         tune=$(echo ${var} |cut -f2 -d=)
-      ;;
-      --sq=*)
-         sq=$(echo ${var} |cut -f2 -d=)
-      ;;
-    esac
-  done
-
-}
-
-# run_tuning
-function run_tuning {
-    cmd="
-        python main.py \
-            --in-graph=${input_model} \
-            --data-location=${dataset_location} \
-            --output-graph=${output_model} \
-            --tune=${tune} \
-            --sq=${sq} \
-            --warmup-steps=${warmup_steps} \
-            --batch-size=${batch_size} \
-            --max-seq-length=${max_seq_length} \
-            --num-inter-threads=${num_inter} \
-            --num-intra-threads=${num_intra}
-        "
-    echo $cmd
-    eval $cmd
-}
-
-main "$@"
diff --git a/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/README.md b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/README.md
index e4618e7604c..4307ec85480 100644
--- a/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/README.md
+++ b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/README.md
@@ -71,6 +71,6 @@ Please set the following environment variables before running quantization or be
 
 ## Benchmark
 
-`bash run_benchmark.sh --input_model=3dunet_dynamic_ndhwc_int8.pb --dataset_location=<path/to/build>/build --batch_size=100 --mode=benchmark`
+`bash run_benchmark.sh --input_model=3dunet_dynamic_ndhwc_int8.pb --dataset_location=<path/to/build>/build --batch_size=100 --iters=500 --mode=benchmark`
 
 `bash run_benchmark.sh --input_model=3dunet_dynamic_ndhwc_int8.pb --dataset_location=<path/to/build>/build --batch_size=1 --mode=accuracy`
diff --git a/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/run_benchmark.sh b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/run_benchmark.sh
index 7e869e7cca7..36f8d8502f0 100644
--- a/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/run_benchmark.sh
+++ b/examples/3.x_api/tensorflow/semantic_image_segmentation/3dunet-mlperf/quantization/ptq/run_benchmark.sh
@@ -31,6 +31,9 @@ function init_params {
       --batch_size=*)
           batch_size=$(echo $var |cut -f2 -d=)
       ;;
+      --iters=*)
+          iters=$(echo $var |cut -f2 -d=)
+      ;;
       *)
           echo "Error: No such parameter: ${var}"
           exit 1
@@ -55,6 +58,7 @@ function run_benchmark {
       --calib-preprocess=${BUILD_DIR}/calib_preprocess \
       --batch-size=${batch_size} \
       --mode=${mode} \
+      --iters=${iters} \
       ${extra_cmd}
 }
 
diff --git a/neural_compressor/tensorflow/utils/model_wrappers.py b/neural_compressor/tensorflow/utils/model_wrappers.py
index e1a58f2f53b..baeaa746914 100644
--- a/neural_compressor/tensorflow/utils/model_wrappers.py
+++ b/neural_compressor/tensorflow/utils/model_wrappers.py
@@ -1429,6 +1429,7 @@ def graph_info(self):
     def save(self, root, *args, **kwargs):
         """Save Keras model."""
         self._model_object.save(root)
+        logger.info("Save quantized model to {}.".format(root))
 
     @property
     def input_node_names(self):

From 5f50a34b18eefb67d5a43f529f1000201b212168 Mon Sep 17 00:00:00 2001
From: zehao-intel <zehao.huang@intel.com>
Date: Tue, 16 Jul 2024 14:35:48 +0800
Subject: [PATCH 10/14] add save log for keras

Signed-off-by: zehao-intel <zehao.huang@intel.com>
---
 .../tensorflow/keras/cv/inception_v3/quantization/ptq/main.py   | 2 +-
 .../tensorflow/keras/cv/mobilenet_v2/quantization/ptq/main.py   | 1 +
 .../tensorflow/keras/cv/resnet_v2_50/quantization/ptq/main.py   | 1 +
 3 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/main.py b/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/main.py
index abccf02d8f5..5f8b08d6e11 100644
--- a/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/main.py
+++ b/examples/3.x_api/tensorflow/keras/cv/inception_v3/quantization/ptq/main.py
@@ -126,7 +126,7 @@ def main(_):
         quant_config = StaticQuantConfig()
         q_model = quantize_model(FLAGS.input_model, quant_config, calib_dataloader)
         q_model.save(FLAGS.output_model)
-        
+        logger.info("Save quantized model to {}.".format(FLAGS.output_model))
 
     if FLAGS.benchmark:
         from neural_compressor.tensorflow import Model
diff --git a/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/main.py b/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/main.py
index b577b24892f..804dd81ef5a 100644
--- a/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/main.py
+++ b/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/main.py
@@ -126,6 +126,7 @@ def main(_):
         quant_config = StaticQuantConfig()
         q_model = quantize_model(FLAGS.input_model, quant_config, calib_dataloader)
         q_model.save(FLAGS.output_model)
+        logger.info("Save quantized model to {}.".format(FLAGS.output_model))
 
     if FLAGS.benchmark:
         from neural_compressor.tensorflow import Model
diff --git a/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/main.py b/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/main.py
index 48f30a79fb4..7fc6a2cdf10 100644
--- a/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/main.py
+++ b/examples/3.x_api/tensorflow/keras/cv/resnet_v2_50/quantization/ptq/main.py
@@ -126,6 +126,7 @@ def main(_):
         quant_config = StaticQuantConfig()
         q_model = quantize_model(FLAGS.input_model, quant_config, calib_dataloader)
         q_model.save(FLAGS.output_model)
+        logger.info("Save quantized model to {}.".format(FLAGS.output_model))
 
     if FLAGS.benchmark:
         from neural_compressor.tensorflow import Model

From cb40371719f81a1253a6a4acaf266a79b6734b33 Mon Sep 17 00:00:00 2001
From: zehao-intel <zehao.huang@intel.com>
Date: Thu, 18 Jul 2024 15:43:23 +0800
Subject: [PATCH 11/14] fix accuracy using per-channel

Signed-off-by: zehao-intel <zehao.huang@intel.com>
---
 .../tensorflow/cv/densenet121/quantization/ptq/main.py    | 2 +-
 .../tensorflow/cv/mobilenet_v2/quantization/ptq/main.py   | 2 +-
 .../faster_rcnn_resnet50/quantization/ptq/README.md       | 7 +++++--
 .../faster_rcnn_resnet50/quantization/ptq/main.py         | 2 +-
 .../mask_rcnn_inception_v2/quantization/ptq/README.md     | 6 +++---
 .../mask_rcnn_inception_v2/quantization/ptq/main.py       | 2 +-
 .../ssd_mobilenet_v1/quantization/ptq/README.md           | 8 +++++---
 .../ssd_mobilenet_v1/quantization/ptq/main.py             | 2 +-
 neural_compressor/tensorflow/quantization/config.py       | 4 ++--
 9 files changed, 20 insertions(+), 15 deletions(-)

diff --git a/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/main.py b/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/main.py
index 21ca3bcb3d8..ed2013159d6 100644
--- a/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/main.py
+++ b/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/main.py
@@ -116,7 +116,7 @@ def run(self):
             )
             calib_dataloader = TFDataLoader(dataset=dataset, batch_size=10)
             
-            quant_config = StaticQuantConfig()
+            quant_config = StaticQuantConfig(weight_granularity="per_channel")
             q_model = quantize_model(args.input_graph, quant_config, calib_dataloader)
             q_model.save(args.output_graph)
 
diff --git a/examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/main.py b/examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/main.py
index d22a9bd4494..fd3a07937de 100644
--- a/examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/main.py
+++ b/examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/main.py
@@ -112,7 +112,7 @@ def run(self):
             )
             calib_dataloader = TFDataLoader(dataset=dataset, batch_size=10)
 
-            quant_config = StaticQuantConfig()
+            quant_config = StaticQuantConfig(weight_granularity="per_channel")
             q_model = quantize_model(args.input_graph, quant_config, calib_dataloader)
             q_model.save(args.output_graph)
 
diff --git a/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/README.md b/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/README.md
index a2c4fbfbbb8..b7b90b6f8ec 100644
--- a/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/README.md
+++ b/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/README.md
@@ -86,13 +86,16 @@ Download CoCo Dataset from [Official Website](https://cocodataset.org/#download)
 ## 1. Quantization
   
   ```shell
-  # The cmd of running faster_rcnn_resnet50
   bash run_quant.sh --input_model=./faster_rcnn_resnet50_fp32_coco_pretrained_model/frozen_inference_graph.pb --output_model=./tensorflow-faster_rcnn_resnet50-tune.pb --dataset_location=/path/to/dataset/coco_val.record
   ```
 
 ## 2. Benchmark
   ```shell
+  # run performance benchmark
   bash run_benchmark.sh --input_model=./tensorflow-faster_rcnn_resnet50-tune.pb  --dataset_location=/path/to/dataset/coco_val.record --mode=performance
+
+  # run accuracy benchmark
+  bash run_benchmark.sh --input_model=./tensorflow-faster_rcnn_resnet50-tune.pb  --dataset_location=/path/to/dataset/coco_val.record --mode=accuracy
   ```
 
 Details of enabling Intel® Neural Compressor on faster_rcnn_resnet50 for Tensorflow.
@@ -111,7 +114,7 @@ After prepare step is done, we just need update main.py like below.
     if args.tune:
         from neural_compressor.tensorflow import StaticQuantConfig, quantize_model, Model
 
-        quant_config = StaticQuantConfig()
+        quant_config = StaticQuantConfig(weight_granularity="per_channel")
         model = Model(args.input_graph)
         model.input_tensor_names = ['image_tensor']
         model.output_tensor_names = ["num_detections", "detection_boxes", "detection_scores", "detection_classes"]
diff --git a/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/main.py b/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/main.py
index 2493297e476..0ca37671fd6 100644
--- a/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/main.py
+++ b/examples/3.x_api/tensorflow/object_detection/faster_rcnn_resnet50/quantization/ptq/main.py
@@ -109,7 +109,7 @@ def main(_):
     if args.tune:
         from neural_compressor.tensorflow import StaticQuantConfig, quantize_model, Model
 
-        quant_config = StaticQuantConfig()
+        quant_config = StaticQuantConfig(weight_granularity="per_channel")
         model = Model(args.input_graph)
         model.input_tensor_names = ['image_tensor']
         model.output_tensor_names = ["num_detections", "detection_boxes", "detection_scores", "detection_classes"]
diff --git a/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/README.md b/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/README.md
index c672d5e6148..9ec8ae2ad78 100644
--- a/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/README.md
+++ b/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/README.md
@@ -89,21 +89,21 @@ Now we support both pb and ckpt formats.
 ### For PB format
   
   ```shell
-  # The cmd of running mask_rcnn_inception_v2
   bash run_quant.sh --input_model=./mask_rcnn_inception_v2_coco_2018_01_28/frozen_inference_graph.pb --output_model=./tensorflow-mask_rcnn_inception_v2-tune.pb --dataset_location=/path/to/dataset/coco_val.record
   ```
 
 ### For ckpt format
   
   ```shell
-  # The cmd of running mask_rcnn_inception_v2
   bash run_quant.sh --input_model=./mask_rcnn_inception_v2_coco_2018_01_28/ --output_model=./tensorflow-mask_rcnn_inception_v2-tune.pb --dataset_location=/path/to/dataset/coco_val.record
   ```
 
 ## 2. Benchmark
   ```shell
+  # run performance benchmark
   bash run_benchmark.sh --input_model=./tensorflow-mask_rcnn_inception_v2-tune.pb  --dataset_location=/path/to/dataset/coco_val.record --mode=performance
 
+  # run accuracy benchmark
   bash run_benchmark.sh --input_model=./tensorflow-mask_rcnn_inception_v2-tune.pb  --dataset_location=/path/to/dataset/coco_val.record --mode=accuracy
   ```
 
@@ -123,7 +123,7 @@ After prepare step is done, we just need update main.py like below.
     if args.tune:
         from neural_compressor.tensorflow import StaticQuantConfig, quantize_model, Model
 
-        quant_config = StaticQuantConfig()
+        quant_config = StaticQuantConfig(weight_granularity="per_channel")
         model = Model(args.input_graph)
         model.input_tensor_names = ['image_tensor']
         model.output_tensor_names = ["num_detections", "detection_boxes", "detection_scores", "detection_classes"]
diff --git a/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/main.py b/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/main.py
index 06498ca56e9..632d66ac25a 100644
--- a/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/main.py
+++ b/examples/3.x_api/tensorflow/object_detection/mask_rcnn_inception_v2/quantization/ptq/main.py
@@ -114,7 +114,7 @@ def main(_):
     if args.tune:
         from neural_compressor.tensorflow import StaticQuantConfig, quantize_model, Model
 
-        quant_config = StaticQuantConfig()
+        quant_config = StaticQuantConfig(weight_granularity="per_channel")
         model = Model(args.input_graph)
         model.input_tensor_names = ['image_tensor']
         model.output_tensor_names = ["num_detections", "detection_boxes", "detection_scores", "detection_classes"]
diff --git a/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/README.md b/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/README.md
index 43026b9002d..1b52ecf8b17 100644
--- a/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/README.md
+++ b/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/README.md
@@ -107,20 +107,22 @@ Now we support both pb and ckpt formats.
 ### For PB format
   
   ```shell
-  # The cmd of running ssd_mobilenet_v1
   bash run_quant.sh --input_model=./ssd_mobilenet_v1_coco_2018_01_28/frozen_inference_graph.pb --output_model=./tensorflow-ssd_mobilenet_v1-tune.pb --dataset_location=/path/to/dataset/coco_val.record
   ```
 
 ### For ckpt format
   
   ```shell
-  # The cmd of running ssd_mobilenet_v1
   bash run_quant.sh --input_model=./ssd_mobilenet_v1_coco_2018_01_28/ --output_model=./tensorflow-ssd_mobilenet_v1-tune.pb --dataset_location=/path/to/dataset/coco_val.record
   ```
 
 ## 2. Benchmark
   ```shell
+  # run performance benchmark
   bash run_benchmark.sh --input_model=./tensorflow-ssd_mobilenet_v1-tune.pb  --dataset_location=/path/to/dataset/coco_val.record --mode=performance
+
+  # run accuracy benchmark
+  bash run_benchmark.sh --input_model=./tensorflow-ssd_mobilenet_v1-tune.pb  --dataset_location=/path/to/dataset/coco_val.record --mode=accuracy
   ```
 
 Details of enabling Intel® Neural Compressor on ssd_mobilenet_v1 for Tensorflow.
@@ -139,7 +141,7 @@ After prepare step is done, we just need update main.py like below.
     if args.tune:
         from neural_compressor.tensorflow import StaticQuantConfig, quantize_model, Model
 
-        quant_config = StaticQuantConfig()
+        quant_config = StaticQuantConfig(weight_granularity="per_channel")
         model = Model(args.input_graph)
         model.input_tensor_names = ['image_tensor']
         model.output_tensor_names = ["num_detections", "detection_boxes", "detection_scores", "detection_classes"]
diff --git a/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/main.py b/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/main.py
index cd9f943d374..dbced65f2d7 100644
--- a/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/main.py
+++ b/examples/3.x_api/tensorflow/object_detection/ssd_mobilenet_v1/quantization/ptq/main.py
@@ -110,7 +110,7 @@ def main(_):
     if args.tune:
         from neural_compressor.tensorflow import StaticQuantConfig, quantize_model, Model
 
-        quant_config = StaticQuantConfig()
+        quant_config = StaticQuantConfig(weight_granularity="per_channel")
         model = Model(args.input_graph)
         model.input_tensor_names = ['image_tensor']
         model.output_tensor_names = ["num_detections", "detection_boxes", "detection_scores", "detection_classes"]
diff --git a/neural_compressor/tensorflow/quantization/config.py b/neural_compressor/tensorflow/quantization/config.py
index 752f8d4ecbe..c7b2d5b0549 100644
--- a/neural_compressor/tensorflow/quantization/config.py
+++ b/neural_compressor/tensorflow/quantization/config.py
@@ -113,7 +113,7 @@ def register_supported_configs(cls) -> List[OperatorConfig]:
             weight_algorithm=["minmax", "kl"],
             act_dtype=["int8", "bf16", "fp32"],
             act_sym=[True, False],
-            act_granularity=["per_tensor", "per_channel"],
+            act_granularity=["per_tensor"],
             act_algorithm=["minmax", "kl"],
         )
         operators = [
@@ -173,7 +173,7 @@ def get_config_set_for_tuning(cls) -> Union[None, "StaticQuantConfig", List["Sta
             weight_algorithm=["minmax", "kl"],
             act_dtype=["int8", "fp32"],
             act_sym=[True, False],
-            act_granularity=["per_tensor", "per_channel"],
+            act_granularity=["per_tensor"],
             act_algorithm=["minmax", "kl"],
         )
 

From 5307820c408498efb2ad8302dcc4ec073b1d8ea0 Mon Sep 17 00:00:00 2001
From: zehao-intel <zehao.huang@intel.com>
Date: Tue, 23 Jul 2024 14:03:31 +0800
Subject: [PATCH 12/14] fix keras accuracy

Signed-off-by: zehao-intel <zehao.huang@intel.com>
---
 .../mobilenet_v2/quantization/ptq/README.md   |  64 ---
 .../quantization/ptq/data_process.py          | 543 ------------------
 .../cv/mobilenet_v2/quantization/ptq/main.py  | 143 -----
 .../quantization/ptq/prepare_model.py         |  35 --
 .../quantization/ptq/requirements.txt         |   2 -
 .../quantization/ptq/run_benchmark.sh         |  51 --
 .../quantization/ptq/run_quant.sh             |  40 --
 .../algorithms/static_quant/keras.py          |   7 +-
 8 files changed, 2 insertions(+), 883 deletions(-)
 delete mode 100644 examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/README.md
 delete mode 100644 examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/data_process.py
 delete mode 100644 examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/main.py
 delete mode 100644 examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/prepare_model.py
 delete mode 100644 examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/requirements.txt
 delete mode 100644 examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/run_benchmark.sh
 delete mode 100644 examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/run_quant.sh

diff --git a/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/README.md b/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/README.md
deleted file mode 100644
index 5f7da9d595f..00000000000
--- a/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/README.md
+++ /dev/null
@@ -1,64 +0,0 @@
-Step-by-Step
-============
-
-This document is used to enable Tensorflow Keras model mobilenet_v2 quantization and benchmark using Intel® Neural Compressor.
-This example can run on Intel CPUs and GPUs.
-
-
-# Prerequisite
-
-## 1. Environment
-
-### Installation
-```shell
-# Install Intel® Neural Compressor
-pip install neural-compressor
-```
-
-### Install Requirements
-The Tensorflow and intel-extension-for-tensorflow is mandatory to be installed to run this example.
-The Intel Extension for Tensorflow for Intel CPUs is installed as default.
-```shell
-pip install -r requirements.txt
-```
-> Note: Validated TensorFlow [Version](/docs/source/installation_guide.md#validated-software-environment).
-
-## 2. Prepare Pretrained model
-
-The pretrained model is provided by [Keras Applications](https://keras.io/api/applications/). prepare the model, Run as follow: 
- ```
-python prepare_model.py --output_model=./mobilenet_v2_keras
- ```
-`--output_model ` the model should be saved as SavedModel format or H5 format.
-
-## 3. Prepare Dataset
-
-  TensorFlow [models](https://github.com/tensorflow/models) repo provides [scripts and instructions](https://github.com/tensorflow/models/tree/master/research/slim#an-automated-script-for-processing-imagenet-data) to download, process and convert the ImageNet dataset to the TF records format.
-  We also prepared related scripts in `imagenet_prepare` directory. To download the raw images, the user must create an account with image-net.org. If you have downloaded the raw data and preprocessed the validation data by moving the images into the appropriate sub-directory based on the label (synset) of the image. we can use below command ro convert it to tf records format.
-
-  ```shell
-  cd examples/3.x_api/tensorflow/keras/cv/
-  # convert validation subset
-  bash prepare_dataset.sh --output_dir=./mobilenet_v2/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
-  # convert train subset
-  bash prepare_dataset.sh --output_dir=./mobilenet_v2/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/train/ --subset=train
-  cd mobilenet_v2/quantization/ptq
-  ```
-> **Note**: 
-> The raw ImageNet dataset resides in JPEG files should be in the following directory structure. Taking validation set as an example:<br>
-> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/PATH/TO/img_raw/val/n01440764/ILSVRC2012_val_00000293.JPEG<br>
-> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/PATH/TO/img_raw/val/n01440764/ILSVRC2012_val_00000543.JPEG<br>
-> where 'n01440764' is the unique synset label associated with these images.
-
-# Run Command
-
-## Quantization
-  ```shell
-  bash run_quant.sh --input_model=./mobilenet_v2_keras --output_model=./result --dataset_location=/path/to/evaluation/dataset
-  ```
-
-## Benchmark
-  ```shell
-  bash run_benchmark.sh --input_model=./result --mode=accuracy --dataset_location=/path/to/evaluation/dataset --batch_size=32
-  bash run_benchmark.sh --input_model=./result --mode=performance --dataset_location=/path/to/evaluation/dataset --batch_size=1
-  ```
diff --git a/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/data_process.py b/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/data_process.py
deleted file mode 100644
index a655a5ce9a8..00000000000
--- a/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/data_process.py
+++ /dev/null
@@ -1,543 +0,0 @@
-#
-# -*- coding: utf-8 -*-
-#
-# Copyright (c) 2024 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-import os
-import collections
-
-import numpy as np
-import tensorflow as tf
-
-from abc import abstractmethod
-from neural_compressor.common import logger
-from neural_compressor.tensorflow.utils.data import default_collate
-
-class ParseDecodeImagenet:
-    """Parse features in Example proto.
-
-    Returns:
-        tuple of parsed image and label
-    """
-
-    def __call__(self, sample):
-        """Parse features in example."""
-        # Dense features in Example proto.
-        feature_map = {
-            "image/encoded": tf.io.FixedLenFeature([], dtype=tf.string, default_value=""),
-            "image/class/label": tf.io.FixedLenFeature([1], dtype=tf.int64, default_value=-1),
-        }
-
-        sparse_float32 = tf.io.VarLenFeature(dtype=tf.float32)
-        # Sparse features in Example proto.
-        feature_map.update(
-            {
-                k: sparse_float32
-                for k in [
-                    "image/object/bbox/xmin",
-                    "image/object/bbox/ymin",
-                    "image/object/bbox/xmax",
-                    "image/object/bbox/ymax",
-                ]
-            }
-        )
-
-        features = tf.io.parse_single_example(serialized=sample, features=feature_map)
-        label = tf.cast(features["image/class/label"], dtype=tf.int32)
-        image = features["image/encoded"]
-        image = tf.image.decode_jpeg(image, channels=3, fancy_upscaling=False, dct_method="INTEGER_FAST")
-        return (image, label)
-
-
-class BilinearImagenetTransform(object):
-    """Combination of a series of transforms which is applicable to images in Imagenet.
-
-    Args:
-        height: Height of the result
-        width:Width of the result
-        central_fraction(float, default=0.875):fraction of size to crop
-        mean_value(list, default=[0.0,0.0,0.0]):means for each channel
-        scale(float, default=1.0):std value
-
-    Returns:
-        tuple of processed image and label
-    """
-
-    def __init__(self, height, width, central_fraction=0.875, mean_value=[0.0, 0.0, 0.0], scale=1.0):
-        """Initialize `BilinearImagenetTransform` class."""
-        self.height = height
-        self.width = width
-        self.mean_value = mean_value
-        self.scale = scale
-        self.central_fraction = central_fraction
-
-    # sample is (images, labels)
-    def __call__(self, sample):
-        """Convert `BilinearImagenetTransform` feature."""
-        image, label = sample
-        if image.dtype is not tf.float32:
-            image = tf.image.convert_image_dtype(image, dtype=tf.float32)
-        # Crop the central region of the image containing 87.5% area of the original image.
-        if self.central_fraction:
-            image = tf.image.central_crop(image, central_fraction=self.central_fraction)
-
-        if self.height and self.width:
-            # Resize the image to the specified height and width.
-            image = tf.expand_dims(image, 0)
-            image = tf.image.resize(image, [self.height, self.width], method=tf.image.ResizeMethod.BILINEAR)
-            image = tf.squeeze(image, [0])
-
-        image = tf.subtract(image, 0.5)
-        image = tf.multiply(image, 2.0)
-        means = tf.broadcast_to(self.mean_value, tf.shape(input=image))
-        image = (image - means) * self.scale
-        return (image, label)
-
-
-class ComposeTransform(object):
-    """Composes several transforms together.
-
-    Args:
-        transform_list (list of Transform objects):  list of transforms to compose
-
-    Returns:
-        sample (tuple): tuple of processed image and label
-    """
-
-    def __init__(self, transform_list):
-        """Initialize `ComposeTransform` class."""
-        self.transform_list = transform_list
-
-    def __call__(self, sample):
-        """Call transforms in transform_list."""
-        for transform in self.transform_list:
-            sample = transform(sample)
-        return sample
-
-
-class LabelShift(object):
-    """Convert label to label - label_shift.
-
-    Args:
-        label_shift(int, default=0): number of label shift
-
-    Returns:
-        tuple of processed image and label
-    """
-
-    def __init__(self, label_shift=0):
-        """Initialize `LabelShift` class."""
-        self.label_shift = label_shift
-
-    def __call__(self, sample):
-        """Convert label to label_shift."""
-        images, labels = sample
-        if isinstance(labels, np.ndarray):
-            labels = labels - self.label_shift
-        elif isinstance(labels, list):
-            if isinstance(labels[0], tuple):
-                labels = [tuple(np.array(label) - self.label_shift) for label in labels]
-            elif isinstance(labels[0], np.ndarray):
-                labels = [label - self.label_shift for label in labels]
-            else:
-                labels = np.array(labels) - self.label_shift
-                labels = labels.tolist()
-        else:
-            labels = np.array(labels) - self.label_shift
-        return images, labels
-
-
-class ShiftRescale(object):
-    """Label shift by 1 and rescale.
-
-    Returns:
-        tuple of processed image and label
-    """
-
-    def __call__(self, sample):
-        image, label = sample
-        label -= 1
-        image = (image - 127.5) / 127.5
-        return (image, label)
-
-
-class ImageRecordDataset(object):
-    """Tensorflow imageNet database in tf record format.
-
-    Please arrange data in this way:
-        root/validation-000-of-100
-        root/validation-001-of-100
-        ...
-        root/validation-099-of-100
-    The file name needs to follow this pattern: '* - * -of- *'
-
-    Args: root (str): Root directory of dataset.
-          transform (transform object, default=None):  transform to process input data.
-          filter (Filter objects, default=None): filter out examples according
-                                                 to specific conditions.
-    """
-
-    """Configuration for Imagenet dataset."""
-
-    def __new__(cls, root, transform=None, filter=None):
-        """Build a new object of TensorflowImageRecord class."""
-        from tensorflow.python.platform import gfile  # pylint: disable=no-name-in-module
-
-        glob_pattern = os.path.join(root, "*-*-of-*")
-        file_names = gfile.Glob(glob_pattern)
-        if not file_names:
-            raise ValueError("Found no files in --root matching: {}".format(glob_pattern))
-
-        # pylint: disable=no-name-in-module
-        from tensorflow.python.data.experimental import parallel_interleave
-
-        ds = tf.data.TFRecordDataset.list_files(file_names, shuffle=False)
-        ds = ds.apply(parallel_interleave(tf.data.TFRecordDataset, cycle_length=len(file_names)))
-
-        if transform is not None:
-            transform.transform_list.insert(0, ParseDecodeImagenet())
-        else:
-            transform = ParseDecodeImagenet()
-        ds = ds.map(transform, num_parallel_calls=None)
-        ds = ds.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)  # this number can be tuned
-        return ds
-
-
-class BaseMetric(object):
-    """The base class of Metric."""
-
-    def __init__(self, metric, single_output=False, hvd=None):
-        """Initialize the basic metric.
-
-        Args:
-            metric: The metric class.
-            single_output: Whether the output is single or not, defaults to False.
-            hvd: The Horovod class for distributed training, defaults to None.
-        """
-        self._metric_cls = metric
-        self._single_output = single_output
-        self._hvd = hvd
-
-    def __call__(self, *args, **kwargs):
-        """Evaluate the model predictions, and the reference.
-
-        Returns:
-            The class itself.
-        """
-        self._metric = self._metric_cls(*args, **kwargs)
-        return self
-
-    @abstractmethod
-    def update(self, preds, labels=None, sample_weight=None):
-        """Update the state that need to be evaluated.
-
-        Args:
-            preds: The prediction result.
-            labels: The reference. Defaults to None.
-            sample_weight: The sampling weight. Defaults to None.
-
-        Raises:
-            NotImplementedError: The method should be implemented by subclass.
-        """
-        raise NotImplementedError
-
-    @abstractmethod
-    def reset(self):
-        """Clear the predictions and labels.
-
-        Raises:
-            NotImplementedError: The method should be implemented by subclass.
-        """
-        raise NotImplementedError
-
-    @abstractmethod
-    def result(self):
-        """Evaluate the difference between predictions and labels.
-
-        Raises:
-            NotImplementedError: The method should be implemented by subclass.
-        """
-        raise NotImplementedError
-
-    @property
-    def metric(self):
-        """Return its metric class.
-
-        Returns:
-            The metric class.
-        """
-        return self._metric_cls
-
-    @property
-    def hvd(self):
-        """Return its hvd class.
-
-        Returns:
-            The hvd class.
-        """
-        return self._hvd
-
-    @hvd.setter
-    def hvd(self, hvd):
-        """Set its hvd.
-
-        Args:
-            hvd: The Horovod class for distributed training.
-        """
-        self._hvd = hvd
-
-
-class TopKMetric(BaseMetric):
-    """Compute Top-k Accuracy classification score for Tensorflow model.
-
-    This metric computes the number of times where the correct label is among
-    the top k labels predicted.
-
-    Attributes:
-        k (int): The number of most likely outcomes considered to find the correct label.
-        num_correct: The number of predictions that were correct classified.
-        num_sample: The total number of predictions.
-    """
-
-    def __init__(self, k=1):
-        """Initialize the k, number of samples and correct predictions.
-
-        Args:
-            k: The number of most likely outcomes considered to find the correct label.
-        """
-        self.k = k
-        self.num_correct = 0
-        self.num_sample = 0
-
-    def update(self, preds, labels, sample_weight=None):
-        """Add the predictions and labels.
-
-        Args:
-            preds: The predictions.
-            labels: The labels corresponding to the predictions.
-            sample_weight: The sample weight.
-        """
-        preds, labels = TopKMetric._topk_shape_validate(preds, labels)
-
-        labels = labels.reshape([len(labels)])
-        with tf.Graph().as_default() as acc_graph:
-            topk = tf.nn.in_top_k(
-                predictions=tf.constant(preds, dtype=tf.float32), targets=tf.constant(labels, dtype=tf.int32), k=self.k
-            )
-            fp32_topk = tf.cast(topk, tf.float32)
-            correct_tensor = tf.reduce_sum(input_tensor=fp32_topk)
-
-            with tf.compat.v1.Session() as acc_sess:
-                correct = acc_sess.run(correct_tensor)
-
-        self.num_sample += len(labels)
-        self.num_correct += correct
-
-    def reset(self):
-        """Reset the number of samples and correct predictions."""
-        self.num_correct = 0
-        self.num_sample = 0
-
-    def result(self):
-        """Compute the top-k score.
-
-        Returns:
-            The top-k score.
-        """
-        if self.num_sample == 0:
-            logger.warning("Sample num during evaluation is 0.")
-            return 0
-        elif getattr(self, "_hvd", None) is not None:  # pragma: no cover
-            allgather_num_correct = sum(self._hvd.allgather_object(self.num_correct))
-            allgather_num_sample = sum(self._hvd.allgather_object(self.num_sample))
-            return allgather_num_correct / allgather_num_sample
-        return self.num_correct / self.num_sample
-
-    @staticmethod
-    def _topk_shape_validate(preds, labels):
-        # preds shape can be Nxclass_num or class_num(N=1 by default)
-        # it's more suitable for 'Accuracy' with preds shape Nx1(or 1) output from argmax
-        if isinstance(preds, int):
-            preds = [preds]
-            preds = np.array(preds)
-        elif isinstance(preds, np.ndarray):
-            preds = np.array(preds)
-        elif isinstance(preds, list):
-            preds = np.array(preds)
-            preds = preds.reshape((-1, preds.shape[-1]))
-
-        # consider labels just int value 1x1
-        if isinstance(labels, int):
-            labels = [labels]
-            labels = np.array(labels)
-        elif isinstance(labels, tuple):
-            labels = np.array([labels])
-            labels = labels.reshape((labels.shape[-1], -1))
-        elif isinstance(labels, list):
-            if isinstance(labels[0], int):
-                labels = np.array(labels)
-                labels = labels.reshape((labels.shape[0], 1))
-            elif isinstance(labels[0], tuple):
-                labels = np.array(labels)
-                labels = labels.reshape((labels.shape[-1], -1))
-            else:
-                labels = np.array(labels)
-        # labels most have 2 axis, 2 cases: N(or Nx1 sparse) or Nxclass_num(one-hot)
-        # only support 2 dimension one-shot labels
-        # or 1 dimension one-hot class_num will confuse with N
-
-        if len(preds.shape) == 1:
-            N = 1
-            class_num = preds.shape[0]
-            preds = preds.reshape([-1, class_num])
-        elif len(preds.shape) >= 2:
-            N = preds.shape[0]
-            preds = preds.reshape([N, -1])
-            class_num = preds.shape[1]
-
-        label_N = labels.shape[0]
-        assert label_N == N, "labels batch size should same with preds"
-        labels = labels.reshape([N, -1])
-        # one-hot labels will have 2 dimension not equal 1
-        if labels.shape[1] != 1:
-            labels = labels.argsort()[..., -1:]
-        return preds, labels
-    
-
-class TFDataLoader(object):  # pragma: no cover
-    """Tensorflow dataloader class.
-
-    In tensorflow1.x dataloader is coupled with the graph, but it also support feed_dict
-    method to do session run, this dataloader is designed to satisfy the usage of feed dict
-    in tf1.x. Although it's a general dataloader and can be used in MXNet and PyTorch.
-
-    Args:
-        dataset: obj. wrapper of needed data.
-        batch_size: int. batch size
-    """
-
-    def __init__(self, dataset, batch_size=1, last_batch="rollover"):
-        """Initialize `TFDataDataLoader` class."""
-        self.dataset = dataset
-        self.last_batch = last_batch
-        self.batch_size = batch_size
-        dataset = dataset.batch(batch_size)
-
-    def batch(self, batch_size, last_batch="rollover"):
-        """Dataset return data per batch."""
-        drop_last = False if last_batch == "rollover" else True
-        self.batch_size = batch_size
-        self.dataset = self.dataset.batch(batch_size, drop_last)
-
-    def __iter__(self):
-        """Iterate dataloader."""
-        return self._generate_dataloader(
-            self.dataset,
-            batch_size=self.batch_size,
-            last_batch=self.last_batch,
-        )
-
-    def _generate_dataloader(
-        self,
-        dataset,
-        batch_size=1,
-        last_batch="rollover",
-        collate_fn=None,
-        sampler=None,
-        batch_sampler=None,
-        num_workers=None,
-        pin_memory=None,
-        distributed=False,
-    ):
-        """Yield data."""
-        drop_last = False if last_batch == "rollover" else True
-
-        def check_dynamic_shape(element_spec):
-            if isinstance(element_spec, collections.abc.Sequence):
-                return any([check_dynamic_shape(ele) for ele in element_spec])
-            elif isinstance(element_spec, tf.TensorSpec):
-                return True if element_spec.shape.num_elements() is None else False
-            else:
-                raise ValueError("unrecognized element spec...")
-
-        def squeeze_output(output):
-            if isinstance(output, collections.abc.Sequence):
-                return [squeeze_output(ele) for ele in output]
-            elif isinstance(output, np.ndarray):
-                return np.squeeze(output, axis=0)
-            else:
-                raise ValueError("not supported output format....")
-
-        if tf.executing_eagerly():
-            index = 0
-            outputs = []
-            for iter_tensors in dataset:
-                samples = []
-                iter_inputs, iter_labels = iter_tensors[0], iter_tensors[1]
-                if isinstance(iter_inputs, tf.Tensor):
-                    samples.append(iter_inputs.numpy())
-                else:
-                    samples.append(tuple(iter_input.numpy() for iter_input in iter_inputs))
-                if isinstance(iter_labels, tf.Tensor):
-                    samples.append(iter_labels.numpy())
-                else:
-                    samples.append([np.array(l) for l in iter_labels])
-                index += 1
-                outputs.append(samples)
-                if index == batch_size:
-                    outputs = default_collate(outputs)
-                    yield outputs
-                    outputs = []
-                    index = 0
-            if len(outputs) > 0:
-                outputs = default_collate(outputs)
-                yield outputs
-        else:
-            try_single_batch = check_dynamic_shape(dataset.element_spec)
-            dataset = dataset.batch(1 if try_single_batch else batch_size, drop_last)
-            ds_iterator = tf.compat.v1.data.make_one_shot_iterator(dataset)
-            iter_tensors = ds_iterator.get_next()
-            data_config = tf.compat.v1.ConfigProto()
-            data_config.use_per_session_threads = 1
-            data_config.intra_op_parallelism_threads = 1
-            data_config.inter_op_parallelism_threads = 16
-            data_sess = tf.compat.v1.Session(config=data_config)
-            # pylint: disable=no-name-in-module
-            from tensorflow.python.framework.errors_impl import OutOfRangeError
-
-            while True:
-                if not try_single_batch:
-                    try:
-                        outputs = data_sess.run(iter_tensors)
-                        yield outputs
-                    except OutOfRangeError:
-                        data_sess.close()
-                        return
-                else:
-                    try:
-                        outputs = []
-                        for i in range(0, batch_size):
-                            outputs.append(squeeze_output(data_sess.run(iter_tensors)))
-                        outputs = default_collate(outputs)
-                        yield outputs
-                    except OutOfRangeError:
-                        if len(outputs) == 0:
-                            data_sess.close()
-                            return
-                        else:
-                            outputs = default_collate(outputs)
-                            yield outputs
-                            data_sess.close()
-                            return
diff --git a/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/main.py b/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/main.py
deleted file mode 100644
index 804dd81ef5a..00000000000
--- a/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/main.py
+++ /dev/null
@@ -1,143 +0,0 @@
-#
-# -*- coding: utf-8 -*-
-#
-# Copyright (c) 2018 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-import time
-
-import numpy as np
-import tensorflow as tf
-
-from neural_compressor.utils import logger
-from data_process import (
-    ImageRecordDataset, 
-    ComposeTransform, 
-    BilinearImagenetTransform, 
-    TFDataLoader,
-    TopKMetric,
-    LabelShift,
-)
-
-tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
-
-flags = tf.compat.v1.flags
-FLAGS = flags.FLAGS
-
-## Required parameters
-flags.DEFINE_string(
-    'input_model', None, 'Run inference with specified keras model.')
-
-flags.DEFINE_string(
-    'output_model', None, 'The output quantized model.')
-
-flags.DEFINE_string(
-    'mode', 'performance', 'define benchmark mode for accuracy or performance')
-
-flags.DEFINE_bool(
-    'tune', False, 'whether to tune the model')
-
-flags.DEFINE_bool(
-    'benchmark', False, 'whether to benchmark the model')
-
-flags.DEFINE_string(
-    'calib_data', None, 'location of calibration dataset')
-
-flags.DEFINE_string(
-    'eval_data', None, 'location of evaluate dataset')
-
-flags.DEFINE_integer('batch_size', 32, 'batch_size')
-
-flags.DEFINE_integer(
-    'iters', 100, 'maximum iteration when evaluating performance')
-
-height = width = 224
-eval_dataset = ImageRecordDataset(root=FLAGS.eval_data, transform=ComposeTransform(transform_list= \
-                 [BilinearImagenetTransform(height=height, width=width)]))
-
-eval_dataloader = TFDataLoader(dataset=eval_dataset, batch_size=FLAGS.batch_size)
-
-if FLAGS.calib_data:
-    calib_dataset = ImageRecordDataset(root=FLAGS.calib_data, transform= \
-        ComposeTransform(transform_list= [BilinearImagenetTransform(height=height, width=width)]))
-    calib_dataloader = TFDataLoader(dataset=calib_dataset, batch_size=10)
-
-def evaluate(model):
-    """
-    Custom evaluate function to inference the model for specified metric on validation dataset.
-
-    Args:
-        model (tf.keras.Model): The input model will be the objection of tf.keras.Model.
-
-    Returns:
-        accuracy (float): evaluation result, the larger is better.
-    """
-    latency_list = []
-    metric = TopKMetric()
-    postprocess = LabelShift(label_shift=1)
-
-    def eval_func(dataloader, metric):
-        warmup = 5
-        iteration = None
-        if FLAGS.benchmark and FLAGS.mode == 'performance':
-            iteration = FLAGS.iters
-        for idx, (inputs, labels) in enumerate(dataloader):
-            start = time.time()
-            predictions = model.predict_on_batch(inputs)
-            end = time.time()
-            latency_list.append(end - start)
-            predictions, labels = postprocess((predictions, labels))
-            metric.update(predictions, labels)
-            if iteration and idx >= iteration:
-                break
-        latency = np.array(latency_list[warmup:]).mean() / eval_dataloader.batch_size
-        return latency
-
-    latency = eval_func(eval_dataloader, metric)
-    if FLAGS.benchmark:
-        logger.info("\n{} mode benchmark result:".format(FLAGS.mode))
-        for i, res in enumerate(latency_list):
-            logger.debug("Iteration {} result {}:".format(i, res))
-    if FLAGS.benchmark and FLAGS.mode == 'performance':
-        logger.info("Batch size = {}".format(eval_dataloader.batch_size))
-        logger.info("Latency: {:.3f} ms".format(latency * 1000))
-        logger.info("Throughput: {:.3f} images/sec".format(1. / latency))
-    acc = metric.result()
-    return acc
-
-def main(_):
-    if FLAGS.tune:
-        from neural_compressor.common import set_random_seed
-        from neural_compressor.tensorflow import quantize_model
-        from neural_compressor.tensorflow.keras import StaticQuantConfig
-
-        set_random_seed(9527)
-        quant_config = StaticQuantConfig()
-        q_model = quantize_model(FLAGS.input_model, quant_config, calib_dataloader)
-        q_model.save(FLAGS.output_model)
-        logger.info("Save quantized model to {}.".format(FLAGS.output_model))
-
-    if FLAGS.benchmark:
-        from neural_compressor.tensorflow import Model
-
-        inc_model = Model(FLAGS.input_model)
-        if FLAGS.mode == 'performance':
-            evaluate(inc_model.model)
-        else:
-            accuracy = evaluate(inc_model.model)
-            logger.info('Batch size = %d' % FLAGS.batch_size)
-            logger.info("Accuracy: %.5f" % accuracy)
-
-if __name__ == "__main__":
-    tf.compat.v1.app.run()
diff --git a/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/prepare_model.py b/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/prepare_model.py
deleted file mode 100644
index e31b3e83de0..00000000000
--- a/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/prepare_model.py
+++ /dev/null
@@ -1,35 +0,0 @@
-#
-# -*- coding: utf-8 -*-
-#
-# Copyright (c) 2022 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-import argparse
-from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2
-def get_mobilenet_v2_model(saved_path):
-    model = MobileNetV2(weights='imagenet')
-    model.save(saved_path)
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-    description='Export pretained keras model',
-    formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument(
-        '--output_model',
-        type=str,
-        help='path to exported model file')
-
-    args = parser.parse_args()
-    get_mobilenet_v2_model(args.output_model)
diff --git a/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/requirements.txt b/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/requirements.txt
deleted file mode 100644
index 8b7b47da969..00000000000
--- a/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/requirements.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-tensorflow>=2.11.1
-intel-extension-for-tensorflow[cpu]
diff --git a/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/run_benchmark.sh b/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/run_benchmark.sh
deleted file mode 100644
index 43b1636c839..00000000000
--- a/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/run_benchmark.sh
+++ /dev/null
@@ -1,51 +0,0 @@
-#!/bin/bash
-set -x
-
-function main {
-
-  init_params "$@"
-  run_benchmark
-
-}
-
-# init params
-function init_params {
-  batch_size=32
-  iters=100
-
-  for var in "$@"
-  do
-    case $var in
-      --input_model=*)
-          input_model=$(echo $var |cut -f2 -d=)
-      ;;
-      --mode=*)
-          mode=$(echo $var |cut -f2 -d=)    
-      ;;
-      --dataset_location=*)
-          dataset_location=$(echo $var |cut -f2 -d=)    
-      ;;
-      --batch_size=*)
-          batch_size=$(echo $var |cut -f2 -d=)
-      ;;
-      --iters=*)
-          iters=$(echo $var |cut -f2 -d=)
-      ;;
-    esac
-  done
-
-}
-
-# run_tuning
-function run_benchmark {
-
-    python main.py \
-            --input_model ${input_model} \
-            --benchmark \
-            --mode ${mode} \
-            --eval_data ${dataset_location} \
-            --batch_size ${batch_size} \
-            --iters ${iters}
-}
-
-main "$@"
diff --git a/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/run_quant.sh b/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/run_quant.sh
deleted file mode 100644
index 7e3ed727f71..00000000000
--- a/examples/3.x_api/tensorflow/keras/cv/mobilenet_v2/quantization/ptq/run_quant.sh
+++ /dev/null
@@ -1,40 +0,0 @@
-#!/bin/bash
-set -x
-
-function main {
-  init_params "$@"
-  run_tuning
-
-}
-
-# init params
-function init_params {
-
-  for var in "$@"
-  do
-    case $var in
-      --input_model=*)
-          input_model=$(echo $var |cut -f2 -d=)
-      ;;
-      --output_model=*)
-          output_model=$(echo $var |cut -f2 -d=)
-      ;;
-      --dataset_location=*)
-          dataset_location=$(echo $var |cut -f2 -d=)
-      ;;
-    esac
-  done
-
-}
-
-# run_tuning
-function run_tuning {
-    python main.py \
-            --input_model ${input_model} \
-            --output_model ${output_model} \
-            --eval_data ${dataset_location} \
-            --calib_data ${dataset_location} \
-            --tune
-}
-
-main "$@"
diff --git a/neural_compressor/tensorflow/algorithms/static_quant/keras.py b/neural_compressor/tensorflow/algorithms/static_quant/keras.py
index f6803c03604..c92ee43d1c2 100644
--- a/neural_compressor/tensorflow/algorithms/static_quant/keras.py
+++ b/neural_compressor/tensorflow/algorithms/static_quant/keras.py
@@ -721,12 +721,9 @@ def _parse_inputs(self, BN_fused_layers=None, conv_names=None):
 
                 for out_layer_name in out_layer_names:
                     if out_layer_name not in input_layer_dict:
-                        input_layer_dict[out_layer_name] = set([layer.name])
+                        input_layer_dict[out_layer_name] = [layer.name]
                     else:
-                        input_layer_dict[out_layer_name].add(layer.name)
-
-        for key in input_layer_dict.keys():
-            input_layer_dict[key] = list(input_layer_dict[key])
+                        input_layer_dict[out_layer_name].append(layer.name)
 
         try:
             model_input = self.model.input

From 150b7b43f8c3c814583f8009c6ea94a880db8816 Mon Sep 17 00:00:00 2001
From: zehao-intel <zehao.huang@intel.com>
Date: Wed, 24 Jul 2024 17:49:51 +0800
Subject: [PATCH 13/14] remove densenet

Signed-off-by: zehao-intel <zehao.huang@intel.com>
---
 examples/.config/model_params_keras_3x.json   |   7 -
 .../.config/model_params_tensorflow_3x.json   |   7 -
 .../cv/densenet121/quantization/ptq/README.md |  78 ---
 .../quantization/ptq/data_process.py          | 581 ------------------
 .../cv/densenet121/quantization/ptq/main.py   | 147 -----
 .../quantization/ptq/requirements.txt         |   2 -
 .../quantization/ptq/run_benchmark.sh         |  51 --
 .../densenet121/quantization/ptq/run_quant.sh |  39 --
 8 files changed, 912 deletions(-)
 delete mode 100644 examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/README.md
 delete mode 100644 examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/data_process.py
 delete mode 100644 examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/main.py
 delete mode 100644 examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/requirements.txt
 delete mode 100644 examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/run_benchmark.sh
 delete mode 100644 examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/run_quant.sh

diff --git a/examples/.config/model_params_keras_3x.json b/examples/.config/model_params_keras_3x.json
index 65b0466667e..40bca7c9e22 100644
--- a/examples/.config/model_params_keras_3x.json
+++ b/examples/.config/model_params_keras_3x.json
@@ -13,13 +13,6 @@
       "input_model": "/tf_dataset2/models/tensorflow/inception_v3_keras/saved_model",
       "main_script": "main.py",
       "batch_size": 32
-    },
-    "mobilenet_v2": {
-      "model_src_dir": "keras/cv/mobilenet_v2/quantization/ptq",
-      "dataset_location": "/tf_dataset/dataset/imagenet",
-      "input_model": "/tf_dataset2/models/tensorflow/mobilenet_v2_keras/saved_model",
-      "main_script": "main.py",
-      "batch_size": 32
     }
   }
 }
diff --git a/examples/.config/model_params_tensorflow_3x.json b/examples/.config/model_params_tensorflow_3x.json
index 1e7f8bdab1a..6bd525ff1b9 100644
--- a/examples/.config/model_params_tensorflow_3x.json
+++ b/examples/.config/model_params_tensorflow_3x.json
@@ -50,13 +50,6 @@
       "main_script": "main.py",
       "batch_size": 64
     },
-    "densenet121": {
-      "model_src_dir": "cv/densenet121/quantization/ptq",
-      "dataset_location": "/tf_dataset/dataset/imagenet",
-      "input_model": "/tf_dataset/tensorflow/densenet/densenet-121.pb",
-      "main_script": "main.py",
-      "batch_size": 32
-    },
     "inception_v3": {
       "model_src_dir": "cv/inception_v3/quantization/ptq",
       "dataset_location": "/tf_dataset/dataset/imagenet",
diff --git a/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/README.md b/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/README.md
deleted file mode 100644
index 7dbe04de7ca..00000000000
--- a/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/README.md
+++ /dev/null
@@ -1,78 +0,0 @@
-Step-by-Step
-============
-
-This document list steps of reproducing densenet121 model tuning and benchmark results via Neural Compressor.
-This example can run on Intel CPUs and GPUs.
-
-> **Note**: 
-> The models is supported in Validated TensorFlow [Version](/docs/source/installation_guide.md#validated-software-environment).
-# Prerequisite
-
-## 1. Environment
-
-### Installation
-Recommend python 3.9 or higher version.
-```shell
-pip install -r requirements.txt
-```
-
-### Install Intel Extension for Tensorflow
-#### Quantizing the model on Intel GPU(Mandatory to install ITEX)
-Intel Extension for Tensorflow is mandatory to be installed for quantizing the model on Intel GPUs.
-
-```shell
-pip install --upgrade intel-extension-for-tensorflow[xpu]
-```
-For any more details, please follow the procedure in [install-gpu-drivers](https://github.com/intel/intel-extension-for-tensorflow/blob/main/docs/install/install_for_xpu.md#install-gpu-drivers)
-
-#### Quantizing the model on Intel CPU(Optional to install ITEX)
-Intel Extension for Tensorflow for Intel CPUs is experimental currently. It's not mandatory for quantizing the model on Intel CPUs.
-
-```shell
-pip install --upgrade intel-extension-for-tensorflow[cpu]
-```
-> **Note**: 
-> The version compatibility of stock Tensorflow and ITEX can be checked [here](https://github.com/intel/intel-extension-for-tensorflow#compatibility-table). Please make sure you have installed compatible Tensorflow and ITEX.
-
-## 2. Prepare pre-trained model
-The densenet-series comes from [tensorflow-densenet](https://github.com/pudae/tensorflow-densenet), please also follow the step [Prepare pre-trained model](#3-prepare-pre-trained-model) to get the pb files or use openvino download tools.
-   ```shell
-   git clone https://github.com/openvinotoolkit/open_model_zoo.git
-   cd open_model_zoo/tools/downloader
-   git checkout tags/2021.2
-   pip install -r requirements.in
-   python downloader.py --name densenet-{121|161|169}-tf -o /PATH/TO/MODEL
-   ```
-
-## 3. Prepare Dataset
-
-  TensorFlow [models](https://github.com/tensorflow/models) repo provides [scripts and instructions](https://github.com/tensorflow/models/tree/master/research/slim#an-automated-script-for-processing-imagenet-data) to download, process and convert the ImageNet dataset to the TF records format.
-  We also prepared related scripts in ` examples/3.x_api/tensorflow/cv` directory. To download the raw images, the user must create an account with image-net.org. If you have downloaded the raw data and preprocessed the validation data by moving the images into the appropriate sub-directory based on the label (synset) of the image. we can use below command ro convert it to tf records format.
-
-  ```shell
-  cd examples/3.x_api/tensorflow/cv
-  # convert validation subset
-  bash prepare_dataset.sh --output_dir=./densenet121/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
-  # convert train subset
-  bash prepare_dataset.sh --output_dir=./densenet121/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/train/ --subset=train
-  ```
-> **Note**: 
-> The raw ImageNet dataset resides in JPEG files should be in the following directory structure. Taking validation set as an example:<br>
-> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/PATH/TO/img_raw/val/n01440764/ILSVRC2012_val_00000293.JPEG<br>
-> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/PATH/TO/img_raw/val/n01440764/ILSVRC2012_val_00000543.JPEG<br>
-> where 'n01440764' is the unique synset label associated with these images.
-
-# Run
-
-## 1 Quantization
-
-  ```shell
-  bash run_quant.sh --input_model=/PATH/TO/densenet-121.pb \
-      --output_model=./nc_densenet121.pb --dataset_location=/path/to/ImageNet/
-  ```
-
-## 2. Benchmark
-  ```shell
-  bash run_benchmark.sh --input_model=./nc_densenet121.pb --mode=accuracy --dataset_location=/path/to/ImageNet/ --batch_size=32
-  bash run_benchmark.sh --input_model=./nc_densenet121.pb --mode=performance --dataset_location=/path/to/ImageNet/ --batch_size=1
-  ```
diff --git a/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/data_process.py b/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/data_process.py
deleted file mode 100644
index 17b4d9cec5e..00000000000
--- a/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/data_process.py
+++ /dev/null
@@ -1,581 +0,0 @@
-#
-# -*- coding: utf-8 -*-
-#
-# Copyright (c) 2024 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-import os
-import collections
-
-import numpy as np
-import tensorflow as tf
-
-from abc import abstractmethod
-from neural_compressor.common import logger
-from neural_compressor.tensorflow.utils.data import default_collate
-
-class ParseDecodeImagenet:
-    """Parse features in Example proto.
-
-    Returns:
-        tuple of parsed image and label
-    """
-
-    def __call__(self, sample):
-        """Parse features in example."""
-        # Dense features in Example proto.
-        feature_map = {
-            "image/encoded": tf.io.FixedLenFeature([], dtype=tf.string, default_value=""),
-            "image/class/label": tf.io.FixedLenFeature([1], dtype=tf.int64, default_value=-1),
-        }
-
-        sparse_float32 = tf.io.VarLenFeature(dtype=tf.float32)
-        # Sparse features in Example proto.
-        feature_map.update(
-            {
-                k: sparse_float32
-                for k in [
-                    "image/object/bbox/xmin",
-                    "image/object/bbox/ymin",
-                    "image/object/bbox/xmax",
-                    "image/object/bbox/ymax",
-                ]
-            }
-        )
-
-        features = tf.io.parse_single_example(serialized=sample, features=feature_map)
-        label = tf.cast(features["image/class/label"], dtype=tf.int32)
-        image = features["image/encoded"]
-        image = tf.image.decode_jpeg(image, channels=3, fancy_upscaling=False, dct_method="INTEGER_FAST")
-        return (image, label)
-
-
-class ResizeCropImagenet(object):
-    """Combination of a series of transforms which is applicable to images in Imagenet.
-
-    Args:
-        height (int): Height of the result
-        width (int): Width of the result
-        random_crop (bool, default=False): whether to random crop
-        resize_side (int, default=256):desired shape after resize operation
-        random_flip_left_right (bool, default=False): whether to random flip left and right
-        mean_value (list, default=[0.0,0.0,0.0]):means for each channel
-        scale (float, default=1.0):std value
-
-    Returns:
-        tuple of processed image and label
-    """
-
-    def __init__(
-        self,
-        height,
-        width,
-        random_crop=False,
-        resize_side=256,
-        resize_method="bilinear",
-        random_flip_left_right=False,
-        mean_value=[0.0, 0.0, 0.0],
-        scale=1.0,
-        data_format="channels_last",
-        subpixels="RGB",
-    ):
-        """Initialize `TensorflowResizeCropImagenetTransform` class."""
-        self.height = height
-        self.width = width
-        self.mean_value = mean_value
-        self.scale = scale
-        self.random_crop = random_crop
-        self.random_flip_left_right = random_flip_left_right
-        self.resize_side = resize_side
-        self.resize_method = resize_method
-        self.data_format = data_format
-        self.subpixels = subpixels
-
-    # sample is (images, labels)
-    def __call__(self, sample):
-        """Convert `TensorflowResizeCropImagenetTransform` feature."""
-        image, label = sample
-        shape = tf.shape(input=image)
-
-        height = (
-            tf.cast(shape[0], dtype=tf.float32)
-            if self.data_format == "channels_last"
-            else tf.cast(shape[1], dtype=tf.float32)
-        )
-        width = (
-            tf.cast(shape[1], dtype=tf.float32)
-            if self.data_format == "channels_last"
-            else tf.cast(shape[2], dtype=tf.float32)
-        )
-        scale = tf.cond(
-            pred=tf.greater(height, width),
-            true_fn=lambda: self.resize_side / width,
-            false_fn=lambda: self.resize_side / height,
-        )
-
-        scale = tf.cast(scale, dtype=tf.float32)
-        new_height = tf.cast(tf.math.rint(height * scale), dtype=tf.int32)
-        new_width = tf.cast(tf.math.rint(width * scale), dtype=tf.int32)
-
-        if self.subpixels == "BGR" and self.data_format == "channels_first":
-            # 'RGB'->'BGR'
-            image = tf.cond(
-                tf.equal(tf.rank(image), 3),
-                lambda: tf.experimental.numpy.moveaxis(image[::-1, ...], 0, -1),
-                lambda: tf.experimental.numpy.moveaxis(image[:, ::-1, ...], 1, -1),
-            )
-        elif self.subpixels == "BGR":
-            # 'RGB'->'BGR'
-            image = image[..., ::-1]
-        image = tf.expand_dims(image, 0)
-        image = tf.image.resize(image, [new_height, new_width], method=self.resize_method)
-        image = tf.squeeze(image)
-        shape = tf.shape(input=image)
-        if self.random_crop:
-            y0 = tf.random.uniform(shape=[], minval=0, maxval=(shape[0] - self.height + 1), dtype=tf.dtypes.int32)
-            x0 = tf.random.uniform(shape=[], minval=0, maxval=(shape[1] - self.width + 1), dtype=tf.dtypes.int32)
-        else:
-            y0 = (shape[0] - self.height) // 2
-            x0 = (shape[1] - self.width) // 2
-
-        image = tf.image.crop_to_bounding_box(image, y0, x0, self.height, self.width)
-        image.set_shape([self.height, self.width, 3])
-        if self.random_flip_left_right:
-            image = tf.image.random_flip_left_right(image)
-        means = tf.broadcast_to(self.mean_value, tf.shape(input=image))
-        image = (image - means) * self.scale
-        return (image, label)
-
-
-class ComposeTransform(object):
-    """Composes several transforms together.
-
-    Args:
-        transform_list (list of Transform objects):  list of transforms to compose
-
-    Returns:
-        sample (tuple): tuple of processed image and label
-    """
-
-    def __init__(self, transform_list):
-        """Initialize `ComposeTransform` class."""
-        self.transform_list = transform_list
-
-    def __call__(self, sample):
-        """Call transforms in transform_list."""
-        for transform in self.transform_list:
-            sample = transform(sample)
-        return sample
-
-
-class LabelShift(object):
-    """Convert label to label - label_shift.
-
-    Args:
-        label_shift(int, default=0): number of label shift
-
-    Returns:
-        tuple of processed image and label
-    """
-
-    def __init__(self, label_shift=0):
-        """Initialize `LabelShift` class."""
-        self.label_shift = label_shift
-
-    def __call__(self, sample):
-        """Convert label to label_shift."""
-        images, labels = sample
-        if isinstance(labels, np.ndarray):
-            labels = labels - self.label_shift
-        elif isinstance(labels, list):
-            if isinstance(labels[0], tuple):
-                labels = [tuple(np.array(label) - self.label_shift) for label in labels]
-            elif isinstance(labels[0], np.ndarray):
-                labels = [label - self.label_shift for label in labels]
-            else:
-                labels = np.array(labels) - self.label_shift
-                labels = labels.tolist()
-        else:
-            labels = np.array(labels) - self.label_shift
-        return images, labels
-
-
-class ImageRecordDataset(object):
-    """Tensorflow imageNet database in tf record format.
-
-    Please arrange data in this way:
-        root/validation-000-of-100
-        root/validation-001-of-100
-        ...
-        root/validation-099-of-100
-    The file name needs to follow this pattern: '* - * -of- *'
-
-    Args: root (str): Root directory of dataset.
-          transform (transform object, default=None):  transform to process input data.
-          filter (Filter objects, default=None): filter out examples according
-                                                 to specific conditions.
-    """
-
-    """Configuration for Imagenet dataset."""
-
-    def __new__(cls, root, transform=None, filter=None):
-        """Build a new object of TensorflowImageRecord class."""
-        from tensorflow.python.platform import gfile  # pylint: disable=no-name-in-module
-
-        glob_pattern = os.path.join(root, "*-*-of-*")
-        file_names = gfile.Glob(glob_pattern)
-        if not file_names:
-            raise ValueError("Found no files in --root matching: {}".format(glob_pattern))
-
-        # pylint: disable=no-name-in-module
-        from tensorflow.python.data.experimental import parallel_interleave
-
-        ds = tf.data.TFRecordDataset.list_files(file_names, shuffle=False)
-        ds = ds.apply(parallel_interleave(tf.data.TFRecordDataset, cycle_length=len(file_names)))
-
-        if transform is not None:
-            transform.transform_list.insert(0, ParseDecodeImagenet())
-        else:
-            transform = ParseDecodeImagenet()
-        ds = ds.map(transform, num_parallel_calls=None)
-        ds = ds.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)  # this number can be tuned
-        return ds
-
-
-class BaseMetric(object):
-    """The base class of Metric."""
-
-    def __init__(self, metric, single_output=False, hvd=None):
-        """Initialize the basic metric.
-
-        Args:
-            metric: The metric class.
-            single_output: Whether the output is single or not, defaults to False.
-            hvd: The Horovod class for distributed training, defaults to None.
-        """
-        self._metric_cls = metric
-        self._single_output = single_output
-        self._hvd = hvd
-
-    def __call__(self, *args, **kwargs):
-        """Evaluate the model predictions, and the reference.
-
-        Returns:
-            The class itself.
-        """
-        self._metric = self._metric_cls(*args, **kwargs)
-        return self
-
-    @abstractmethod
-    def update(self, preds, labels=None, sample_weight=None):
-        """Update the state that need to be evaluated.
-
-        Args:
-            preds: The prediction result.
-            labels: The reference. Defaults to None.
-            sample_weight: The sampling weight. Defaults to None.
-
-        Raises:
-            NotImplementedError: The method should be implemented by subclass.
-        """
-        raise NotImplementedError
-
-    @abstractmethod
-    def reset(self):
-        """Clear the predictions and labels.
-
-        Raises:
-            NotImplementedError: The method should be implemented by subclass.
-        """
-        raise NotImplementedError
-
-    @abstractmethod
-    def result(self):
-        """Evaluate the difference between predictions and labels.
-
-        Raises:
-            NotImplementedError: The method should be implemented by subclass.
-        """
-        raise NotImplementedError
-
-    @property
-    def metric(self):
-        """Return its metric class.
-
-        Returns:
-            The metric class.
-        """
-        return self._metric_cls
-
-    @property
-    def hvd(self):
-        """Return its hvd class.
-
-        Returns:
-            The hvd class.
-        """
-        return self._hvd
-
-    @hvd.setter
-    def hvd(self, hvd):
-        """Set its hvd.
-
-        Args:
-            hvd: The Horovod class for distributed training.
-        """
-        self._hvd = hvd
-
-
-class TopKMetric(BaseMetric):
-    """Compute Top-k Accuracy classification score for Tensorflow model.
-
-    This metric computes the number of times where the correct label is among
-    the top k labels predicted.
-
-    Attributes:
-        k (int): The number of most likely outcomes considered to find the correct label.
-        num_correct: The number of predictions that were correct classified.
-        num_sample: The total number of predictions.
-    """
-
-    def __init__(self, k=1):
-        """Initialize the k, number of samples and correct predictions.
-
-        Args:
-            k: The number of most likely outcomes considered to find the correct label.
-        """
-        self.k = k
-        self.num_correct = 0
-        self.num_sample = 0
-
-    def update(self, preds, labels, sample_weight=None):
-        """Add the predictions and labels.
-
-        Args:
-            preds: The predictions.
-            labels: The labels corresponding to the predictions.
-            sample_weight: The sample weight.
-        """
-        preds, labels = TopKMetric._topk_shape_validate(preds, labels)
-
-        labels = labels.reshape([len(labels)])
-        with tf.Graph().as_default() as acc_graph:
-            topk = tf.nn.in_top_k(
-                predictions=tf.constant(preds, dtype=tf.float32), targets=tf.constant(labels, dtype=tf.int32), k=self.k
-            )
-            fp32_topk = tf.cast(topk, tf.float32)
-            correct_tensor = tf.reduce_sum(input_tensor=fp32_topk)
-
-            with tf.compat.v1.Session() as acc_sess:
-                correct = acc_sess.run(correct_tensor)
-
-        self.num_sample += len(labels)
-        self.num_correct += correct
-
-    def reset(self):
-        """Reset the number of samples and correct predictions."""
-        self.num_correct = 0
-        self.num_sample = 0
-
-    def result(self):
-        """Compute the top-k score.
-
-        Returns:
-            The top-k score.
-        """
-        if self.num_sample == 0:
-            logger.warning("Sample num during evaluation is 0.")
-            return 0
-        elif getattr(self, "_hvd", None) is not None:  # pragma: no cover
-            allgather_num_correct = sum(self._hvd.allgather_object(self.num_correct))
-            allgather_num_sample = sum(self._hvd.allgather_object(self.num_sample))
-            return allgather_num_correct / allgather_num_sample
-        return self.num_correct / self.num_sample
-
-    @staticmethod
-    def _topk_shape_validate(preds, labels):
-        # preds shape can be Nxclass_num or class_num(N=1 by default)
-        # it's more suitable for 'Accuracy' with preds shape Nx1(or 1) output from argmax
-        if isinstance(preds, int):
-            preds = [preds]
-            preds = np.array(preds)
-        elif isinstance(preds, np.ndarray):
-            preds = np.array(preds)
-        elif isinstance(preds, list):
-            preds = np.array(preds)
-            preds = preds.reshape((-1, preds.shape[-1]))
-
-        # consider labels just int value 1x1
-        if isinstance(labels, int):
-            labels = [labels]
-            labels = np.array(labels)
-        elif isinstance(labels, tuple):
-            labels = np.array([labels])
-            labels = labels.reshape((labels.shape[-1], -1))
-        elif isinstance(labels, list):
-            if isinstance(labels[0], int):
-                labels = np.array(labels)
-                labels = labels.reshape((labels.shape[0], 1))
-            elif isinstance(labels[0], tuple):
-                labels = np.array(labels)
-                labels = labels.reshape((labels.shape[-1], -1))
-            else:
-                labels = np.array(labels)
-        # labels most have 2 axis, 2 cases: N(or Nx1 sparse) or Nxclass_num(one-hot)
-        # only support 2 dimension one-shot labels
-        # or 1 dimension one-hot class_num will confuse with N
-
-        if len(preds.shape) == 1:
-            N = 1
-            class_num = preds.shape[0]
-            preds = preds.reshape([-1, class_num])
-        elif len(preds.shape) >= 2:
-            N = preds.shape[0]
-            preds = preds.reshape([N, -1])
-            class_num = preds.shape[1]
-
-        label_N = labels.shape[0]
-        assert label_N == N, "labels batch size should same with preds"
-        labels = labels.reshape([N, -1])
-        # one-hot labels will have 2 dimension not equal 1
-        if labels.shape[1] != 1:
-            labels = labels.argsort()[..., -1:]
-        return preds, labels
-    
-
-class TFDataLoader(object):  # pragma: no cover
-    """Tensorflow dataloader class.
-
-    In tensorflow1.x dataloader is coupled with the graph, but it also support feed_dict
-    method to do session run, this dataloader is designed to satisfy the usage of feed dict
-    in tf1.x. Although it's a general dataloader and can be used in MXNet and PyTorch.
-
-    Args:
-        dataset: obj. wrapper of needed data.
-        batch_size: int. batch size
-    """
-
-    def __init__(self, dataset, batch_size=1, last_batch="rollover"):
-        """Initialize `TFDataDataLoader` class."""
-        self.dataset = dataset
-        self.last_batch = last_batch
-        self.batch_size = batch_size
-        dataset = dataset.batch(batch_size)
-
-    def batch(self, batch_size, last_batch="rollover"):
-        """Dataset return data per batch."""
-        drop_last = False if last_batch == "rollover" else True
-        self.batch_size = batch_size
-        self.dataset = self.dataset.batch(batch_size, drop_last)
-
-    def __iter__(self):
-        """Iterate dataloader."""
-        return self._generate_dataloader(
-            self.dataset,
-            batch_size=self.batch_size,
-            last_batch=self.last_batch,
-        )
-
-    def _generate_dataloader(
-        self,
-        dataset,
-        batch_size=1,
-        last_batch="rollover",
-        collate_fn=None,
-        sampler=None,
-        batch_sampler=None,
-        num_workers=None,
-        pin_memory=None,
-        distributed=False,
-    ):
-        """Yield data."""
-        drop_last = False if last_batch == "rollover" else True
-
-        def check_dynamic_shape(element_spec):
-            if isinstance(element_spec, collections.abc.Sequence):
-                return any([check_dynamic_shape(ele) for ele in element_spec])
-            elif isinstance(element_spec, tf.TensorSpec):
-                return True if element_spec.shape.num_elements() is None else False
-            else:
-                raise ValueError("unrecognized element spec...")
-
-        def squeeze_output(output):
-            if isinstance(output, collections.abc.Sequence):
-                return [squeeze_output(ele) for ele in output]
-            elif isinstance(output, np.ndarray):
-                return np.squeeze(output, axis=0)
-            else:
-                raise ValueError("not supported output format....")
-
-        if tf.executing_eagerly():
-            index = 0
-            outputs = []
-            for iter_tensors in dataset:
-                samples = []
-                iter_inputs, iter_labels = iter_tensors[0], iter_tensors[1]
-                if isinstance(iter_inputs, tf.Tensor):
-                    samples.append(iter_inputs.numpy())
-                else:
-                    samples.append(tuple(iter_input.numpy() for iter_input in iter_inputs))
-                if isinstance(iter_labels, tf.Tensor):
-                    samples.append(iter_labels.numpy())
-                else:
-                    samples.append([np.array(l) for l in iter_labels])
-                index += 1
-                outputs.append(samples)
-                if index == batch_size:
-                    outputs = default_collate(outputs)
-                    yield outputs
-                    outputs = []
-                    index = 0
-            if len(outputs) > 0:
-                outputs = default_collate(outputs)
-                yield outputs
-        else:
-            try_single_batch = check_dynamic_shape(dataset.element_spec)
-            dataset = dataset.batch(1 if try_single_batch else batch_size, drop_last)
-            ds_iterator = tf.compat.v1.data.make_one_shot_iterator(dataset)
-            iter_tensors = ds_iterator.get_next()
-            data_config = tf.compat.v1.ConfigProto()
-            data_config.use_per_session_threads = 1
-            data_config.intra_op_parallelism_threads = 1
-            data_config.inter_op_parallelism_threads = 16
-            data_sess = tf.compat.v1.Session(config=data_config)
-            # pylint: disable=no-name-in-module
-            from tensorflow.python.framework.errors_impl import OutOfRangeError
-
-            while True:
-                if not try_single_batch:
-                    try:
-                        outputs = data_sess.run(iter_tensors)
-                        yield outputs
-                    except OutOfRangeError:
-                        data_sess.close()
-                        return
-                else:
-                    try:
-                        outputs = []
-                        for i in range(0, batch_size):
-                            outputs.append(squeeze_output(data_sess.run(iter_tensors)))
-                        outputs = default_collate(outputs)
-                        yield outputs
-                    except OutOfRangeError:
-                        if len(outputs) == 0:
-                            data_sess.close()
-                            return
-                        else:
-                            outputs = default_collate(outputs)
-                            yield outputs
-                            data_sess.close()
-                            return
diff --git a/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/main.py b/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/main.py
deleted file mode 100644
index ed2013159d6..00000000000
--- a/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/main.py
+++ /dev/null
@@ -1,147 +0,0 @@
-#
-# -*- coding: utf-8 -*-
-#
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-import time
-
-import numpy as np
-import tensorflow as tf
-
-from argparse import ArgumentParser
-from data_process import (
-    ImageRecordDataset, 
-    ComposeTransform, 
-    ResizeCropImagenet, 
-    LabelShift,
-    TFDataLoader,
-    TopKMetric
-)
-
-tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
-
-arg_parser = ArgumentParser(description='Parse args')
-arg_parser.add_argument('-g', "--input-graph",
-                        help='Specify the input graph for the transform tool',
-                        dest='input_graph')
-arg_parser.add_argument("--output-graph",
-                        help='Specify tune result model save dir',
-                        dest='output_graph')
-arg_parser.add_argument('--benchmark', dest='benchmark', action='store_true', help='run benchmark')
-arg_parser.add_argument('--mode', dest='mode', default='performance', help='benchmark mode')
-arg_parser.add_argument('--tune', dest='tune', action='store_true', help='use neural_compressor to tune.')
-arg_parser.add_argument('--dataset_location', dest='dataset_location',
-                          help='location of calibration dataset and evaluate dataset')
-arg_parser.add_argument('--batch_size', type=int, default=32, dest='batch_size', help='batch_size of benchmark')
-arg_parser.add_argument('--iters', type=int, default=100, dest='iters', help='interations')
-args = arg_parser.parse_args()
-
-def evaluate(model, eval_dataloader, metric, postprocess=None):
-    """Custom evaluate function to estimate the accuracy of the model.
-
-    Args:
-        model (tf.Graph_def): The input model graph
-        
-    Returns:
-        accuracy (float): evaluation result, the larger is better.
-    """
-    from neural_compressor.tensorflow import Model
-    model = Model(model)
-    input_tensor = model.input_tensor
-    output_tensor = model.output_tensor if len(model.output_tensor)>1 else \
-                        model.output_tensor[0]
-    iteration = -1
-    if args.benchmark and args.mode == 'performance':
-        iteration = args.iters
-
-    def eval_func(dataloader):
-        latency_list = []
-        for idx, (inputs, labels) in enumerate(dataloader):
-            # dataloader should keep the order and len of inputs same with input_tensor
-            inputs = np.array([inputs])
-            feed_dict = dict(zip(input_tensor, inputs))
-
-            start = time.time()
-            predictions = model.sess.run(output_tensor, feed_dict)
-            end = time.time()
-
-            if postprocess:
-                predictions, labels = postprocess((predictions, labels))
-
-            metric.update(predictions, labels)
-            latency_list.append(end-start)
-            if idx + 1 == iteration:
-                break
-        latency = np.array(latency_list).mean() / args.batch_size
-        return latency
-
-    latency = eval_func(eval_dataloader)
-    if args.benchmark and args.mode == 'performance':
-        print("Batch size = {}".format(args.batch_size))
-        print("Latency: {:.3f} ms".format(latency * 1000))
-        print("Throughput: {:.3f} images/sec".format(1. / latency))
-    acc = metric.result()
-    return acc
-
-class eval_classifier_optimized_graph:
-    """Evaluate image classifier with optimized TensorFlow graph."""
-
-    def run(self):
-        """This is neural_compressor function include tuning, export and benchmark option."""
-        from neural_compressor.common import set_random_seed
-        set_random_seed(9527)
-
-        if args.tune:
-            from neural_compressor.tensorflow import StaticQuantConfig, quantize_model
-
-            dataset = ImageRecordDataset(
-                root=args.dataset_location, 
-                transform=ComposeTransform(transform_list= [
-                    ResizeCropImagenet(height=224, width=224, scale=0.017, mean_value=[123.68, 116.78, 103.94]),
-                    ]
-                )
-            )
-            calib_dataloader = TFDataLoader(dataset=dataset, batch_size=10)
-            
-            quant_config = StaticQuantConfig(weight_granularity="per_channel")
-            q_model = quantize_model(args.input_graph, quant_config, calib_dataloader)
-            q_model.save(args.output_graph)
-
-        if args.benchmark:
-            dataset = ImageRecordDataset(
-                root=args.dataset_location, 
-                transform=ComposeTransform(transform_list= [
-                    ResizeCropImagenet(height=224, width=224, scale=0.017, mean_value=[123.68, 116.78, 103.94]),
-                    ]
-                )
-            )
-            dataloader = TFDataLoader(dataset=dataset, batch_size=args.batch_size)
-
-            def eval(model):
-                top1 = TopKMetric(k=1)
-                postprocess = LabelShift(label_shift=1)
-                return evaluate(model, dataloader, top1, postprocess)
-
-            if args.mode == 'performance':
-                eval(args.input_graph)
-            elif args.mode == 'accuracy':
-                acc_result = eval(args.input_graph)
-                print("Batch size = %d" % dataloader.batch_size)
-                print("Accuracy: %.5f" % acc_result)
-
-if __name__ == "__main__":
-    evaluate_opt_graph = eval_classifier_optimized_graph()
-    evaluate_opt_graph.run()
diff --git a/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/requirements.txt b/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/requirements.txt
deleted file mode 100644
index 2755e1a41ac..00000000000
--- a/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/requirements.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-tensorflow
-neural-compressor
diff --git a/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/run_benchmark.sh b/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/run_benchmark.sh
deleted file mode 100644
index 8ecac837cf7..00000000000
--- a/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/run_benchmark.sh
+++ /dev/null
@@ -1,51 +0,0 @@
-#!/bin/bash
-set -x
-
-function main {
-
-  init_params "$@"
-  run_benchmark
-
-}
-
-# init params
-function init_params {
-  batch_size=32
-  iters=100
-
-  for var in "$@"
-  do
-    case $var in
-      --input_model=*)
-          input_model=$(echo $var |cut -f2 -d=)
-      ;;
-      --mode=*)
-          mode=$(echo $var |cut -f2 -d=)
-      ;;
-      --dataset_location=*)
-          dataset_location=$(echo $var |cut -f2 -d=)
-      ;;
-      --batch_size=*)
-          batch_size=$(echo $var |cut -f2 -d=)
-      ;;
-      --iters=*)
-          iters=$(echo $var |cut -f2 -d=)
-      ;;
-    esac
-  done
-
-}
-
-# run_tuning
-function run_benchmark {
-
-    python main.py \
-            --input-graph ${input_model} \
-            --mode ${mode} \
-            --dataset_location ${dataset_location} \
-            --batch_size ${batch_size} \
-            --benchmark \
-            --iters ${iters}
-}
-
-main "$@"
diff --git a/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/run_quant.sh b/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/run_quant.sh
deleted file mode 100644
index 6a9e1b859c9..00000000000
--- a/examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/run_quant.sh
+++ /dev/null
@@ -1,39 +0,0 @@
-#!/bin/bash
-set -x
-
-function main {
-  init_params "$@"
-  run_tuning
-
-}
-
-# init params
-function init_params {
-
-  for var in "$@"
-  do
-    case $var in
-      --input_model=*)
-          input_model=$(echo $var |cut -f2 -d=)
-      ;;
-      --output_model=*)
-          output_model=$(echo $var |cut -f2 -d=)
-      ;;
-      --dataset_location=*)
-          dataset_location=$(echo $var |cut -f2 -d=)
-      ;;
-    esac
-  done
-
-}
-
-# run_tuning
-function run_tuning {
-    python main.py \
-            --input-graph ${input_model} \
-            --output-graph ${output_model} \
-            --dataset_location ${dataset_location} \
-            --tune
-}
-
-main "$@"

From f37f34143b17d65aa6726065be5842571957f826 Mon Sep 17 00:00:00 2001
From: zehao-intel <zehao.huang@intel.com>
Date: Thu, 25 Jul 2024 13:59:24 +0800
Subject: [PATCH 14/14] change folder name of examples

Signed-off-by: zehao-intel <zehao.huang@intel.com>
---
 examples/.config/model_params_keras_3x.json            |  4 ++--
 examples/.config/model_params_tensorflow_3x.json       | 10 +++++-----
 .../inception_v3/quantization/ptq/README.md            |  0
 .../inception_v3/quantization/ptq/data_process.py      |  0
 .../inception_v3/quantization/ptq/main.py              |  0
 .../inception_v3/quantization/ptq/requirements.txt     |  0
 .../inception_v3/quantization/ptq/run_benchmark.sh     |  0
 .../inception_v3/quantization/ptq/run_quant.sh         |  0
 .../mobilenet_v2/quantization/ptq/README.md            |  0
 .../mobilenet_v2/quantization/ptq/data_process.py      |  0
 .../mobilenet_v2/quantization/ptq/main.py              |  0
 .../mobilenet_v2/quantization/ptq/requirements.txt     |  0
 .../mobilenet_v2/quantization/ptq/run_benchmark.sh     |  0
 .../mobilenet_v2/quantization/ptq/run_quant.sh         |  0
 .../{cv => image_recognition}/prepare_dataset.sh       |  0
 .../resnet_v2_50/quantization/ptq/README.md            |  0
 .../resnet_v2_50/quantization/ptq/data_process.py      |  0
 .../resnet_v2_50/quantization/ptq/main.py              |  0
 .../resnet_v2_50/quantization/ptq/requirements.txt     |  0
 .../resnet_v2_50/quantization/ptq/run_benchmark.sh     |  0
 .../resnet_v2_50/quantization/ptq/run_quant.sh         |  0
 .../vgg16/quantization/ptq/README.md                   |  0
 .../vgg16/quantization/ptq/data_process.py             |  0
 .../vgg16/quantization/ptq/main.py                     |  0
 .../vgg16/quantization/ptq/requirements.txt            |  0
 .../vgg16/quantization/ptq/run_benchmark.sh            |  0
 .../vgg16/quantization/ptq/run_quant.sh                |  0
 .../vision_transformer/quantization/ptq/README.md      |  0
 .../vision_transformer/quantization/ptq/__init__.py    |  0
 .../quantization/ptq/data_process.py                   |  0
 .../vision_transformer/quantization/ptq/main.py        |  0
 .../quantization/ptq/requirements.txt                  |  0
 .../quantization/ptq/run_benchmark.sh                  |  0
 .../vision_transformer/quantization/ptq/run_quant.sh   |  0
 .../imagenet_prepare/build_imagenet_data.py            |  0
 .../imagenet_prepare/download_and_convert_imagenet.sh  |  0
 .../imagenet_prepare/download_imagenet.sh              |  0
 .../imagenet_prepare/imagenet_lsvrc_2015_synsets.txt   |  0
 .../imagenet_prepare/imagenet_metadata.txt             |  0
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 54 files changed, 7 insertions(+), 7 deletions(-)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/inception_v3/quantization/ptq/README.md (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/inception_v3/quantization/ptq/data_process.py (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/inception_v3/quantization/ptq/main.py (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/inception_v3/quantization/ptq/requirements.txt (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/inception_v3/quantization/ptq/run_benchmark.sh (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/inception_v3/quantization/ptq/run_quant.sh (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/mobilenet_v2/quantization/ptq/README.md (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/mobilenet_v2/quantization/ptq/data_process.py (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/mobilenet_v2/quantization/ptq/main.py (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/mobilenet_v2/quantization/ptq/requirements.txt (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/mobilenet_v2/quantization/ptq/run_benchmark.sh (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/mobilenet_v2/quantization/ptq/run_quant.sh (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/prepare_dataset.sh (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/resnet_v2_50/quantization/ptq/README.md (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/resnet_v2_50/quantization/ptq/data_process.py (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/resnet_v2_50/quantization/ptq/main.py (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/resnet_v2_50/quantization/ptq/requirements.txt (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/resnet_v2_50/quantization/ptq/run_benchmark.sh (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/resnet_v2_50/quantization/ptq/run_quant.sh (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/vgg16/quantization/ptq/README.md (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/vgg16/quantization/ptq/data_process.py (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/vgg16/quantization/ptq/main.py (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/vgg16/quantization/ptq/requirements.txt (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/vgg16/quantization/ptq/run_benchmark.sh (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/vgg16/quantization/ptq/run_quant.sh (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/vision_transformer/quantization/ptq/README.md (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/vision_transformer/quantization/ptq/__init__.py (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/vision_transformer/quantization/ptq/data_process.py (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/vision_transformer/quantization/ptq/main.py (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/vision_transformer/quantization/ptq/requirements.txt (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/vision_transformer/quantization/ptq/run_benchmark.sh (100%)
 rename examples/3.x_api/tensorflow/{cv => image_recognition}/vision_transformer/quantization/ptq/run_quant.sh (100%)
 rename examples/3.x_api/tensorflow/keras/{cv => image_recognition}/imagenet_prepare/build_imagenet_data.py (100%)
 rename examples/3.x_api/tensorflow/keras/{cv => image_recognition}/imagenet_prepare/download_and_convert_imagenet.sh (100%)
 rename examples/3.x_api/tensorflow/keras/{cv => image_recognition}/imagenet_prepare/download_imagenet.sh (100%)
 rename examples/3.x_api/tensorflow/keras/{cv => image_recognition}/imagenet_prepare/imagenet_lsvrc_2015_synsets.txt (100%)
 rename examples/3.x_api/tensorflow/keras/{cv => image_recognition}/imagenet_prepare/imagenet_metadata.txt (100%)
 rename examples/3.x_api/tensorflow/keras/{cv => image_recognition}/inception_v3/quantization/ptq/README.md (100%)
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 rename examples/3.x_api/tensorflow/keras/{cv => image_recognition}/inception_v3/quantization/ptq/prepare_model.py (100%)
 rename examples/3.x_api/tensorflow/keras/{cv => image_recognition}/inception_v3/quantization/ptq/requirements.txt (100%)
 rename examples/3.x_api/tensorflow/keras/{cv => image_recognition}/inception_v3/quantization/ptq/run_benchmark.sh (100%)
 rename examples/3.x_api/tensorflow/keras/{cv => image_recognition}/inception_v3/quantization/ptq/run_quant.sh (100%)
 rename examples/3.x_api/tensorflow/keras/{cv => image_recognition}/prepare_dataset.sh (100%)
 rename examples/3.x_api/tensorflow/keras/{cv => image_recognition}/resnet_v2_50/quantization/ptq/README.md (100%)
 rename examples/3.x_api/tensorflow/keras/{cv => image_recognition}/resnet_v2_50/quantization/ptq/data_process.py (100%)
 rename examples/3.x_api/tensorflow/keras/{cv => image_recognition}/resnet_v2_50/quantization/ptq/main.py (100%)
 rename examples/3.x_api/tensorflow/keras/{cv => image_recognition}/resnet_v2_50/quantization/ptq/prepare_model.py (100%)
 rename examples/3.x_api/tensorflow/keras/{cv => image_recognition}/resnet_v2_50/quantization/ptq/requirements.txt (100%)
 rename examples/3.x_api/tensorflow/keras/{cv => image_recognition}/resnet_v2_50/quantization/ptq/run_benchmark.sh (100%)
 rename examples/3.x_api/tensorflow/keras/{cv => image_recognition}/resnet_v2_50/quantization/ptq/run_quant.sh (100%)

diff --git a/examples/.config/model_params_keras_3x.json b/examples/.config/model_params_keras_3x.json
index 40bca7c9e22..bac8a06b4a3 100644
--- a/examples/.config/model_params_keras_3x.json
+++ b/examples/.config/model_params_keras_3x.json
@@ -1,14 +1,14 @@
 {
   "keras": {
     "resnetv2_50": {
-      "model_src_dir": "keras/cv/resnet_v2_50/quantization/ptq",
+      "model_src_dir": "keras/image_recognition/resnet_v2_50/quantization/ptq",
       "dataset_location": "/tf_dataset/dataset/imagenet",
       "input_model": "/tf_dataset2/models/tensorflow/resnetv2_50_keras/saved_model",
       "main_script": "main.py",
       "batch_size": 32
     },
     "inception_v3": {
-      "model_src_dir": "keras/cv/inception_v3/quantization/ptq",
+      "model_src_dir": "keras/image_recognition/inception_v3/quantization/ptq",
       "dataset_location": "/tf_dataset/dataset/imagenet",
       "input_model": "/tf_dataset2/models/tensorflow/inception_v3_keras/saved_model",
       "main_script": "main.py",
diff --git a/examples/.config/model_params_tensorflow_3x.json b/examples/.config/model_params_tensorflow_3x.json
index 6bd525ff1b9..550f4d01219 100644
--- a/examples/.config/model_params_tensorflow_3x.json
+++ b/examples/.config/model_params_tensorflow_3x.json
@@ -51,7 +51,7 @@
       "batch_size": 64
     },
     "inception_v3": {
-      "model_src_dir": "cv/inception_v3/quantization/ptq",
+      "model_src_dir": "image_recognition/inception_v3/quantization/ptq",
       "dataset_location": "/tf_dataset/dataset/imagenet",
       "input_model": "/tf_dataset/pre-trained-models/inceptionv3/fp32/freezed_inceptionv3.pb",
       "main_script": "main.py",
@@ -59,28 +59,28 @@
       "fp32_model_url": "https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_8/inceptionv3_fp32_pretrained_model.pb"
     },
     "mobilenetv2": {
-      "model_src_dir": "cv/mobilenet_v2/quantization/ptq",
+      "model_src_dir": "image_recognition/mobilenet_v2/quantization/ptq",
       "dataset_location": "/tf_dataset/dataset/imagenet",
       "input_model": "/tf_dataset/pre-train-model-slim/pbfile/frozen_pb/frozen_mobilenet_v2.pb",
       "main_script": "main.py",
       "batch_size": 32
     },
     "resnetv2_50": {
-      "model_src_dir": "cv/resnet_v2_50/quantization/ptq",
+      "model_src_dir": "image_recognition/resnet_v2_50/quantization/ptq",
       "dataset_location": "/tf_dataset/dataset/imagenet",
       "input_model": "/tf_dataset/pre-train-model-slim/pbfile/frozen_pb/frozen_resnet_v2_50.pb",
       "main_script": "main.py",
       "batch_size": 32
     },
     "vgg16": {
-      "model_src_dir": "cv/vgg16/quantization/ptq",
+      "model_src_dir": "image_recognition/vgg16/quantization/ptq",
       "dataset_location": "/tf_dataset/dataset/imagenet",
       "input_model": "/tf_dataset/pre-train-model-slim/pbfile/frozen_pb/frozen_vgg16.pb",
       "main_script": "main.py",
       "batch_size": 32
     },
     "ViT": {
-      "model_src_dir": "cv/vision_transformer/quantization/ptq",
+      "model_src_dir": "image_recognition/vision_transformer/quantization/ptq",
       "dataset_location": "/tf_dataset/dataset/imagenet",
       "input_model": "/tf_dataset/tensorflow/vit/HF-ViT-Base16-Img224-frozen.pb",
       "main_script": "main.py",
diff --git a/examples/3.x_api/tensorflow/cv/inception_v3/quantization/ptq/README.md b/examples/3.x_api/tensorflow/image_recognition/inception_v3/quantization/ptq/README.md
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rename to examples/3.x_api/tensorflow/image_recognition/inception_v3/quantization/ptq/README.md
diff --git a/examples/3.x_api/tensorflow/cv/inception_v3/quantization/ptq/data_process.py b/examples/3.x_api/tensorflow/image_recognition/inception_v3/quantization/ptq/data_process.py
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diff --git a/examples/3.x_api/tensorflow/cv/inception_v3/quantization/ptq/run_quant.sh b/examples/3.x_api/tensorflow/image_recognition/inception_v3/quantization/ptq/run_quant.sh
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diff --git a/examples/3.x_api/tensorflow/cv/mobilenet_v2/quantization/ptq/README.md b/examples/3.x_api/tensorflow/image_recognition/mobilenet_v2/quantization/ptq/README.md
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