Uploading yolov7 and yolov8 converted document.

doc/quick_start/tutorial_for_yolov5_converted.md

@@ -72,6 +72,17 @@ mkdir -p  /opt/openvino_toolkit/models/convert/public/yolov5n/FP32/
 sudo cp yolov5n.bin yolov5n.mapping yolov5n.xml /opt/openvino_toolkit/models/convert/public/yolov5n/FP32/
 ```
 
+# yolov5 optimize to yolov5-int8
+```
+The yolov5 optimize to yolov5-int8 refer to the link:
+
+https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/111-yolov5-quantization-migration
+
+The installation guide
+https://github.com/openvinotoolkit/openvino_notebooks/blob/main/README.md#-installation-guide
+
+```
+
 # FAQ
 
 <p>

doc/quick_start/tutorial_for_yolov7_converted.md

@@ -0,0 +1,103 @@
+# Tutorial_For_yolov7_Converted
+
+# Introduction
+This document describes a method to convert YOLOv7 nano PyTorch weight files with the .pt extension to ONNX weight files, and a method to convert ONNX weight files to IR 
+files using the OpenVINO model optimizer. This method can help OpenVINO users optimize YOLOv7 for deployment in practical applications.
+
+## Reference Phrase
+|Term|Description|
+|---|---|
+|OpenVINO|Open Visual Inference & Neural Network Optimization|
+|ONNX|Open Neural Network Exchange|
+|YOLO|You Only Look Once|
+|IR|Intermediate Representation|
+
+## Reference Document
+|Doc|Link|
+|---|---|
+|OpenVINO|[openvino_2_0_transition_guide](https://docs.openvino.ai/latest/openvino_2_0_transition_guide.html)|
+|YOLOv7|[yolov7](https://github.com/WongKinYiu/yolov7)|
+
+# Convert Weight File to ONNX
+* Copy YOLOv7 Repository from GitHub
+```
+git clone https://github.com/WongKinYiu/yolov7.git
+```
+
+* Set Environment for Installing YOLOv7
+```
+cd yolov7
+python3 -m venv yolo_env            // Create a virtual python environment
+source yolo_env/bin/activate        // Activate environment
+pip install -r requirements.txt     // Install yolov7 prerequisites
+pip install onnx                    // Install ONNX
+pip install nvidia-pyindex          // Add NVIDIA PIP index
+pip install onnx-graphsurgeon       // Install GraphSurgeon
+```
+
+* Download PyTorch Weights
+```
+mkdir model_convert && cd model_convert
+wget "https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt"
+```
+
+* Convert PyTorch weights to ONNX weights
+YOLOv7 repository provides export.py script, which can be used to convert PyTorch weight to ONNX weight.
+```
+cd ..
+python3 export.py --weights model_convert/yolov7.pt
+```
+
+# Convert ONNX files to IR files
+After obtaining the ONNX weight file from the previous section [Convert Weight File to ONNX](#convert-weight-file-to-onnx), we can use the model optimizer to convert it to an IR file.
+
+* Install the OpenVINO Model Optimizer Environment
+To use the model optimizer, you need to run the following command to install some necessary components (if you are still in the yolo_env virtual environment, you need to run the **deactivate** command to exit the environment or start a new terminal).
+```
+python3 -m venv ov_env                      // Create openVINO virtual environment
+source ov_env/bin/activate                  // Activate environment
+python -m pip install --upgrade pip         // Upgrade pip
+pip install openvino[onnx]==2022.3.0        // Install OpenVINO for ONNX
+pip install openvino-dev[onnx]==2022.3.0    // Install OpenVINO Dev Tool for ONNX
+```
+
+* Generate IR file
+```
+cd model_convert
+mo --input_model yolov7.onnx
+```
+Then we will get three files: yolov7.xml, yolov7.bin, and yolov7.mapping under the model_convert folder.
+
+# Move to the Recommended Model Path
+```
+cd ~/yolov7/model_convert
+mkdir -p  /opt/openvino_toolkit/models/convert/public/yolov7/FP32/
+sudo cp yolov7.bin yolov7.mapping yolov7.xml /opt/openvino_toolkit/models/convert/public/yolov7/FP32/
+```
+
+# yolov7 optimize to yolov7-int8
+```
+The yolov7 optimize to yolov7-int8 refer to the link:
+
+https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/226-yolov7-optimization
+
+The installation guide
+https://github.com/openvinotoolkit/openvino_notebooks/blob/main/README.md#-installation-guide
+
+```
+
+
+# FAQ
+
+<p>
+<details>
+<summary>How to install the python3-venv package?</summary>
+
+On Debian/Ubuntu systems, you need to install the python3-venv package using the following command.
+```
+apt-get update
+apt-get install python3-venv
+```
+You may need to use sudo with that command. After installing, recreate your virtual environment.
+</details>
+</p>

doc/quick_start/tutorial_for_yolov8_converted.md

@@ -0,0 +1,99 @@
+# Tutorial_For_yolov8_Converted
+
+# Introduction
+Ultralytics YOLOv8 is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility.
+YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation,
+image classification and pose estimation tasks.
+This document describes a method to convert YOLOv8 nano PyTorch weight files with the .pt extension to ONNX weight files, and a method to convert ONNX weight files to IR 
+files using the OpenVINO model optimizer. This method can help OpenVINO users optimize YOLOv8 for deployment in practical applications.
+
+## <div align="center">Documentation</div>
+
+See below for a quickstart installation and usage example, and see the [YOLOv8 Docs](https://docs.ultralytics.com) for full documentation on training, validation, prediction and deployment.
+
+<details open>
+<summary>Install</summary>
+
+
+Pip install the ultralytics package including all [requirements](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) in a [**Python>=3.7**](https://www.python.org/) environment with [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).
+
+```bash
+mkdir -p yolov8 && cd yolov8
+pip install ultralytics
+apt install python3.8-venv
+python3 -m venv openvino_env
+source openvino_env/bin/activate
+```
+
+ #### Train
+Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. For a full list of available arguments seethe Configuration page.
+YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command:
+
+```CLI
+# Build a new model from YAML and start training from scratch
+yolo detect train data=coco128.yaml model=yolov8n.yaml epochs=100 imgsz=640
+
+# Start training from a pretrained *.pt model
+yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
+```
+
+
+#### Val
+
+Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need to passed as the model retains it's training data and arguments as model attributes.
+```CLI
+# val official model
+yolo detect val model=yolov8n.pt
+
+```
+
+#### Predict
+Use a trained YOLOv8n model to run predictions on images.
+``` CLI
+# predict with official model
+yolo detect predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
+```
+
+#### Export
+Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
+```
+# export official model
+yolo export model=yolov8n.pt format=openvino
+
+```
+
+# Move to the Recommended Model Path
+```
+cd yolov8n_openvino_model
+
+mkdir -p  /opt/openvino_toolkit/models/convert/public/FP32/yolov8n
+
+sudo cp yolov8* /opt/openvino_toolkit/models/convert/public/FP32/yolov8n
+
+```
+
+# yolov8n optimize to yolov8n-int8
+```
+The yolov8n optimize to yolov8n-int8 refer to the link:
+
+https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/230-yolov8-optimization/230-yolov8-optimization.ipynb
+
+The installation guide
+https://github.com/openvinotoolkit/openvino_notebooks/blob/main/README.md#-installation-guide
+
+```
+
+# FAQ
+
+<p>
+<details>
+<summary>Reference link</summary>
+
+```
+https://github.com/ultralytics/ultralytics
+https://docs.ultralytics.com/tasks/detect/#predict
+
+```
+
+</details>
+</p>