Update sotabench.py with latest seresnext26 and efficientnet_b3 models

Author: rwightman

sotabench.py

@@ -46,7 +46,14 @@ def _entry(model_name, paper_model_name, paper_arxiv_id, batch_size=BATCH_SIZE,
            ttp=True, args=dict(img_size=320), batch_size=BATCH_SIZE//4),
     _entry('efficientnet_b0', 'EfficientNet-B0', '1905.11946'),
     _entry('efficientnet_b1', 'EfficientNet-B1', '1905.11946'),
-    _entry('efficientnet_b2', 'EfficientNet-B2', '1905.11946'),
+    _entry('efficientnet_b2', 'EfficientNet-B2', '1905.11946',
+           model_desc='Trained from scratch in PyTorch w/ RandAugment'),
+    _entry('efficientnet_b2a', 'EfficientNet-B2 (288x288, 1.0 crop)', '1905.11946',
+           model_desc='Trained from scratch in PyTorch w/ RandAugment'),
+    _entry('efficientnet_b3', 'EfficientNet-B3', '1905.11946',
+           model_desc='Trained from scratch in PyTorch w/ RandAugment'),
+    _entry('efficientnet_b3a', 'EfficientNet-B3 (320x320, 1.0 crop)', '1905.11946',
+           model_desc='Trained from scratch in PyTorch w/ RandAugment'),
     _entry('fbnetc_100', 'FBNet-C', '1812.03443',
            model_desc='Trained in PyTorch with RMSProp, exponential LR decay'),
     _entry('gluon_inception_v3', 'Inception V3', '1512.00567', model_desc='Ported from GluonCV Model Zoo'),
@@ -95,7 +102,11 @@ def _entry(model_name, paper_model_name, paper_arxiv_id, batch_size=BATCH_SIZE,
     _entry('seresnet18', 'SE-ResNet-18', '1709.01507'),
     _entry('seresnet34', 'SE-ResNet-34', '1709.01507'),
     _entry('seresnext26_32x4d', 'SE-ResNeXt-26 32x4d', '1709.01507',
+           model_desc='Block cfg of SE-ResNeXt-34 w/ Bottleneck'),
+    _entry('seresnext26d_32x4d', 'SE-ResNeXt-26-D 32x4d', '',
            model_desc='Block cfg of SE-ResNeXt-34 w/ Bottleneck, deep stem, and avg-pool in downsample layers.'),
+    _entry('seresnext26t_32x4d', 'SE-ResNeXt-26-T 32x4d', '',
+           model_desc='Block cfg of SE-ResNeXt-34 w/ Bottleneck, deep tiered stem, and avg-pool in downsample layers.'),
     _entry('spnasnet_100', 'Single-Path NAS', '1904.02877',
            model_desc='Trained in PyTorch with SGD, cosine LR decay'),
     _entry('tf_efficientnet_b0', 'EfficientNet-B0 (AutoAugment)', '1905.11946',