Update sotabench

sotabench.py

@@ -93,7 +93,10 @@ model_list = [
     _entry('resnet26d', 'ResNet-26-D', '1812.01187',
            model_desc='Block cfg of ResNet-34 w/ Bottleneck, deep stem, and avg-pool in downsample layers.'),
     _entry('resnet34', 'ResNet-34', '1812.01187'),
-    _entry('resnet50', 'ResNet-50', '1812.01187'),
+    _entry('resnet50', 'ResNet-50', '1812.01187', model_desc='Trained with AugMix + JSD loss'),
+    _entry('resnet50', 'ResNet-50 (288x288 Mean-Max Pooling)', '1812.01187',
+           ttp=True, args=dict(img_size=288),
+           model_desc='Trained with AugMix + JSD loss'),
     _entry('resnext50_32x4d', 'ResNeXt-50 32x4d', '1812.01187'),
     _entry('resnext50d_32x4d', 'ResNeXt-50-D 32x4d', '1812.01187',
            model_desc="'D' variant (3x3 deep stem w/ avg-pool downscale). Trained with "
@@ -107,6 +110,8 @@ model_list = [
            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', '1812.01187',
            model_desc='Block cfg of SE-ResNeXt-34 w/ Bottleneck, deep tiered stem, and avg-pool in downsample layers.'),
+    _entry('seresnext26tn_32x4d', 'SE-ResNeXt-26-TN 32x4d', '1812.01187',
+           model_desc='Block cfg of SE-ResNeXt-34 w/ Bottleneck, deep tiered narrow 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',