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Update sotabench

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pull/82/head
Ross Wightman 5 years ago
parent 2f41905ba5
commit e42503e1d0
1 changed files with 6 additions and 1 deletions
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sotabench.py
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@ -93,7 +93,10 @@ model_list = [
_entry('resnet26d', 'ResNet-26-D', '1812.01187', _entry('resnet26d', 'ResNet-26-D', '1812.01187',
model_desc='Block cfg of ResNet-34 w/ Bottleneck, deep stem, and avg-pool in downsample layers.'), model_desc='Block cfg of ResNet-34 w/ Bottleneck, deep stem, and avg-pool in downsample layers.'),
_entry('resnet34', 'ResNet-34', '1812.01187'), _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('resnext50_32x4d', 'ResNeXt-50 32x4d', '1812.01187'),
_entry('resnext50d_32x4d', 'ResNeXt-50-D 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 " 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.'), 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', _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.'), 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', _entry('spnasnet_100', 'Single-Path NAS', '1904.02877',
model_desc='Trained in PyTorch with SGD, cosine LR decay'), model_desc='Trained in PyTorch with SGD, cosine LR decay'),
_entry('tf_efficientnet_b0', 'EfficientNet-B0 (AutoAugment)', '1905.11946', _entry('tf_efficientnet_b0', 'EfficientNet-B0 (AutoAugment)', '1905.11946',

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