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@ -56,6 +56,8 @@ model_list = [
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model_desc='Trained from scratch in PyTorch w/ RandAugment'),
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model_desc='Trained from scratch in PyTorch w/ RandAugment'),
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_entry('efficientnet_es', 'EfficientNet-EdgeTPU-S', '1905.11946',
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_entry('efficientnet_es', 'EfficientNet-EdgeTPU-S', '1905.11946',
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model_desc='Trained from scratch in PyTorch w/ RandAugment'),
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model_desc='Trained from scratch in PyTorch w/ RandAugment'),
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_entry('efficientnet_em', 'EfficientNet-EdgeTPU-M', '1905.11946',
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model_desc='Trained from scratch in PyTorch w/ RandAugment'),
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_entry('gluon_inception_v3', 'Inception V3', '1512.00567', model_desc='Ported from GluonCV Model Zoo'),
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_entry('gluon_inception_v3', 'Inception V3', '1512.00567', model_desc='Ported from GluonCV Model Zoo'),
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_entry('gluon_resnet18_v1b', 'ResNet-18', '1812.01187', model_desc='Ported from GluonCV Model Zoo'),
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_entry('gluon_resnet18_v1b', 'ResNet-18', '1812.01187', model_desc='Ported from GluonCV Model Zoo'),
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@ -111,8 +113,11 @@ model_list = [
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model_desc="'D' variant (3x3 deep stem w/ avg-pool downscale). Trained with "
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model_desc="'D' variant (3x3 deep stem w/ avg-pool downscale). Trained with "
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"SGD w/ cosine LR decay, random-erasing (gaussian per-pixel noise) and label-smoothing"),
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"SGD w/ cosine LR decay, random-erasing (gaussian per-pixel noise) and label-smoothing"),
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_entry('wide_resnet50_2', 'Wide-ResNet-50', '1605.07146'),
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_entry('seresnet18', 'SE-ResNet-18', '1709.01507'),
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_entry('seresnet18', 'SE-ResNet-18', '1709.01507'),
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_entry('seresnet34', 'SE-ResNet-34', '1709.01507'),
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_entry('seresnet34', 'SE-ResNet-34', '1709.01507'),
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_entry('seresnet50', 'SE-ResNet-50', '1709.01507'),
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_entry('seresnext26_32x4d', 'SE-ResNeXt-26 32x4d', '1709.01507',
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_entry('seresnext26_32x4d', 'SE-ResNeXt-26 32x4d', '1709.01507',
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model_desc='Block cfg of SE-ResNeXt-34 w/ Bottleneck'),
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model_desc='Block cfg of SE-ResNeXt-34 w/ Bottleneck'),
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_entry('seresnext26d_32x4d', 'SE-ResNeXt-26-D 32x4d', '1812.01187',
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_entry('seresnext26d_32x4d', 'SE-ResNeXt-26-D 32x4d', '1812.01187',
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@ -121,6 +126,7 @@ model_list = [
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model_desc='Block cfg of SE-ResNeXt-34 w/ Bottleneck, deep tiered stem, and avg-pool in downsample layers.'),
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model_desc='Block cfg of SE-ResNeXt-34 w/ Bottleneck, deep tiered stem, and avg-pool in downsample layers.'),
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_entry('seresnext26tn_32x4d', 'SE-ResNeXt-26-TN 32x4d', '1812.01187',
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_entry('seresnext26tn_32x4d', 'SE-ResNeXt-26-TN 32x4d', '1812.01187',
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model_desc='Block cfg of SE-ResNeXt-34 w/ Bottleneck, deep tiered narrow stem, and avg-pool in downsample layers.'),
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model_desc='Block cfg of SE-ResNeXt-34 w/ Bottleneck, deep tiered narrow stem, and avg-pool in downsample layers.'),
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_entry('seresnext50_32x4d', 'SE-ResNeXt-50 32x4d', '1709.01507'),
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_entry('skresnet18', 'SK-ResNet-18', '1903.06586'),
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_entry('skresnet18', 'SK-ResNet-18', '1903.06586'),
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_entry('skresnet34', 'SK-ResNet-34', '1903.06586'),
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_entry('skresnet34', 'SK-ResNet-34', '1903.06586'),
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@ -139,6 +145,7 @@ model_list = [
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_entry('densenetblur121d', 'DenseNet-Blur-121D', '1904.11486',
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_entry('densenetblur121d', 'DenseNet-Blur-121D', '1904.11486',
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model_desc='DenseNet with blur pooling and deep stem'),
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model_desc='DenseNet with blur pooling and deep stem'),
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_entry('ese_vovnet19b_dw', 'VoVNet-19-DW-V2', '1911.06667'),
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_entry('ese_vovnet39b', 'VoVNet-39-V2', '1911.06667'),
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_entry('ese_vovnet39b', 'VoVNet-39-V2', '1911.06667'),
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_entry('tf_efficientnet_b0', 'EfficientNet-B0 (AutoAugment)', '1905.11946',
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_entry('tf_efficientnet_b0', 'EfficientNet-B0 (AutoAugment)', '1905.11946',
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@ -247,13 +254,13 @@ model_list = [
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_entry('inception_v4', 'Inception V4', '1602.07261'),
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_entry('inception_v4', 'Inception V4', '1602.07261'),
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_entry('nasnetalarge', 'NASNet-A Large', '1707.07012', batch_size=BATCH_SIZE // 4),
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_entry('nasnetalarge', 'NASNet-A Large', '1707.07012', batch_size=BATCH_SIZE // 4),
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_entry('pnasnet5large', 'PNASNet-5', '1712.00559', batch_size=BATCH_SIZE // 4),
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_entry('pnasnet5large', 'PNASNet-5', '1712.00559', batch_size=BATCH_SIZE // 4),
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_entry('seresnet50', 'SE-ResNet-50', '1709.01507'),
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_entry('seresnet101', 'SE-ResNet-101', '1709.01507'),
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_entry('seresnet152', 'SE-ResNet-152', '1709.01507'),
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_entry('seresnext50_32x4d', 'SE-ResNeXt-50 32x4d', '1709.01507'),
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_entry('seresnext101_32x4d', 'SE-ResNeXt-101 32x4d', '1709.01507'),
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_entry('senet154', 'SENet-154', '1709.01507'),
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_entry('xception', 'Xception', '1610.02357', batch_size=BATCH_SIZE//2),
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_entry('xception', 'Xception', '1610.02357', batch_size=BATCH_SIZE//2),
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_entry('legacy_seresnet50', 'SE-ResNet-50', '1709.01507'),
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_entry('legacy_seresnet101', 'SE-ResNet-101', '1709.01507'),
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_entry('legacy_seresnet152', 'SE-ResNet-152', '1709.01507'),
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_entry('legacy_seresnext50_32x4d', 'SE-ResNeXt-50 32x4d', '1709.01507'),
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_entry('legacy_seresnext101_32x4d', 'SE-ResNeXt-101 32x4d', '1709.01507'),
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_entry('legacy_senet154', 'SENet-154', '1709.01507'),
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## Torchvision weights
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## Torchvision weights
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# _entry('densenet121'),
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# _entry('densenet121'),
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@ -443,12 +450,6 @@ model_list = [
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]
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]
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# FIXME debug sotabench dataset issues
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from pprint import pprint
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from glob import glob
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pprint([glob('./**', recursive=True)])
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pprint([glob('./.data/vision/**', recursive=True)])
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for m in model_list:
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for m in model_list:
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model_name = m['model']
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model_name = m['model']
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# create model from name
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# create model from name
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