ImageNet has multiple versions, but the most commonly used one is ILSVRC 2012. The ResNet family models below are trained by standard data augmentations, i.e., RandomResizedCrop, RandomHorizontalFlip and Normalize.
Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
---|---|---|---|---|---|---|
VGG-11 | 132.86 | 7.63 | 69.03 | 88.63 | config | model* |
VGG-13 | 133.05 | 11.34 | 69.93 | 89.26 | config | model* |
VGG-16 | 138.36 | 15.5 | 71.59 | 90.39 | config | model* |
VGG-19 | 143.67 | 19.67 | 72.38 | 90.88 | config | model* |
VGG-11-BN | 132.87 | 7.64 | 70.37 | 89.81 | config | model* |
VGG-13-BN | 133.05 | 11.36 | 71.55 | 90.37 | config | model* |
VGG-16-BN | 138.37 | 15.53 | 73.36 | 91.5 | config | model* |
VGG-19-BN | 143.68 | 19.7 | 74.24 | 91.84 | config | model* |
ResNet-18 | 11.69 | 1.82 | 70.07 | 89.44 | config | model | log |
ResNet-34 | 21.8 | 3.68 | 73.85 | 91.53 | config | model | log |
ResNet-50 | 25.56 | 4.12 | 76.55 | 93.15 | config | model | log |
ResNet-101 | 44.55 | 7.85 | 78.18 | 94.03 | config | model | log |
ResNeSt-50 | 27.48 | 5.41 | 81.13 | 95.59 | model | log | |
ResNeSt-101 | 48.28 | 10.27 | 82.32 | 96.24 | model | log | |
ResNeSt-200 | 70.2 | 17.53 | 82.41 | 96.22 | model | log | |
ResNeSt-269 | 110.93 | 22.58 | 82.70 | 96.28 | model | log | |
ResNet-152 | 60.19 | 11.58 | 78.63 | 94.16 | config | model | log |
ResNetV1D-50 | 25.58 | 4.36 | 77.4 | 93.66 | config | model | log |
ResNetV1D-101 | 44.57 | 8.09 | 78.85 | 94.38 | config | model | log |
ResNetV1D-152 | 60.21 | 11.82 | 79.35 | 94.61 | config | model | log |
ResNeXt-32x4d-50 | 25.03 | 4.27 | 77.92 | 93.74 | config | model | log |
ResNeXt-32x4d-101 | 44.18 | 8.03 | 78.7 | 94.34 | config | model | log |
ResNeXt-32x8d-101 | 88.79 | 16.5 | 79.22 | 94.52 | config | model | log |
ResNeXt-32x4d-152 | 59.95 | 11.8 | 79.06 | 94.47 | config | model | log |
SE-ResNet-50 | 28.09 | 4.13 | 77.74 | 93.84 | config | model | log |
SE-ResNet-101 | 49.33 | 7.86 | 78.26 | 94.07 | config | model | log |
ShuffleNetV1 1.0x (group=3) | 1.87 | 0.146 | 68.13 | 87.81 | config | model | log |
ShuffleNetV2 1.0x | 2.28 | 0.149 | 69.55 | 88.92 | config | model | log |
MobileNet V2 | 3.5 | 0.319 | 71.86 | 90.42 | config | model | log |
Models with * are converted from other repos, others are trained by ourselves.
Model | Params(M) | Flops(G) | Top-1 (%) | Config | Download |
---|---|---|---|---|---|
ResNet-18-b16x8 | 11.17 | 0.56 | 94.72 | config | model | log |
ResNet-34-b16x8 | 21.28 | 1.16 | 95.34 | config | model | log |
ResNet-50-b16x8 | 23.52 | 1.31 | 95.36 | config | model | log |
ResNet-101-b16x8 | 42.51 | 2.52 | 95.66 | config | model | log |
ResNet-152-b16x8 | 58.16 | 3.74 | 95.96 | config | model | log |