Model Zoo¶
ImageNet¶
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 | 68.75 | 88.87 | config | model | log |
| VGG-13 | 133.05 | 11.34 | 70.02 | 89.46 | config | model | log |
| VGG-16 | 138.36 | 15.5 | 71.62 | 90.49 | config | model | log |
| VGG-19 | 143.67 | 19.67 | 72.41 | 90.80 | config | model | log |
| VGG-11-BN | 132.87 | 7.64 | 70.75 | 90.12 | config | model | log |
| VGG-13-BN | 133.05 | 11.36 | 72.15 | 90.71 | config | model | log |
| VGG-16-BN | 138.37 | 15.53 | 73.72 | 91.68 | config | model | log |
| VGG-19-BN | 143.68 | 19.7 | 74.70 | 92.24 | config | model | log |
| RepVGG-A0* | 9.11（train) | 8.31 (deploy) | 1.52 (train) | 1.36 (deploy) | 72.41 | 90.50 | config (train) | config (deploy) | model | log |
| RepVGG-A1* | 14.09 (train) | 12.79 (deploy) | 2.64 (train) | 2.37 (deploy) | 74.47 | 91.85 | config (train) | config (deploy) | model | log |
| RepVGG-A2* | 28.21 (train) | 25.5 (deploy) | 5.7 (train) | 5.12 (deploy) | 76.48 | 93.01 | config (train) | config (deploy) | model | log |
| RepVGG-B0* | 15.82 (train) | 14.34 (deploy) | 3.42 (train) | 3.06 (deploy) | 75.14 | 92.42 | config (train) | config (deploy) | model | log |
| RepVGG-B1* | 57.42 (train) | 51.83 (deploy) | 13.16 (train) | 11.82 (deploy) | 78.37 | 94.11 | config (train) | config (deploy) | model | log |
| RepVGG-B1g2* | 45.78 (train) | 41.36 (deploy) | 9.82 (train) | 8.82 (deploy) | 77.79 | 93.88 | config (train) | config (deploy) | model | log |
| RepVGG-B1g4* | 39.97 (train) | 36.13 (deploy) | 8.15 (train) | 7.32 (deploy) | 77.58 | 93.84 | config (train) | config (deploy) | model | log |
| RepVGG-B2* | 89.02 (train) | 80.32 (deploy) | 20.46 (train) | 18.39 (deploy) | 78.78 | 94.42 | config (train) | config (deploy) | model | log |
| RepVGG-B2g4* | 61.76 (train) | 55.78 (deploy) | 12.63 (train) | 11.34 (deploy) | 79.38 | 94.68 | config (train) | config (deploy) | model | log |
| RepVGG-B3* | 123.09 (train) | 110.96 (deploy) | 29.17 (train) | 26.22 (deploy) | 80.52 | 95.26 | config (train) | config (deploy) | model | log |
| RepVGG-B3g4* | 83.83 (train) | 75.63 (deploy) | 17.9 (train) | 16.08 (deploy) | 80.22 | 95.10 | config (train) | config (deploy) | model | log |
| RepVGG-D2se* | 133.33 (train) | 120.39 (deploy) | 36.56 (train) | 32.85 (deploy) | 81.81 | 95.94 | config (train) | config (deploy) | model | log |
| 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 |
| ResNet-152 | 60.19 | 11.58 | 78.63 | 94.16 | config | model | log |
| Res2Net-50-14w-8s* | 25.06 | 4.22 | 78.14 | 93.85 | config | model | log |
| Res2Net-50-26w-8s* | 48.40 | 8.39 | 79.20 | 94.36 | config | model | log |
| Res2Net-101-26w-4s* | 45.21 | 8.12 | 79.19 | 94.44 | 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 | |
| ResNetV1D-50 | 25.58 | 4.36 | 77.54 | 93.57 | config | model | log |
| ResNetV1D-101 | 44.57 | 8.09 | 78.93 | 94.48 | config | model | log |
| ResNetV1D-152 | 60.21 | 11.82 | 79.41 | 94.7 | config | model | log |
| ResNeXt-32x4d-50 | 25.03 | 4.27 | 77.90 | 93.66 | config | model | log |
| ResNeXt-32x4d-101 | 44.18 | 8.03 | 78.71 | 94.12 | config | model | log |
| ResNeXt-32x8d-101 | 88.79 | 16.5 | 79.23 | 94.58 | config | model | log |
| ResNeXt-32x4d-152 | 59.95 | 11.8 | 78.93 | 94.41 | 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 |
| ViT-B/16* | 86.86 | 33.03 | 85.43 | 97.77 | config | model | log |
| ViT-B/32* | 88.3 | 8.56 | 84.01 | 97.08 | config | model | log |
| ViT-L/16* | 304.72 | 116.68 | 85.63 | 97.63 | config | model | log |
| Swin-Transformer tiny | 28.29 | 4.36 | 81.18 | 95.61 | config | model | log |
| Swin-Transformer small | 49.61 | 8.52 | 83.02 | 96.29 | config | model | log |
| Swin-Transformer base | 87.77 | 15.14 | 83.36 | 96.44 | config | model | log |
| Transformer in Transformer small* | 23.76 | 3.36 | 81.52 | 95.73 | config | model | log |
| T2T-ViT_t-14* | 21.47 | 4.34 | 81.69 | 95.85 | config | model | log |
| T2T-ViT_t-19* | 39.08 | 7.80 | 82.43 | 96.08 | config | model | log |
| T2T-ViT_t-24* | 64.00 | 12.69 | 82.55 | 96.06 | config | model | log |
| Mixer-B/16* | 59.88 | 12.61 | 76.68 | 92.25 | config | model | log |
| Mixer-L/16* | 208.2 | 44.57 | 72.34 | 88.02 | config | model | log |
Models with * are converted from other repos, others are trained by ourselves.