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d56170a734
* add config for resnest test * fix config * add label smoothing * add memcached * minor fix * fix bug * fix config * add config * minor fix * fix configs * use EResize * change interpolation * add more configs * add docsting * add unittest * remove unnecessary changes * minor fix * add more docstring * fix linting * add efficient backbone * add config * add Edge Residual * fix bug * remove unnecessary files * refactor * add resize in crop to ensure crop size is output size * fix bug and add comments * test * fix * add more configs * add more configs * add more configs * fix bug * add model zoo * fix * reorganize code * add edge tpu * add edge tpu converter * rename * update readme * reorganize code and config * Rename configs of EfficientNet, and add metafile & model_zoo * Remove `backend='pillow'` * Add comments about EfficientNet-EdgeTPU * Rename the convert tool of EfficientNet. * Refactor EfficientNet and update docstring. * Update EfficientNet-EdgeTPU config * Fix unit tests Co-authored-by: lixinran <lixr423@outlook.com> Co-authored-by: lixinran <lixinran@sensetime.com> Co-authored-by: mzr1996 <mzr1996@163.com>
40 KiB
40 KiB
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 |
| RepVGG-A1* | 14.09 (train) | 12.79 (deploy) | 2.64 (train) | 2.37 (deploy) | 74.47 | 91.85 | config (train) | config (deploy) | model |
| RepVGG-A2* | 28.21 (train) | 25.5 (deploy) | 5.7 (train) | 5.12 (deploy) | 76.48 | 93.01 | config (train) | config (deploy) | model |
| RepVGG-B0* | 15.82 (train) | 14.34 (deploy) | 3.42 (train) | 3.06 (deploy) | 75.14 | 92.42 | config (train) | config (deploy) | model |
| RepVGG-B1* | 57.42 (train) | 51.83 (deploy) | 13.16 (train) | 11.82 (deploy) | 78.37 | 94.11 | config (train) | config (deploy) | model |
| RepVGG-B1g2* | 45.78 (train) | 41.36 (deploy) | 9.82 (train) | 8.82 (deploy) | 77.79 | 93.88 | config (train) | config (deploy) | model |
| RepVGG-B1g4* | 39.97 (train) | 36.13 (deploy) | 8.15 (train) | 7.32 (deploy) | 77.58 | 93.84 | config (train) | config (deploy) | model |
| RepVGG-B2* | 89.02 (train) | 80.32 (deploy) | 20.46 (train) | 18.39 (deploy) | 78.78 | 94.42 | config (train) | config (deploy) | model |
| RepVGG-B2g4* | 61.76 (train) | 55.78 (deploy) | 12.63 (train) | 11.34 (deploy) | 79.38 | 94.68 | config (train) | config (deploy) | model |
| RepVGG-B3* | 123.09 (train) | 110.96 (deploy) | 29.17 (train) | 26.22 (deploy) | 80.52 | 95.26 | config (train) | config (deploy) | model |
| RepVGG-B3g4* | 83.83 (train) | 75.63 (deploy) | 17.9 (train) | 16.08 (deploy) | 80.22 | 95.10 | config (train) | config (deploy) | model |
| RepVGG-D2se* | 133.33 (train) | 120.39 (deploy) | 36.56 (train) | 32.85 (deploy) | 81.81 | 95.94 | config (train) | config (deploy) | 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 (rsb-a1) | 25.56 | 4.12 | 80.12 | 94.78 | 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 |
| Res2Net-50-26w-8s* | 48.40 | 8.39 | 79.20 | 94.36 | config | model |
| Res2Net-101-26w-4s* | 45.21 | 8.12 | 79.19 | 94.44 | config | model |
| ResNeSt-50* | 27.48 | 5.41 | 81.13 | 95.59 | config | model |
| ResNeSt-101* | 48.28 | 10.27 | 82.32 | 96.24 | config | model |
| ResNeSt-200* | 70.2 | 17.53 | 82.41 | 96.22 | config | model |
| ResNeSt-269* | 110.93 | 22.58 | 82.70 | 96.28 | config | model |
| 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 |
| RegNetX-400MF | 5.16 | 0.41 | 72.56 | 90.78 | config | model | log |
| RegNetX-800MF | 7.26 | 0.81 | 74.76 | 92.32 | config | model | log |
| RegNetX-1.6GF | 9.19 | 1.63 | 76.84 | 93.31 | config | model | log |
| RegNetX-3.2GF | 15.3 | 3.21 | 78.09 | 94.08 | config | model | log |
| RegNetX-4.0GF | 22.12 | 4.0 | 78.60 | 94.17 | config | model | log |
| RegNetX-6.4GF | 26.21 | 6.51 | 79.38 | 94.65 | config | model | log |
| RegNetX-8.0GF | 39.57 | 8.03 | 79.12 | 94.51 | config | model | log |
| RegNetX-12GF | 46.11 | 12.15 | 79.67 | 95.03 | 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 |
| ViT-B/32* | 88.3 | 8.56 | 84.01 | 97.08 | config | model |
| ViT-L/16* | 304.72 | 116.68 | 85.63 | 97.63 | config | model |
| 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 |
| T2T-ViT_t-14 | 21.47 | 4.34 | 81.83 | 95.84 | config | model | log |
| T2T-ViT_t-19 | 39.08 | 7.80 | 82.63 | 96.18 | config | model | log |
| T2T-ViT_t-24 | 64.00 | 12.69 | 82.71 | 96.09 | config | model | log |
| Mixer-B/16* | 59.88 | 12.61 | 76.68 | 92.25 | config | model |
| Mixer-L/16* | 208.2 | 44.57 | 72.34 | 88.02 | config | model |
| DeiT-tiny* | 5.72 | 1.08 | 72.13 | 91.13 | config | model |
| DeiT-tiny distilled* | 5.72 | 1.08 | 74.51 | 91.90 | config | model |
| DeiT-small* | 22.05 | 4.24 | 79.83 | 94.95 | config | model |
| DeiT-small distilled* | 22.05 | 4.24 | 81.17 | 95.40 | config | model |
| DeiT-base* | 86.57 | 16.86 | 81.79 | 95.59 | config | model |
| DeiT-base distilled* | 86.57 | 16.86 | 83.33 | 96.49 | config | model |
| DeiT-base 384px* | 86.86 | 49.37 | 83.04 | 96.31 | config | model |
| DeiT-base distilled 384px* | 86.86 | 49.37 | 85.55 | 97.35 | config | model |
| Conformer-tiny-p16* | 23.52 | 4.90 | 81.31 | 95.60 | config | model |
| Conformer-small-p32* | 38.85 | 7.09 | 81.96 | 96.02 | config | model |
| Conformer-small-p16* | 37.67 | 10.31 | 83.32 | 96.46 | config | model |
| Conformer-base-p16* | 83.29 | 22.89 | 83.82 | 96.59 | config | model |
| EfficientNet-B0* | 5.29 | 0.02 | 76.74 | 93.17 | config | model |
| EfficientNet-B0 (AA)* | 5.29 | 0.02 | 77.26 | 93.41 | config | model |
| EfficientNet-B0 (AA + AdvProp)* | 5.29 | 0.02 | 77.53 | 93.61 | config | model |
| EfficientNet-B1* | 7.79 | 0.03 | 78.68 | 94.28 | config | model |
| EfficientNet-B1 (AA)* | 7.79 | 0.03 | 79.20 | 94.42 | config | model |
| EfficientNet-B1 (AA + AdvProp)* | 7.79 | 0.03 | 79.52 | 94.43 | config | model |
| EfficientNet-B2* | 9.11 | 0.03 | 79.64 | 94.80 | config | model |
| EfficientNet-B2 (AA)* | 9.11 | 0.03 | 80.21 | 94.96 | config | model |
| EfficientNet-B2 (AA + AdvProp)* | 9.11 | 0.03 | 80.45 | 95.07 | config | model |
| EfficientNet-B3* | 12.23 | 0.06 | 81.01 | 95.34 | config | model |
| EfficientNet-B3 (AA)* | 12.23 | 0.06 | 81.58 | 95.67 | config | model |
| EfficientNet-B3 (AA + AdvProp)* | 12.23 | 0.06 | 81.81 | 95.69 | config | model |
| EfficientNet-B4* | 19.34 | 0.12 | 82.57 | 96.09 | config | model |
| EfficientNet-B4 (AA)* | 19.34 | 0.12 | 82.95 | 96.26 | config | model |
| EfficientNet-B4 (AA + AdvProp)* | 19.34 | 0.12 | 83.25 | 96.44 | config | model |
| EfficientNet-B5* | 30.39 | 0.24 | 83.18 | 96.47 | config | model |
| EfficientNet-B5 (AA)* | 30.39 | 0.24 | 83.82 | 96.76 | config | model |
| EfficientNet-B5 (AA + AdvProp)* | 30.39 | 0.24 | 84.21 | 96.98 | config | model |
| EfficientNet-B6 (AA)* | 43.04 | 0.41 | 84.05 | 96.82 | config | model |
| EfficientNet-B6 (AA + AdvProp)* | 43.04 | 0.41 | 84.74 | 97.14 | config | model |
| EfficientNet-B7 (AA)* | 66.35 | 0.72 | 84.38 | 96.88 | config | model |
| EfficientNet-B7 (AA + AdvProp)* | 66.35 | 0.72 | 85.14 | 97.23 | config | model |
| EfficientNet-B8 (AA + AdvProp)* | 87.41 | 1.09 | 85.38 | 97.28 | config | model |
Models with * are converted from other repos, others are trained by ourselves.
CIFAR10
| Model | Params(M) | Flops(G) | Top-1 (%) | Config | Download |
|---|---|---|---|---|---|
| ResNet-18-b16x8 | 11.17 | 0.56 | 94.82 | config | |
| ResNet-34-b16x8 | 21.28 | 1.16 | 95.34 | config | |
| ResNet-50-b16x8 | 23.52 | 1.31 | 95.55 | config | |
| ResNet-101-b16x8 | 42.51 | 2.52 | 95.58 | config | |
| ResNet-152-b16x8 | 58.16 | 3.74 | 95.76 | config |