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mmclassification/docs/model_zoo.md
Zhiliang Peng 18f6bb0b10
[Feature] Implement the conformer backbone. (#494) 
* implement the conformer

* format code style

* format code style

* reuse the TransformerEncoderLayer in the vision_transformer.py

* Modify variable name

* delete unused params

* Remove warning info in Conformer head since it already exists in
Conformer.

* Rename some variables

* Add unit tests

* Use `getattr` instead of `get_submodule`.

* Remove some useless layers

* Refactor conformer and add configs

* Update configs and add metafile.

* Fix unit tests

* Update README

Co-authored-by: mzr1996 <mzr1996@163.com>
2021-12-07 14:00:17 +08:00

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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 config model | log
ResNeSt-101* 48.28 10.27 82.32 96.24 config model | log
ResNeSt-200* 70.2 17.53 82.41 96.22 config model | log
ResNeSt-269* 110.93 22.58 82.70 96.28 config 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
Conformer-tiny-p16* 23.52 4.90 81.31 95.60 config model | log
Conformer-small-p32 38.85 7.09 81.96 96.02 config model | log
Conformer-small-p16* 37.67 10.31 83.32 96.46 config model | log
Conformer-base-p16* 83.29 22.89 83.82 96.59 config model | log

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