add README.md in configs
lixinran
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# Backpropagation Applied to Handwritten Zip Code Recognition
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## Introduction
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```latex
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@ARTICLE{6795724,
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author={Y. {LeCun} and B. {Boser} and J. S. {Denker} and D. {Henderson} and R. E. {Howard} and W. {Hubbard} and L. D. {Jackel}},
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journal={Neural Computation},
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title={Backpropagation Applied to Handwritten Zip Code Recognition},
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year={1989},
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volume={1},
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number={4},
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pages={541-551},
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doi={10.1162/neco.1989.1.4.541}}
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}
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```
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# MobileNetV2: Inverted Residuals and Linear Bottlenecks
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## Introduction
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```latex
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@INPROCEEDINGS{8578572,
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author={M. {Sandler} and A. {Howard} and M. {Zhu} and A. {Zhmoginov} and L. {Chen}},
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booktitle={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
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title={MobileNetV2: Inverted Residuals and Linear Bottlenecks},
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year={2018},
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volume={},
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number={},
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pages={4510-4520},
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doi={10.1109/CVPR.2018.00474}}
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}
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```
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## Results and models
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### ImageNet
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| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
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|:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:|:--------:|
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| MobileNet V2 | 3.5 | 0.319 | 71.86 | 90.42 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/imagenet/mobilenet_v2_b32x8.py) | [model](https://download.openmmlab.com/mmclassification/v0/imagenet/mobilenet_v2_batch256_20200708-3b2dc3af.pth) | [log](https://download.openmmlab.com/mmclassification/v0/imagenet/mobilenet_v2_batch256_20200708-3b2dc3af.log.json) |
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# Deep Residual Learning for Image Recognition
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## Introduction
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```latex
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@inproceedings{he2016deep,
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title={Deep residual learning for image recognition},
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author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
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booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
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pages={770--778},
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year={2016}
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}
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```
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## Results and models
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## Cifar10
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| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
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|:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:|:--------:|
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| ResNet-18-b16x8 | 11.17 | 0.56 | 94.72 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cifar10/resnet18_b16x8.py) | [model](https://download.openmmlab.com/mmclassification/v0/cifar10/resnet18_b16x8_20200823-f906fa4e.pth) | [log](https://download.openmmlab.com/mmclassification/v0/cifar10/resnet18_b16x8_20200823-f906fa4e.log.json) |
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| ResNet-34-b16x8 | 21.28 | 1.16 | 95.34 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cifar10/resnet34_b16x8.py) | [model](https://download.openmmlab.com/mmclassification/v0/cifar10/resnet34_b16x8_20200823-52d5d832.pth) | [log](https://download.openmmlab.com/mmclassification/v0/cifar10/resnet34_b16x8_20200823-52d5d832.log.json) |
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| ResNet-50-b16x8 | 23.52 | 1.31 | 95.36 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cifar10/resnet50_b16x8.py) | [model](https://download.openmmlab.com/mmclassification/v0/cifar10/resnet50_b16x8_20200823-882aa7b1.pth) | [log](https://download.openmmlab.com/mmclassification/v0/cifar10/resnet50_b16x8_20200823-882aa7b1.log.json) |
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| ResNet-101-b16x8 | 42.51 | 2.52 | 95.66 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cifar10/resnet101_b16x8.py) | [model](https://download.openmmlab.com/mmclassification/v0/cifar10/resnet101_b16x8_20200823-d9501bbc.pth) | [log](https://download.openmmlab.com/mmclassification/v0/cifar10/resnet101_b16x8_20200823-d9501bbc.log.json) |
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| ResNet-152-b16x8 | 58.16 | 3.74 | 95.96 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cifar10/resnet152_b16x8.py) | [model](https://download.openmmlab.com/mmclassification/v0/cifar10/resnet152_b16x8_20200823-ad4d5d0c.pth) | [log](https://download.openmmlab.com/mmclassification/v0/cifar10/resnet152_b16x8_20200823-ad4d5d0c.log.json) |
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### ImageNet
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| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
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|:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:|:--------:|
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| ResNet-18 | 11.69 | 1.82 | 70.07 | 89.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/imagenet/resnet18_b32x8.py) | [model](https://download.openmmlab.com/mmclassification/v0/imagenet/resnet18_batch256_20200708-34ab8f90.pth) | [log](https://download.openmmlab.com/mmclassification/v0/imagenet/resnet18_batch256_20200708-34ab8f90.log.json) |
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| ResNet-34 | 21.8 | 3.68 | 73.85 | 91.53 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/imagenet/resnet34_b32x8.py) | [model](https://download.openmmlab.com/mmclassification/v0/imagenet/resnet34_batch256_20200708-32ffb4f7.pth) | [log](https://download.openmmlab.com/mmclassification/v0/imagenet/resnet34_batch256_20200708-32ffb4f7.log.json) |
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| ResNet-50 | 25.56 | 4.12 | 76.55 | 93.15 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/imagenet/resnet50_b32x8.py) | [model](https://download.openmmlab.com/mmclassification/v0/imagenet/resnet50_batch256_20200708-cfb998bf.pth) | [log](https://download.openmmlab.com/mmclassification/v0/imagenet/resnet50_batch256_20200708-cfb998bf.log.json) |
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| ResNet-101 | 44.55 | 7.85 | 78.18 | 94.03 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/imagenet/resnet101_b32x8.py) | [model](https://download.openmmlab.com/mmclassification/v0/imagenet/resnet101_batch256_20200708-753f3608.pth) | [log](https://download.openmmlab.com/mmclassification/v0/imagenet/resnet101_batch256_20200708-753f3608.log.json) |
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| ResNet-152 | 60.19 | 11.58 | 78.63 | 94.16 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/imagenet/resnet152_b32x8.py) | [model](https://download.openmmlab.com/mmclassification/v0/imagenet/resnet152_batch256_20200708-ec25b1f9.pth) | [log](https://download.openmmlab.com/mmclassification/v0/imagenet/resnet152_batch256_20200708-ec25b1f9.log.json) |
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| ResNetV1D-50 | 25.58 | 4.36 | 77.4 | 93.66 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/imagenet/resnetv1d50_b32x8.py) | [model](https://download.openmmlab.com/mmclassification/v0/imagenet/resnetv1d50_batch256_20200708-1ad0ce94.pth) | [log](https://download.openmmlab.com/mmclassification/v0/imagenet/resnetv1d50_batch256_20200708-1ad0ce94.log.json) |
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| ResNetV1D-101 | 44.57 | 8.09 | 78.85 | 94.38 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/imagenet/resnetv1d101_b32x8.py) | [model](https://download.openmmlab.com/mmclassification/v0/imagenet/resnetv1d101_batch256_20200708-9cb302ef.pth) | [log](https://download.openmmlab.com/mmclassification/v0/imagenet/resnetv1d101_batch256_20200708-9cb302ef.log.json) |
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| ResNetV1D-152 | 60.21 | 11.82 | 79.35 | 94.61 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/imagenet/resnetv1d152_b32x8.py) | [model](https://download.openmmlab.com/mmclassification/v0/imagenet/resnetv1d152_batch256_20200708-e79cb6a2.pth) | [log](https://download.openmmlab.com/mmclassification/v0/imagenet/resnetv1d152_batch256_20200708-e79cb6a2.log.json) |
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# Aggregated Residual Transformations for Deep Neural Networks
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## Introduction
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```latex
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@inproceedings{xie2017aggregated,
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title={Aggregated residual transformations for deep neural networks},
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author={Xie, Saining and Girshick, Ross and Doll{\'a}r, Piotr and Tu, Zhuowen and He, Kaiming},
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booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
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pages={1492--1500},
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year={2017}
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}
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```
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## Results and models
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### ImageNet
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| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
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|:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:|:--------:|
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| ResNeXt-32x4d-50 | 25.03 | 4.27 | 77.92 | 93.74 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/imagenet/resnext50_32x4d_b32x8.py) | [model](https://download.openmmlab.com/mmclassification/v0/imagenet/resnext50_32x4d_batch256_20200708-c07adbb7.pth) | [log](https://download.openmmlab.com/mmclassification/v0/imagenet/resnext50_32x4d_batch256_20200708-c07adbb7.log.json) |
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| ResNeXt-32x4d-101 | 44.18 | 8.03 | 78.7 | 94.34 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/imagenet/resnext101_32x4d_b32x8.py) | [model](https://download.openmmlab.com/mmclassification/v0/imagenet/resnext101_32x4d_batch256_20200708-87f2d1c9.pth) | [log](https://download.openmmlab.com/mmclassification/v0/imagenet/resnext101_32x4d_batch256_20200708-87f2d1c9.log.json) |
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| ResNeXt-32x8d-101 | 88.79 | 16.5 | 79.22 | 94.52 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/imagenet/resnext101_32x8d_b32x8.py) | [model](https://download.openmmlab.com/mmclassification/v0/imagenet/resnext101_32x8d_batch256_20200708-1ec34aa7.pth) | [log](https://download.openmmlab.com/mmclassification/v0/imagenet/resnext101_32x8d_batch256_20200708-1ec34aa7.log.json) |
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| ResNeXt-32x4d-152 | 59.95 | 11.8 | 79.06 | 94.47 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/imagenet/resnext152_32x4d_b32x8.py) | [model](https://download.openmmlab.com/mmclassification/v0/imagenet/resnext152_32x4d_batch256_20200708-aab5034c.pth) | [log](https://download.openmmlab.com/mmclassification/v0/imagenet/resnext152_32x4d_batch256_20200708-aab5034c.log.json) |
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# Squeeze-and-Excitation Networks
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## Introduction
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```latex
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@inproceedings{hu2018squeeze,
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title={Squeeze-and-excitation networks},
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author={Hu, Jie and Shen, Li and Sun, Gang},
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booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
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pages={7132--7141},
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year={2018}
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}
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```
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## Results and models
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### ImageNet
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| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
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|:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:|:--------:|
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| SE-ResNet-50 | 28.09 | 4.13 | 77.74 | 93.84 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/imagenet/seresnet50_b32x8.py) | [model](https://download.openmmlab.com/mmclassification/v0/imagenet/se-resnet50_batch256_20200804-ae206104.pth) | [log](https://download.openmmlab.com/mmclassification/v0/imagenet/se-resnet50_batch256_20200708-657b3c36.log.json) |
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| SE-ResNet-101 | 49.33 | 7.86 | 78.26 | 94.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/imagenet/seresnet101_b32x8.py) | [model](https://download.openmmlab.com/mmclassification/v0/imagenet/se-resnet101_batch256_20200804-ba5b51d4.pth) | [log](https://download.openmmlab.com/mmclassification/v0/imagenet/se-resnet101_batch256_20200708-038a4d04.log.json) |
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# Squeeze-and-Excitation Networks
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## Introduction
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```latex
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@inproceedings{hu2018squeeze,
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title={Squeeze-and-excitation networks},
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author={Hu, Jie and Shen, Li and Sun, Gang},
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booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
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pages={7132--7141},
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year={2018}
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}
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```
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# ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
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## Introduction
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```latex
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@inproceedings{zhang2018shufflenet,
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title={Shufflenet: An extremely efficient convolutional neural network for mobile devices},
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author={Zhang, Xiangyu and Zhou, Xinyu and Lin, Mengxiao and Sun, Jian},
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booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
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pages={6848--6856},
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year={2018}
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}
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```
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## Results and models
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### ImageNet
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| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
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|:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:|:--------:|
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| ShuffleNetV1 1.0x (group=3) | 1.87 | 0.146 | 68.13 | 87.81 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/imagenet/shufflenet_v1_1x_b64x16_linearlr_bn_nowd.py) | [model](https://download.openmmlab.com/mmclassification/v0/imagenet/shufflenet_v1_batch1024_20200804-5d6cec73.pth) | [log](https://download.openmmlab.com/mmclassification/v0/imagenet/shufflenet_v1_batch1024_20200804-5d6cec73.log.json) |
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# Shufflenet v2: Practical guidelines for efficient cnn architecture design
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## Introduction
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```latex
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@inproceedings{ma2018shufflenet,
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title={Shufflenet v2: Practical guidelines for efficient cnn architecture design},
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author={Ma, Ningning and Zhang, Xiangyu and Zheng, Hai-Tao and Sun, Jian},
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booktitle={Proceedings of the European conference on computer vision (ECCV)},
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pages={116--131},
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year={2018}
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}
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```
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## Results and models
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### ImageNet
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| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
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|:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:|:--------:|
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| ShuffleNetV2 1.0x | 2.28 | 0.149 | 69.55 | 88.92 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/imagenet/shufflenet_v2_1x_b64x16_linearlr_bn_nowd.py) | [model](https://download.openmmlab.com/mmclassification/v0/imagenet/shufflenet_v2_batch1024_20200812-5bf4721e.pth) | [log](https://download.openmmlab.com/mmclassification/v0/imagenet/shufflenet_v2_batch1024_20200804-8860eec9.log.json) |
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# Very Deep Convolutional Networks for Large-Scale Image Recognition
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## Introduction
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```latex
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@article{simonyan2014very,
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title={Very deep convolutional networks for large-scale image recognition},
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author={Simonyan, Karen and Zisserman, Andrew},
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journal={arXiv preprint arXiv:1409.1556},
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year={2014}
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}
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```
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## Results and models
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### ImageNet
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| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
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|:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:|:--------:|
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| VGG-11 | 132.86 | 7.63 | 69.03 | 88.63 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/imagenet/vgg11.py) | [model](https://download.openmmlab.com/mmclassification/v0/imagenet/vgg11-01ecd97e.pth)* |
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| VGG-13 | 133.05 | 11.34 | 69.93 | 89.26 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/imagenet/vgg13.py) | [model](https://download.openmmlab.com/mmclassification/v0/imagenet/vgg13-9ad3945d.pth)*|
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| VGG-16 | 138.36 | 15.5 | 71.59 | 90.39 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/imagenet/vgg16.py) | [model](https://download.openmmlab.com/mmclassification/v0/imagenet/vgg16-91b6d117.pth)*|
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| VGG-19 | 143.67 | 19.67 | 72.38 | 90.88 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/imagenet/vgg19.py) | [model](https://download.openmmlab.com/mmclassification/v0/imagenet/vgg19-fee352a8.pth)*|
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| VGG-11-BN | 132.87 | 7.64 | 70.37 | 89.81 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/imagenet/vgg11bn.py) | [model](https://download.openmmlab.com/mmclassification/v0/imagenet/vgg11_bn-6fbbbf3f.pth)*|
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| VGG-13-BN | 133.05 | 11.36 | 71.55 | 90.37 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/imagenet/vgg13bn.py) | [model](https://download.openmmlab.com/mmclassification/v0/imagenet/vgg13_bn-4b5f9390.pth)*|
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| VGG-16-BN | 138.37 | 15.53 | 73.36 | 91.5 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/imagenet/vgg19.py) | [model](https://download.openmmlab.com/mmclassification/v0/imagenet/vgg16_bn-3ac6d8fd.pth)*|
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| VGG-19-BN | 143.68 | 19.7 | 74.24 | 91.84 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/imagenet/vgg19bn.py) | [model](https://download.openmmlab.com/mmclassification/v0/imagenet/vgg19_bn-7c058385.pth)*|
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