【深度学习】CV 图像分类常见的 36 个模型

文章：新机器视觉

今天给大家介绍自 2014 年以来，计算机视觉 CV 领域图像分类方向文献和代码的超全总结和列表！总共涉及 36 种 ConvNet 模型。该 GitHub 项目作者是 weiaicunzai，项目地址是：

https://github.com/weiaicunzai/awesome-image-classification

背景

我相信图像识别是深入到其它机器视觉领域一个很好的起点，特别是对于刚刚入门深度学习的人来说。当我初学 CV 时，犯了很多错。我当时非常希望有人能告诉我应该从哪一篇论文开始读起。到目前为止，似乎还没有一个像 deep-learning-object-detection 这样的 GitHub 项目。因此，我决定建立一个 GitHub 项目，列出深入学习中关于图像分类的论文和代码，以帮助其他人。

对于学习路线，我的个人建议是，对于那些刚入门深度学习的人，可以试着从 vgg 开始，然后是 googlenet、resnet，之后可以自由地继续阅读列出的其它论文或切换到其它领域。

性能表

基于简化的目的，我只从论文中列举出在 ImageNet 上准确率最高的 top1 和 top5。注意，这并不一定意味着准确率越高，一个网络就比另一个网络更好。因为有些网络专注于降低模型复杂性而不是提高准确性，或者有些论文只给出 ImageNet 上的 single crop results，而另一些则给出模型融合或 multicrop results。

关于性能表的标注：

ConvNet：卷积神经网络的名称
ImageNet top1 acc：论文中基于 ImageNet 数据集最好的 top1 准确率
ImageNet top5 acc：论文中基于 ImageNet 数据集最好的 top5 准确率
Published In：论文发表在哪个会议或期刊

论文&代码

1. VGG

Very Deep Convolutional Networks for Large-Scale Image Recognition.

Karen Simonyan, Andrew Zisserman

pdf: https://arxiv.org/abs/1409.1556

code: torchvision :

https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py

code: keras-applications :

https://github.com/keras-team/keras-applications/blob/master/keras_applications/vgg16.py

code: keras-applications :

https://github.com/keras-team/keras-applications/blob/master/keras_applications/vgg19.py

2. GoogleNet

Going Deeper with Convolutions

Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich

pdf: https://arxiv.org/abs/1409.4842

code: unofficial-tensorflow :

https://github.com/conan7882/GoogLeNet-Inception

code: unofficial-caffe :

https://github.com/lim0606/caffe-googlenet-bn

3. PReLU-nets

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun

pdf: https://arxiv.org/abs/1502.01852

code: unofficial-chainer :

https://github.com/nutszebra/prelu_net

4. ResNet

Deep Residual Learning for Image Recognition

Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun

pdf: https://arxiv.org/abs/1512.03385

code: facebook-torch :

https://github.com/facebook/fb.resnet.torch

code: torchvision :

https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py

code: keras-applications :

https://github.com/keras-team/keras-applications/blob/master/keras_applications/resnet.py

code: unofficial-keras :

https://github.com/raghakot/keras-resnet

code: unofficial-tensorflow :

https://github.com/ry/tensorflow-resnet

5. PreActResNet

Identity Mappings in Deep Residual Networks

Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun

pdf: https://arxiv.org/abs/1603.05027

code: facebook-torch :

https://github.com/facebook/fb.resnet.torch/blob/master/models/preresnet.lua

code: official :

https://github.com/KaimingHe/resnet-1k-layers

code: unoffical-pytorch :

https://github.com/kuangliu/pytorch-cifar/blob/master/models/preact_resnet.py

code: unoffical-mxnet :

https://github.com/tornadomeet/ResNet

6. Inceptionv3

Rethinking the Inception Architecture for Computer Vision

Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Zbigniew Wojna

pdf: https://arxiv.org/abs/1512.00567

code: torchvision :

https://github.com/pytorch/vision/blob/master/torchvision/models/inception.py

code: keras-applications :

https://github.com/keras-team/keras-applications/blob/master/keras_applications/inception_v3.py

7. Inceptionv4 && Inception-ResNetv2

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi

pdf: https://arxiv.org/abs/1602.07261

code: unofficial-keras :

https://github.com/kentsommer/keras-inceptionV4

code: unofficial-keras :

https://github.com/titu1994/Inception-v4

code: unofficial-keras :

https://github.com/yuyang-huang/keras-inception-resnet-v2

8. RIR

Resnet in Resnet: Generalizing Residual Architectures

Sasha Targ, Diogo Almeida, Kevin Lyman

pdf: https://arxiv.org/abs/1603.08029

code: unofficial-tensorflow :

https://github.com/SunnerLi/RiR-Tensorflow

code: unofficial-chainer :

https://github.com/nutszebra/resnet_in_resnet

9. Stochastic Depth ResNet

Deep Networks with Stochastic Depth

Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, Kilian Weinberger

pdf: https://arxiv.org/abs/1603.09382

code: unofficial-torch :

https://github.com/yueatsprograms/Stochastic_Depth

code: unofficial-chainer :

https://github.com/yasunorikudo/chainer-ResDrop

code: unofficial-keras :

https://github.com/dblN/stochastic_depth_keras

10. WRN

Wide Residual Networks

Sergey Zagoruyko, Nikos Komodakis

pdf: https://arxiv.org/abs/1605.07146

code: official :

https://github.com/szagoruyko/wide-residual-networks

code: unofficial-pytorch :

https://github.com/xternalz/WideResNet-pytorch

code: unofficial-keras :

https://github.com/asmith26/wide_resnets_keras

code: unofficial-pytorch :

https://github.com/meliketoy/wide-resnet.pytorch

11. squeezenet

SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

Forrest N. Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, William J. Dally, Kurt Keutzer

pdf: https://arxiv.org/abs/1602.07360

code: torchvision :

https://github.com/pytorch/vision/blob/master/torchvision/models/squeezenet.py

code: unofficial-caffe :

https://github.com/DeepScale/SqueezeNet

code: unofficial-keras :

https://github.com/rcmalli/keras-squeezenet

code: unofficial-caffe :

https://github.com/songhan/SqueezeNet-Residual

12. GeNet

Genetic CNN

Lingxi Xie, Alan Yuille

pdf: https://arxiv.org/abs/1703.01513

code: unofficial-tensorflow :

https://github.com/aqibsaeed/Genetic-CNN

12. MetaQNN

Designing Neural Network Architectures using Reinforcement Learning

Bowen Baker, Otkrist Gupta, Nikhil Naik, Ramesh Raskar

pdf: https://arxiv.org/abs/1703.01513

code: official : https://github.com/bowenbaker/metaqnn

13. PyramidNet

Deep Pyramidal Residual Networks

Dongyoon Han, Jiwhan Kim, Junmo Kim

pdf: https://arxiv.org/abs/1610.02915

code: official :

https://github.com/jhkim89/PyramidNet

code: unofficial-pytorch :

https://github.com/dyhan0920/PyramidNet-PyTorch

14. DenseNet

Densely Connected Convolutional Networks

Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger

pdf: https://arxiv.org/abs/1608.06993

code: official :

https://github.com/liuzhuang13/DenseNet

code: unofficial-keras :

https://github.com/titu1994/DenseNet

code: unofficial-caffe :

https://github.com/shicai/DenseNet-Caffe

code: unofficial-tensorflow :

https://github.com/YixuanLi/densenet-tensorflow

code: unofficial-pytorch :

https://github.com/YixuanLi/densenet-tensorflow

code: unofficial-pytorch :

https://github.com/bamos/densenet.pytorch

code: unofficial-keras :

https://github.com/flyyufelix/DenseNet-Keras

15. FractalNet

FractalNet: Ultra-Deep Neural Networks without Residuals

Gustav Larsson, Michael Maire, Gregory Shakhnarovich

pdf: https://arxiv.org/abs/1605.07648

code: unofficial-caffe :

https://github.com/gustavla/fractalnet

code: unofficial-keras :

https://github.com/snf/keras-fractalnet

code: unofficial-tensorflow :

https://github.com/tensorpro/FractalNet

16. ResNext

Aggregated Residual Transformations for Deep Neural Networks

Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, Kaiming He

pdf: https://arxiv.org/abs/1611.05431

code: official :

https://github.com/facebookresearch/ResNeXt

code: keras-applications :

https://github.com/keras-team/keras-applications/blob/master/keras_applications/resnext.py

code: unofficial-pytorch :

https://github.com/prlz77/ResNeXt.pytorch

code: unofficial-keras :

https://github.com/titu1994/Keras-ResNeXt

code: unofficial-tensorflow :

https://github.com/taki0112/ResNeXt-Tensorflow

code: unofficial-tensorflow :

https://github.com/wenxinxu/ResNeXt-in-tensorflow

17. IGCV1

Interleaved Group Convolutions for Deep Neural Networks

Ting Zhang, Guo-Jun Qi, Bin Xiao, Jingdong Wang

pdf: https://arxiv.org/abs/1707.02725

code official :

https://github.com/hellozting/InterleavedGroupConvolutions

18. Residual Attention Network

Residual Attention Network for Image Classification

Fei Wang, Mengqing Jiang, Chen Qian, Shuo Yang, Cheng Li, Honggang Zhang, Xiaogang Wang, Xiaoou Tang

pdf: https://arxiv.org/abs/1704.06904

code: official :

https://github.com/fwang91/residual-attention-network

code: unofficial-pytorch :

https://github.com/tengshaofeng/ResidualAttentionNetwork-pytorch

code: unofficial-gluon :

https://github.com/PistonY/ResidualAttentionNetwork

code: unofficial-keras :

https://github.com/koichiro11/residual-attention-network

19. Xception

Xception: Deep Learning with Depthwise Separable Convolutions

François Chollet

pdf: https://arxiv.org/abs/1610.02357

code: unofficial-pytorch :

https://github.com/jfzhang95/pytorch-deeplab-xception/blob/master/modeling/backbone/xception.py

code: unofficial-tensorflow :

https://github.com/kwotsin/TensorFlow-Xception

code: unofficial-caffe :

https://github.com/yihui-he/Xception-caffe

code: unofficial-pytorch :

https://github.com/tstandley/Xception-PyTorch

code: keras-applications :

https://github.com/keras-team/keras-applications/blob/master/keras_applications/xception.py

20. MobileNet

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam

pdf: https://arxiv.org/abs/1704.04861

code: unofficial-tensorflow :

https://github.com/Zehaos/MobileNet

code: unofficial-caffe :

https://github.com/shicai/MobileNet-Caffe

code: unofficial-pytorch :

https://github.com/marvis/pytorch-mobilenet

code: keras-applications :

https://github.com/keras-team/keras-applications/blob/master/keras_applications/mobilenet.py

21. PolyNet

PolyNet: A Pursuit of Structural Diversity in Very Deep Networks

Xingcheng Zhang, Zhizhong Li, Chen Change Loy, Dahua Lin

pdf: https://arxiv.org/abs/1611.05725

code: official :

https://github.com/open-mmlab/polynet

22. DPN

Dual Path Networks

Yunpeng Chen, Jianan Li, Huaxin Xiao, Xiaojie Jin, Shuicheng Yan, Jiashi Feng

pdf: https://arxiv.org/abs/1707.01629

code: official :

https://github.com/cypw/DPNs

code: unoffical-keras :

https://github.com/titu1994/Keras-DualPathNetworks

code: unofficial-pytorch :

https://github.com/oyam/pytorch-DPNs

code: unofficial-pytorch :

https://github.com/rwightman/pytorch-dpn-pretrained

23. Block-QNN

Practical Block-wise Neural Network Architecture Generation

Zhao Zhong, Junjie Yan, Wei Wu, Jing Shao, Cheng-Lin Liu

pdf: https://arxiv.org/abs/1708.05552

24. CRU-Net

Sharing Residual Units Through Collective Tensor Factorization in Deep Neural Networks

Chen Yunpeng, Jin Xiaojie, Kang Bingyi, Feng Jiashi, Yan Shuicheng

pdf: https://arxiv.org/abs/1703.02180

code official :

https://github.com/cypw/CRU-Net

code unofficial-mxnet :

https://github.com/bruinxiong/Modified-CRUNet-and-Residual-Attention-Network.mxnet

25. ShuffleNet

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, Jian Sun

pdf: https://arxiv.org/abs/1707.01083

code: unofficial-tensorflow :

https://github.com/MG2033/ShuffleNet

code: unofficial-pytorch :

https://github.com/jaxony/ShuffleNet

code: unofficial-caffe :

https://github.com/farmingyard/ShuffleNet

code: unofficial-keras :

https://github.com/scheckmedia/keras-shufflenet

26. CondenseNet

CondenseNet: An Efficient DenseNet using Learned Group Convolutions

Gao Huang, Shichen Liu, Laurens van der Maaten, Kilian Q. Weinberger

pdf: https://arxiv.org/abs/1711.09224

code: official :

https://github.com/ShichenLiu/CondenseNet

code: unofficial-tensorflow :

https://github.com/markdtw/condensenet-tensorflow

27. NasNet

Learning Transferable Architectures for Scalable Image Recognition

Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le

pdf: https://arxiv.org/abs/1707.07012

code: unofficial-keras :

https://github.com/titu1994/Keras-NASNet

code: keras-applications :

https://github.com/keras-team/keras-applications/blob/master/keras_applications/nasnet.py

code: unofficial-pytorch :

https://github.com/wandering007/nasnet-pytorch

code: unofficial-tensorflow :

https://github.com/yeephycho/nasnet-tensorflow

28. MobileNetV2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen

pdf: https://arxiv.org/abs/1801.04381

code: unofficial-keras :

https://github.com/xiaochus/MobileNetV2

code: unofficial-pytorch :

https://github.com/Randl/MobileNetV2-pytorch

code: unofficial-tensorflow :

https://github.com/neuleaf/MobileNetV2

29. IGCV2

IGCV2: Interleaved Structured Sparse Convolutional Neural Networks

Guotian Xie, Jingdong Wang, Ting Zhang, Jianhuang Lai, Richang Hong, Guo-Jun Qi

pdf: https://arxiv.org/abs/1804.06202

30. hier

Hierarchical Representations for Efficient Architecture Search

Hanxiao Liu, Karen Simonyan, Oriol Vinyals, Chrisantha Fernando, Koray Kavukcuoglu

pdf: https://arxiv.org/abs/1711.00436

31. PNasNet

Progressive Neural Architecture Search

Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, Kevin Murphy

pdf: https://arxiv.org/abs/1712.00559

code: tensorflow-slim :

https://github.com/tensorflow/models/blob/master/research/slim/nets/nasnet/pnasnet.py

code: unofficial-pytorch :

https://github.com/chenxi116/PNASNet.pytorch

code: unofficial-tensorflow :

https://github.com/chenxi116/PNASNet.TF

32. AmoebaNet

Regularized Evolution for Image Classifier Architecture Search

Esteban Real, Alok Aggarwal, Yanping Huang, Quoc V Le

pdf: https://arxiv.org/abs/1802.01548

code: tensorflow-tpu :

https://github.com/tensorflow/tpu/tree/master/models/official/amoeba_net

33. SENet

Squeeze-and-Excitation Networks

Jie Hu, Li Shen, Samuel Albanie, Gang Sun, Enhua Wu

pdf: https://arxiv.org/abs/1709.01507

code: official :

https://github.com/hujie-frank/SENet

code: unofficial-pytorch :

https://github.com/moskomule/senet.pytorch

code: unofficial-tensorflow :

https://github.com/taki0112/SENet-Tensorflow

code: unofficial-caffe :

https://github.com/shicai/SENet-Caffe

code: unofficial-mxnet :

https://github.com/bruinxiong/SENet.mxnet

34. ShuffleNetV2

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, Jian Sun

pdf: https://arxiv.org/abs/1807.11164

code: unofficial-pytorch :

https://github.com/Randl/ShuffleNetV2-pytorch

code: unofficial-keras :

https://github.com/opconty/keras-shufflenetV2

code: unofficial-pytorch :

https://github.com/Bugdragon/ShuffleNet_v2_PyTorch

code: unofficial-caff2:

https://github.com/wolegechu/ShuffleNetV2.Caffe2

35. IGCV3

IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks

Ke Sun, Mingjie Li, Dong Liu, Jingdong Wang

pdf: https://arxiv.org/abs/1806.00178

code: official :

https://github.com/homles11/IGCV3

code: unofficial-pytorch :

https://github.com/xxradon/IGCV3-pytorch

code: unofficial-tensorflow :

https://github.com/ZHANG-SHI-CHANG/IGCV3

36. MNasNet

MnasNet: Platform-Aware Neural Architecture Search for Mobile

Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Quoc V. Le

pdf: https://arxiv.org/abs/1807.11626

code: unofficial-pytorch :

https://github.com/AnjieZheng/MnasNet-PyTorch

code: unofficial-caffe :

https://github.com/LiJianfei06/MnasNet-caffe

code: unofficial-MxNet :

https://github.com/chinakook/Mnasnet.MXNet

code: unofficial-keras :

https://github.com/Shathe/MNasNet-Keras-Tensorflow




    



    
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​【深度学习】CV 图像分类常见的 36 个模型

背景

性能表

论文&代码

【深度学习】CV 图像分类常见的 36 个模型