来源：新机器视觉
链接：https://mp.weixin.qq.com/s/NY0ditoxAI7x1ji4LQxWgg
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
image
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 : **
**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
- 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
声明：部分内容来源于网络，仅供读者学术交流之目的。文章版权归原作者所有。如有不妥，请联系删除。
网友评论