图像分类最全资料

作者weiaicunzai在github上开源了自己对于图像分类前沿论文及代码的总结。对于想学习图像分类的同学有很大的帮助。该项目更新频率很高，作者一直在维持更新。快快收藏~

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

这是 weiaicunzai 在学习图像分类时，整理的论文和代码等资源合集。其中论文年份是从2014年开始，所列出的论文都是在ImageNet上有一定 TOP1和TOP5 准确度的。

1、网络实验数据表格

部分表格截图

2、论文&代码

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

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

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

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

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

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

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

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

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

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

squeezenet

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

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

GeNet

Genetic CNN

Lingxi Xie, Alan Yuille

• pdf: https://arxiv.org/abs/1703.01513
• code: unofficial-tensorflow : https://github.com/aqibsaeed/Genetic-CNN

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

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

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

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

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

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

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

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

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

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

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

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

CRU-Net

Sharing Residual Units Through Collective Tensor Factorization in Deep Neural Networks

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

• pdfhttps://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

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

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

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

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

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

hier

Hierarchical Representations for Efficient Architecture Search

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

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

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

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

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

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

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