daaiyiyejian/awesome-image-classification

A curated list of deep learning image classification papers and codes

Awesome - Image Classification

A curated list of deep learning image classification papers and codes since 2014, Inspired by awesome-object-detection, deep_learning_object_detection and awesome-deep-learning-papers.

Background

I believe image classification is a great start point before diving into other computer vision fields, espacially for begginers who know nothing about deep learning. When I started to learn computer vision, I've made a lot of mistakes, I wish someone could have told me that which paper I should start with back then. There doesn't seem to have a repository to have a list of image classification papers like deep_learning_object_detection until now. Therefore, I decided to make a repository of a list of deep learning image classification papers and codes to help others. My personal advice for people who know nothing about deep learning, try to start with vgg, then googlenet, resnet, feel free to continue reading other listed papers or switch to other fields after you are finished.

Note: I also have a repository of pytorch implementation of some of the image classification networks, you can check out here.

Performance Table

For simplicity reason, I only listed the best top1 and top5 accuracy on ImageNet from the papers. Note that this does not necessarily mean one network is better than another when the acc is higher, cause some networks are focused on reducing the model complexity instead of improving accuracy, or some papers only give the single crop results on ImageNet, but others give the model fusion or multicrop results.

• ConvNet: name of the covolution network
• ImageNet top1 acc: best top1 accuracy on ImageNet from the Paper
• ImageNet top5 acc: best top5 accuracy on ImageNet from the Paper
• Published In: which conference or journal the paper was published in.

ConvNet	ImageNet top1 acc	ImageNet top5 acc	Published In
Vgg	76.3	93.2	ICLR2015
GoogleNet	-	93.33	CVPR2015
PReLU-nets	-	95.06	ICCV2015
ResNet	-	96.43	CVPR2015
PreActResNet	79.9	95.2	CVPR2016
Inceptionv3	82.8	96.42	CVPR2016
Inceptionv4	82.3	96.2	AAAI2016
Inception-ResNet-v2	82.4	96.3	AAAI2016
Inceptionv4 + Inception-ResNet-v2	83.5	96.92	AAAI2016
RiR	-	-	ICLR Workshop2016
Stochastic Depth ResNet	78.02	-	ECCV2016
WRN	78.1	94.21	BMVC2016
SqueezeNet	60.4	82.5	arXiv2017(rejected by ICLR2017)
GeNet	72.13	90.26	ICCV2017
MetaQNN	-	-	ICLR2017
PyramidNet	80.8	95.3	CVPR2017
DenseNet	79.2	94.71	ECCV2017
FractalNet	75.8	92.61	ICLR2017
ResNext	-	96.97	CVPR2017
IGCV1	73.05	91.08	ICCV2017
Residual Attention Network	80.5	95.2	CVPR2017
Xception	79	94.5	CVPR2017
MobileNet	70.6	-	arXiv2017
PolyNet	82.64	96.55	CVPR2017
DPN	79	94.5	NIPS2017
Block-QNN	77.4	93.54	CVPR2018
CRU-Net	79.7	94.7	IJCAI2018
ShuffleNet	75.3	-	CVPR2018
CondenseNet	73.8	91.7	CVPR2018
NasNet	82.7	96.2	CVPR2018
MobileNetV2	74.7	-	CVPR2018
IGCV2	70.07	-	CVPR2018
hier	79.7	94.8	ICLR2018
PNasNet	82.9	96.2	ECCV2018
AmoebaNet	83.9	96.6	arXiv2018
SENet	-	97.749	CVPR2018
ShuffleNetV2	81.44	-	ECCV2018
IGCV3	72.2	-	BMVC2018
MnasNet	76.13	92.85	arXiv2018

Papers&Codes

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 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

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/1611.02167
• 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