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
DLA	75.3	-	CVPR2018
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	AAAI2018
SENet	-	97.749	CVPR2018
ShuffleNetV2	81.44	-	ECCV2018
CBAM	79.93	94.41	ECCV2018
IGCV3	72.2	-	BMVC2018
BAM	77.56	93.71	BMVC2018
MnasNet	76.13	92.85	CVPR2018
SKNet	80.60	-	CVPR2019
DARTS	73.3	91.3	ICLR2019
ProxylessNAS	75.1	92.5	ICLR2019
MobileNetV3	75.2	-	CVPR2019
Res2Net	79.2	94.37	PAMI2019
LIP-ResNet	79.33	94.6	ICCV2019
EfficientNet	84.3	97.0	ICML2019
FixResNeXt	86.4	98.0	NIPS2019
BiT	87.5	-	ECCV2020
PSConv + ResNext101	80.502	95.276	ECCV2020
NoisyStudent	88.4	98.7	CVPR2020
RegNet	79.9	-	CVPR2020
GhostNet	75.7	-	CVPR2020
ViT	88.55	-	ICLR2021
DeiT	85.2	-	ICML2021
PVT	81.7	-	ICCV2021
T2T-Vit	83.3	-	ICCV2021
DeepVit	80.9	-	Arvix2021
ViL	83.7	-	ICCV2021
TNT	83.9	-	Arvix2021
CvT	87.7	-	ICCV2021
CViT	84.1	-	ICCV2021
Focal-T	84.0	-	NIPS2021
Twins	83.7	-	NIPS2021
PVTv2	81.7	-	CVM2022

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

• 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

DLA

Deep Layer Aggregation Fisher Yu, Dequan Wang, Evan Shelhamer, Trevor Darrell

• pdf: https://arxiv.org/abs/1707.06484
• code: official-pytorch: https://github.com/ucbdrive/dla

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

CBAM

CBAM: Convolutional Block Attention Module Sanghyun Woo, Jongchan Park, Joon-Young Lee, In So Kweon

• pdf: https://arxiv.org/abs/1807.06521
• code: official-pytorch : https://github.com/Jongchan/attention-module
• code: unofficial-pytorch : https://github.com/luuuyi/CBAM.PyTorch
• code: unofficial-pytorch : https://github.com/elbuco1/CBAM
• code: unofficial-keras : https://github.com/kobiso/CBAM-keras

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

BAM

BAM: Bottleneck Attention Module Jongchan Park, Sanghyun Woo, Joon-Young Lee, In So Kweon

• pdf: https://arxiv.org/abs/1807.06514
• code: official-pytorch : https://github.com/Jongchan/attention-module
• code: unofficial-tensorflow : https://github.com/huyz1117/BAM

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

SKNet

Selective Kernel Networks Xiang Li, Wenhai Wang, Xiaolin Hu, Jian Yang

• pdf: https://arxiv.org/abs/1903.06586
• code: official : https://github.com/implus/SKNet

DARTS

DARTS: Differentiable Architecture Search Hanxiao Liu, Karen Simonyan, Yiming Yang

• pdf: https://arxiv.org/abs/1806.09055
• code: official : https://github.com/quark0/darts
• code: unofficial-pytorch : https://github.com/khanrc/pt.darts
• code: unofficial-tensorflow : https://github.com/NeroLoh/darts-tensorflow

ProxylessNAS

ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware Han Cai, Ligeng Zhu, Song Han

• pdf: https://arxiv.org/abs/1812.00332
• code: official : https://github.com/mit-han-lab/ProxylessNAS

MobileNetV3

Searching for MobileNetV3 Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, Hartwig Adam

• pdf: https://arxiv.org/abs/1905.02244
• code: unofficial-pytorch : https://github.com/xiaolai-sqlai/mobilenetv3
• code: unofficial-pytorch : https://github.com/kuan-wang/pytorch-mobilenet-v3
• code: unofficial-pytorch : https://github.com/leaderj1001/MobileNetV3-Pytorch
• code: unofficial-pytorch : https://github.com/d-li14/mobilenetv3.pytorch
• code: unofficial-caffe : https://github.com/jixing0415/caffe-mobilenet-v3
• code: unofficial-keras : https://github.com/xiaochus/MobileNetV3

Res2Net

Res2Net: A New Multi-scale Backbone Architecture Shang-Hua Gao, Ming-Ming Cheng, Kai Zhao, Xin-Yu Zhang, Ming-Hsuan Yang, Philip Torr

• pdf: https://arxiv.org/abs/1904.01169
• code: unofficial-pytorch : https://github.com/4uiiurz1/pytorch-res2net
• code: unofficial-keras : https://github.com/fupiao1998/res2net-keras
• code: official-pytorch : https://github.com/Res2Net

LIP-ResNet

LIP: Local Importance-based Pooling Ziteng Gao, Limin Wang, Gangshan Wu

• pdf: https://arxiv.org/abs/1908.04156
• code: official-pytorch : https://github.com/sebgao/LIP

EfficientNet

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks Mingxing Tan, Quoc V. Le

• pdf: https://arxiv.org/abs/1905.11946
• code: unofficial-pytorch : https://github.com/lukemelas/EfficientNet-PyTorch
• code: official-tensorflow : https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet

FixResNeXt

Fixing the train-test resolution discrepancy Hugo Touvron, Andrea Vedaldi, Matthijs Douze, Hervé Jégou

• pdf: https://arxiv.org/abs/1906.06423
• code: official-pytorch : https://github.com/facebookresearch/FixRes

BiT

Big Transfer (BiT): General Visual Representation Learning Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby

• pdf: https://arxiv.org/abs/1912.11370
• code: official-tensorflow: https://github.com/google-research/big_transfer

PSConv + ResNext101

PSConv: Squeezing Feature Pyramid into One Compact Poly-Scale Convolutional Layer Duo Li1, Anbang Yao2B, and Qifeng Chen1B

• pdf: https://arxiv.org/abs/2007.06191
• code: https://github.com/d-li14/PSConv

NoisyStudent

Self-training with Noisy Student improves ImageNet classification Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le

• pdf: https://arxiv.org/abs/1911.04252
• code: official-tensorflow: https://github.com/google-research/noisystudent
• code: unofficial-pytorch: https://github.com/sally20921/NoisyStudent

RegNet

Designing Network Design Spaces Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár

• pdf: https://arxiv.org/abs/2003.13678
• code: official-pytorch: https://github.com/facebookresearch/pycls
• code: unofficial-pytorch: https://github.com/d-li14/regnet.pytorch

GhostNet

GhostNet: More Features from Cheap Operations Kai Han, Yunhe Wang, Qi Tian, Jianyuan Guo, Chunjing Xu, Chang Xu

• pdf: https://arxiv.org/abs/1911.11907
• code: official-pytorch: https://github.com/huawei-noah/ghostnet

ViT

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby

• pdf: https://arxiv.org/abs/2010.11929
• code: official-tensorflow: https://github.com/google-research/vision_transformer
• code: unofficial-pytorch: https://github.com/jeonsworld/ViT-pytorch

DeiT

Training data-efficient image transformers & distillation through attention Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou

• pdf: https://arxiv.org/abs/2012.12877
• code: official-pytorch: https://github.com/facebookresearch/deit

PVT

Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao

• pdf: https://arxiv.org/abs/2102.12122
• code: official-pytorch: https://github.com/whai362/PVT

T2T

Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet Li Yuan, Yunpeng Chen, Tao Wang, Weihao Yu, Yujun Shi, Zihang Jiang, Francis EH Tay, Jiashi Feng, Shuicheng Yan

• pdf: https://arxiv.org/abs/2101.11986
• code: official-pytorch: https://github.com/yitu-opensource/T2T-ViT

DeepVit

DeepViT: Towards Deeper Vision Transformer Daquan Zhou, Bingyi Kang, Xiaojie Jin, Linjie Yang, Xiaochen Lian, Zihang Jiang, Qibin Hou, and Jiashi Feng.

• pdf: https://arxiv.org/abs/2103.11886
• code: official-pytorch: https://github.com/zhoudaquan/dvit_repo

ViL

Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding Pengchuan Zhang, Xiyang Dai, Jianwei Yang, Bin Xiao, Lu Yuan, Lei Zhang, Jianfeng Gao

• pdf: https://arxiv.org/abs/2103.15358
• code: official-pytorch: https://github.com/microsoft/vision-longformer

TNT

Transformer in Transformer Kai Han, An Xiao, Enhua Wu, Jianyuan Guo, Chunjing Xu, Yunhe Wang

• pdf: https://arxiv.org/abs/2103.00112
• code: https://github.com/huawei-noah/CV-Backbones

CvT

CvT: Introducing Convolutions to Vision Transformers Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, Lei Zhang

• pdf: https://arxiv.org/abs/2103.15808
• code: https://github.com/microsoft/CvT

CViT

CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification Chun-Fu (Richard) Chen, Quanfu Fan, Rameswar Panda

• pdf: https://arxiv.org/abs/2103.14899
• code: https://github.com/IBM/CrossViT

Focal-T

Focal Attention for Long-Range Interactions in Vision Transformers Jianwei Yang, Chunyuan Li, Pengchuan Zhang, Xiyang Dai, Bin Xiao, Lu Yuan, Jianfeng Gao

• pdf: https://arxiv.org/abs/2107.00641
• code: https://github.com/microsoft/Focal-Transformer

Twins

Twins: Revisiting the Design of Spatial Attention in Vision Transformers

• pdf: https://arxiv.org/abs/2104.13840
• code: https://git.io/Twins

PVTv2

Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao

• pdf: https://arxiv.org/abs/2106.13797
• code: official-pytorch: https://github.com/whai362/PVT