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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原文标题:CV 图像分类常见的 36 个模型汇总!附完整论文和代码
文章出处:【微信号:vision263com,微信公众号:新机器视觉】欢迎添加关注!文章转载请注明出处。
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