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！
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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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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