Implement a few key architectures for image classification by using neural network
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 | 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 | CVPR2018 |
SKNet | 80.60 | - | CVPR2019 |
DARTS | 73.3 | 91.3 | ICLR2019 |
ProxylessNAS | 75.1 | 92.5 | ICLR2019 |
MobileNetV3 | 75.2 | - | arXiv2019 |
Res2Net | 79.2 | 94.37 | arXiv2019 |
Very Deep Convolutional Networks for Large-Scale Image Recognition. Karen Simonyan, Andrew Zisserman
Going Deeper with Convolutions Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Identity Mappings in Deep Residual Networks Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Rethinking the Inception Architecture for Computer Vision Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Zbigniew Wojna
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi
Resnet in Resnet: Generalizing Residual Architectures Sasha Targ, Diogo Almeida, Kevin Lyman
Deep Networks with Stochastic Depth Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, Kilian Weinberger
Wide Residual Networks Sergey Zagoruyko, Nikos Komodakis
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
Genetic CNN Lingxi Xie, Alan Yuille
Designing Neural Network Architectures using Reinforcement Learning Bowen Baker, Otkrist Gupta, Nikhil Naik, Ramesh Raskar
Deep Pyramidal Residual Networks Dongyoon Han, Jiwhan Kim, Junmo Kim
Densely Connected Convolutional Networks Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger
FractalNet: Ultra-Deep Neural Networks without Residuals Gustav Larsson, Michael Maire, Gregory Shakhnarovich
Aggregated Residual Transformations for Deep Neural Networks Saining Xie, Ross Girshick, Piotr Dollr, Zhuowen Tu, Kaiming He
Interleaved Group Convolutions for Deep Neural Networks Ting Zhang, Guo-Jun Qi, Bin Xiao, Jingdong Wang
Residual Attention Network for Image Classification Fei Wang, Mengqing Jiang, Chen Qian, Shuo Yang, Cheng Li, Honggang Zhang, Xiaogang Wang, Xiaoou Tang
Xception: Deep Learning with Depthwise Separable Convolutions Franois Chollet
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
PolyNet: A Pursuit of Structural Diversity in Very Deep Networks Xingcheng Zhang, Zhizhong Li, Chen Change Loy, Dahua Lin
Dual Path Networks Yunpeng Chen, Jianan Li, Huaxin Xiao, Xiaojie Jin, Shuicheng Yan, Jiashi Feng
Practical Block-wise Neural Network Architecture Generation Zhao Zhong, Junjie Yan, Wei Wu, Jing Shao, Cheng-Lin Liu
Sharing Residual Units Through Collective Tensor Factorization in Deep Neural Networks Chen Yunpeng, Jin Xiaojie, Kang Bingyi, Feng Jiashi, Yan Shuicheng
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, Jian Sun
CondenseNet: An Efficient DenseNet using Learned Group Convolutions Gao Huang, Shichen Liu, Laurens van der Maaten, Kilian Q. Weinberger
Learning Transferable Architectures for Scalable Image Recognition Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le
MobileNetV2: Inverted Residuals and Linear Bottlenecks Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen
IGCV2: Interleaved Structured Sparse Convolutional Neural Networks Guotian Xie, Jingdong Wang, Ting Zhang, Jianhuang Lai, Richang Hong, Guo-Jun Qi
Hierarchical Representations for Efficient Architecture Search Hanxiao Liu, Karen Simonyan, Oriol Vinyals, Chrisantha Fernando, Koray Kavukcuoglu
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
Regularized Evolution for Image Classifier Architecture Search Esteban Real, Alok Aggarwal, Yanping Huang, Quoc V Le
Squeeze-and-Excitation Networks Jie Hu, Li Shen, Samuel Albanie, Gang Sun, Enhua Wu
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, Jian Sun
IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks Ke Sun, Mingjie Li, Dong Liu, Jingdong Wang
MnasNet: Platform-Aware Neural Architecture Search for Mobile Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Quoc V. Le
Selective Kernel Networks Xiang Li, Wenhai Wang, Xiaolin Hu, Jian Yang
DARTS: Differentiable Architecture Search Hanxiao Liu, Karen Simonyan, Yiming Yang
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware Han Cai, Ligeng Zhu, Song Han
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
Res2Net: A New Multi-scale Backbone Architecture Shang-Hua Gao, Ming-Ming Cheng, Kai Zhao, Xin-Yu Zhang, Ming-Hsuan Yang, Philip Torr