jimilee / image-classification

A collection of SOTA Image Classification Models in PyTorch

SOTA Image Classification Models in PyTorch

Model Zoo

Model	ImageNet-1k Top-1 Acc (%)	Params (M)	GFLOPs	Variants & Weights
MicroNet	51.4|59.4|62.5	2|2|3	7M|14M|23M	M1|M2|M3
MobileFormer	76.7|77.9|79.3	9|11|14	214M|294M|508M	214|294|508
ResNet*	71.5|80.4|81.5	12|26|45	2|4|8	18|50|101
GFNet	80.1|81.5|82.9	15|32|54	2|5|8	T|S|B
HireMLP	79.7|82.1|83.2	18|33|58	2|4|8	T|S|B
WaveMLP	80.9|82.9|83.3	17|30|44	2|5|8	T|S|M
PVTv2	78.7|82.0|83.6	14|25|63	2|4|10	B1|B2|B4
ResT	79.6|81.6|83.6	14|30|52	2|4|8	S|B|L
UniFormer	----|82.9|83.8	--|22|50	-|4|8	-|S|B
PoolFormer	80.3|81.4|82.1	21|31|56	4|5|9	S24|S36|M36
CycleMLP	81.6|83.0|83.2	27|52|76	4|10|12	B2|B4|B5
PatchConvnet	82.1|83.2|83.5	25|48|99	4|8|16	S60|S120|B60
ConvNeXt	82.1|83.1|83.8	28|50|89	5|9|15	T|S|B
Shuffle	82.4|83.6|84.0	28|50|88	5|9|16	T|S|B
Conformer	81.3|83.4|84.1	24|38|83	5|11|23	T|S|B
CSWin	82.7|83.6|84.2	23|35|78	4|7|15	T|S|B

Notes: ResNet* is from "ResNet strikes back" paper.

Table Notes

• Only include models trained on ImageNet1k with image size of 224x224.
• Models' weights are from respective official repositories.
• Large mdoels (Parameters > 100M) are not included.

Usage

Requirements (click to expand)

• python >= 3.6
• torch >= 1.8.1
• torchvision >= 0.9.1

Other requirements can be installed with pip install -r requirements.txt.

Show Available Models

$ python tools/show.py

A table with model names and variants will be shown:

Model Names    Model Variants
-------------  --------------------------------
ResNet         ['18', '34', '50', '101', '152']
MicroNet       ['M1', 'M2', 'M3']
ConvNeXt       ['T', 'S', 'B']
GFNet          ['T', 'S', 'B']
PVTv2          ['B1', 'B2', 'B3', 'B4', 'B5']
ResT           ['S', 'B', 'L']
Conformer      ['T', 'S', 'B']
Shuffle        ['T', 'S', 'B']
CSWin          ['T', 'S', 'B', 'L']
CycleMLP       ['B1', 'B2', 'B3', 'B4', 'B5']
HireMLP        ['T', 'S', 'B']
WaveMLP        ['T', 'S', 'M']
PoolFormer     ['S24', 'S36', 'M36']
PatchConvnet   ['S60', 'S120', 'B60']
UniFormer      ['S', 'B']

Inference

• Download your desired model's weights from Model Zoo table.
• Change MODEL parameters and TEST parameters in config file here. And run the the following command.

$ python tools/infer.py --cfg configs/test.yaml

You will see an output similar to this:

File: assests\dog.jpg >>>>> Golden retriever

Training

$ python tools/train.py --cfg configs/train.yaml

Evaluate

$ python tools/val.py --cfg configs/train.yaml

Fine-tune

Fine-tune on CIFAR-10:

$ python tools/finetune.py --cfg configs/finetune.yaml

References

• https://github.com/rwightman/pytorch-image-models
• https://github.com/facebookresearch/deit

Citations

@misc{li2021micronet,
  title={MicroNet: Improving Image Recognition with Extremely Low FLOPs}, 
  author={Yunsheng Li and Yinpeng Chen and Xiyang Dai and Dongdong Chen and Mengchen Liu and Lu Yuan and Zicheng Liu and Lei Zhang and Nuno Vasconcelos},
  year={2021},
  eprint={2108.05894},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

@misc{wightman2021resnet,
  title={ResNet strikes back: An improved training procedure in timm}, 
  author={Ross Wightman and Hugo Touvron and Hervé Jégou},
  year={2021},
  eprint={2110.00476},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

@misc{rao2021global,
  title={Global Filter Networks for Image Classification}, 
  author={Yongming Rao and Wenliang Zhao and Zheng Zhu and Jiwen Lu and Jie Zhou},
  year={2021},
  eprint={2107.00645},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

@misc{wang2021pvtv2,
  title={PVTv2: Improved Baselines with Pyramid Vision Transformer}, 
  author={Wenhai Wang and Enze Xie and Xiang Li and Deng-Ping Fan and Kaitao Song and Ding Liang and Tong Lu and Ping Luo and Ling Shao},
  year={2021},
  eprint={2106.13797},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

@misc{zhang2021rest,
  title={ResT: An Efficient Transformer for Visual Recognition}, 
  author={Qinglong Zhang and Yubin Yang},
  year={2021},
  eprint={2105.13677},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

@misc{touvron2021augmenting,
  title={Augmenting Convolutional networks with attention-based aggregation}, 
  author={Hugo Touvron and Matthieu Cord and Alaaeldin El-Nouby and Piotr Bojanowski and Armand Joulin and Gabriel Synnaeve and Hervé Jégou},
  year={2021},
  eprint={2112.13692},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

@misc{peng2021conformer,
  title={Conformer: Local Features Coupling Global Representations for Visual Recognition}, 
  author={Zhiliang Peng and Wei Huang and Shanzhi Gu and Lingxi Xie and Yaowei Wang and Jianbin Jiao and Qixiang Ye},
  year={2021},
  eprint={2105.03889},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

@misc{huang2021shuffle,
  title={Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer}, 
  author={Zilong Huang and Youcheng Ben and Guozhong Luo and Pei Cheng and Gang Yu and Bin Fu},
  year={2021},
  eprint={2106.03650},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

@misc{dong2022cswin,
  title={CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows}, 
  author={Xiaoyi Dong and Jianmin Bao and Dongdong Chen and Weiming Zhang and Nenghai Yu and Lu Yuan and Dong Chen and Baining Guo},
  year={2022},
  eprint={2107.00652},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

@misc{chen2021cyclemlp,
  title={CycleMLP: A MLP-like Architecture for Dense Prediction}, 
  author={Shoufa Chen and Enze Xie and Chongjian Ge and Runjian Chen and Ding Liang and Ping Luo},
  year={2021},
  eprint={2107.10224},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

@misc{guo2021hiremlp,
  title={Hire-MLP: Vision MLP via Hierarchical Rearrangement}, 
  author={Jianyuan Guo and Yehui Tang and Kai Han and Xinghao Chen and Han Wu and Chao Xu and Chang Xu and Yunhe Wang},
  year={2021},
  eprint={2108.13341},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

@misc{yu2021metaformer,
  title={MetaFormer is Actually What You Need for Vision}, 
  author={Weihao Yu and Mi Luo and Pan Zhou and Chenyang Si and Yichen Zhou and Xinchao Wang and Jiashi Feng and Shuicheng Yan},
  year={2021},
  eprint={2111.11418},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

@misc{guo2021hiremlp,
  title={Hire-MLP: Vision MLP via Hierarchical Rearrangement}, 
  author={Jianyuan Guo and Yehui Tang and Kai Han and Xinghao Chen and Han Wu and Chao Xu and Chang Xu and Yunhe Wang},
  year={2021},
  eprint={2108.13341},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

@misc{tang2021image,
  title={An Image Patch is a Wave: Phase-Aware Vision MLP}, 
  author={Yehui Tang and Kai Han and Jianyuan Guo and Chang Xu and Yanxi Li and Chao Xu and Yunhe Wang},
  year={2021},
  eprint={2111.12294},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

@misc{liu2022convnet,
  title={A ConvNet for the 2020s}, 
  author={Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
  year={2022},
  eprint={2201.03545},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

@misc{li2022uniformer,
  title={UniFormer: Unifying Convolution and Self-attention for Visual Recognition}, 
  author={Kunchang Li and Yali Wang and Junhao Zhang and Peng Gao and Guanglu Song and Yu Liu and Hongsheng Li and Yu Qiao},
  year={2022},
  eprint={2201.09450},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}