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<div align="right"> Language: 🇺🇸 <a title="Chinese" href="./README.zh-CN.md">🇨🇳</a> </div> <div align="center"><a title="" href="https://github.com/ZJCV/ZCls"><img align="center" src="./imgs/ZCls.png"></a></div> <p align="center"> «ZCls» is a classification model training/inferring framework <br> <br> <a href="https://github.com/RichardLitt/standard-readme"><img src="https://img.shields.io/badge/standard--readme-OK-green.svg?style=flat-square"></a> <a href="https://conventionalcommits.org"><img src="https://img.shields.io/badge/Conventional%20Commits-1.0.0-yellow.svg"></a> <a href="http://commitizen.github.io/cz-cli/"><img src="https://img.shields.io/badge/commitizen-friendly-brightgreen.svg"></a> <a href="https://pypi.org/project/zcls/"><img src="https://img.shields.io/badge/PYPI-zcls-brightgreen"></a> <a href='https://zcls.readthedocs.io/en/latest/?badge=latest'> <img src='https://readthedocs.org/projects/zcls/badge/?version=latest' alt='Documentation Status' /> </a> </p>

A more faster training framework is under building: ZCls2

Supported Recognizers:

<p align="center"> <img align="center" src="./imgs/roadmap.svg"> </p>

Refer to roadmap for details

Table of Contents

Background

In the fields of object detection/object segmentation/action recognition, there have been many training frameworks with high integration and perfect process, such as facebookresearch/detectron2, open-mmlab/mmaction2 ...

Object classification is the most developed and theoretically basic field in deeplearning. Referring to the existing training framework, a training/inferring framework based on object classification model is implemented. I hope ZCls can bring you a better realization.

Installation

See INSTALL

Usage

How to train, see Get Started with ZCls

Use builtin datasets, see Use Builtin Datasets

Use custom datasets, see Use Custom Datasets

Use pretrained model, see Use Pretrained Model

Maintainers

Thanks

@misc{ding2021diverse,
      title={Diverse Branch Block: Building a Convolution as an Inception-like Unit}, 
      author={Xiaohan Ding and Xiangyu Zhang and Jungong Han and Guiguang Ding},
      year={2021},
      eprint={2103.13425},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{ding2021repvgg,
      title={RepVGG: Making VGG-style ConvNets Great Again}, 
      author={Xiaohan Ding and Xiangyu Zhang and Ningning Ma and Jungong Han and Guiguang Ding and Jian Sun},
      year={2021},
      eprint={2101.03697},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{fan2020pyslowfast,
  author =       {Haoqi Fan and Yanghao Li and Bo Xiong and Wan-Yen Lo and
                  Christoph Feichtenhofer},
  title =        {PySlowFast},
  howpublished = {\url{https://github.com/facebookresearch/slowfast}},
  year =         {2020}
}

@misc{zhang2020resnest,
      title={ResNeSt: Split-Attention Networks}, 
      author={Hang Zhang and Chongruo Wu and Zhongyue Zhang and Yi Zhu and Haibin Lin and Zhi Zhang and Yue Sun and Tong He and Jonas Mueller and R. Manmatha and Mu Li and Alexander Smola},
      year={2020},
      eprint={2004.08955},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{han2020ghostnet,
      title={GhostNet: More Features from Cheap Operations}, 
      author={Kai Han and Yunhe Wang and Qi Tian and Jianyuan Guo and Chunjing Xu and Chang Xu},
      year={2020},
      eprint={1911.11907},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

For more thanks, check THANKS

Contributing

Anyone's participation is welcome! Open an issue or submit PRs.

Small note:

License

Apache License 2.0 © 2020 zjykzj