zcls2 0.5.3
More Faster Object Classification Training Framework
Requires Python: >=3.8

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  «ZCls2» is a more faster classification model training framework
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## Table of Contents

- [Table of Contents](#table-of-contents)
- [Background](#background)
- [Installation](#installation)
- [Usage](#usage)
- [Maintainers](#maintainers)
- [Thanks](#thanks)
- [Contributing](#contributing)
- [License](#license)

## Background

After nearly one and a half years of development, [ZCls](https://github.com/ZJCV/ZCls) has integrated many training features, includes configuration module, register module, training module, and many model implementations (`resnet/mobilenet/senet-sknet-resnest/acbnet-repvgg-dbbnet/ghostnet/gcnet...`) and so on. In the development process, it is found that compared with the current excellent classification training framework, such as [apex](https://github.com/NVIDIA/apex/tree/master/examples/imagenet), the training speed of [ZCls](https://github.com/ZJCV/ZCls) is not outstanding. 

In order to better improve the training speed, we decided to develop a new training framework [ZCls2](https://github.com/ZJCV/ZCls2), which is implemented based on [apex](https://github.com/NVIDIA/apex/tree/master/examples/imagenet) and provides more friendly and powerful functions. In the preliminary implementation, it can be found that [ZCls2](https://github.com/ZJCV/ZCls2) improves the training speed by at least 50% compared with [ZCls](https://github.com/ZJCV/ZCls).  More functions are being added.

## Installation

See [Install](https://zcls2.readthedocs.io/en/latest/install/)

## Usage

See [Get started](https://zcls2.readthedocs.io/en/latest/get-started/)

## Maintainers

* zhujian - *Initial work* - [zjykzj](https://github.com/zjykzj)

## Thanks

* [NVIDIA/apex](https://github.com/NVIDIA/apex/tree/master/examples/imagenet)
* [ZJCV/ZCls](https://github.com/ZJCV/ZCls)

## Contributing

Anyone's participation is welcome! Open an [issue](https://github.com/ZJCV/ZCls2/issues) or submit PRs.

Small note:

* Git submission specifications should be complied
  with [Conventional Commits](https://www.conventionalcommits.org/en/v1.0.0-beta.4/)
* If versioned, please conform to the [Semantic Versioning 2.0.0](https://semver.org) specification
* If editing the README, please conform to the [standard-readme](https://github.com/RichardLitt/standard-readme)
  specification.

## License

[Apache License 2.0](LICENSE) © 2022 zjykzj