Object Classification Code Base
Project description
«ZCls» is a classification model benchmark code base
Supported Recognizers:
Refer to roadmap for details
Table of Contents
Background
In order to further improve the algorithm performance, it is usually necessary to improve the existing model, which inevitably involves code refactoring. Creating this repo, on the one hand, serves as the CodeBase of the new model/optimization method, on the other hand, it also records the comparison between the custom model and the existing implementation (such as Torchvision Models)
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
Maintainers
- zhujian - Initial work - zjykzj
Thanks
@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{ding2019acnet,
title={ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks},
author={Xiaohan Ding and Yuchen Guo and Guiguang Ding and Jungong Han},
year={2019},
eprint={1908.03930},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@misc{howard2019searching,
title={Searching for MobileNetV3},
author={Andrew Howard and Mark Sandler and Grace Chu and Liang-Chieh Chen and Bo Chen and Mingxing Tan and Weijun Wang and Yukun Zhu and Ruoming Pang and Vijay Vasudevan and Quoc V. Le and Hartwig Adam},
year={2019},
eprint={1905.02244},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@misc{cao2019gcnet,
title={GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond},
author={Yue Cao and Jiarui Xu and Stephen Lin and Fangyun Wei and Han Hu},
year={2019},
eprint={1904.11492},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
For more thanks, check THANKS
Contributing
Anyone's participation is welcome! Open an issue or submit PRs.
Small note:
- Git submission specifications should be complied with Conventional Commits
- If versioned, please conform to the Semantic Versioning 2.0.0 specification
- If editing the README, please conform to the standard-readme specification.
License
Apache License 2.0 © 2020 zjykzj
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file zcls-0.4.1.tar.gz
.
File metadata
- Download URL: zcls-0.4.1.tar.gz
- Upload date:
- Size: 68.8 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.4.0 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.7.4
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 92692d83ade7119851ed46d40b53a94968b4a06fe4947e3b0d4eb973c78e45ef |
|
MD5 | bcb5d90181f7d9f7db92d1d36950a560 |
|
BLAKE2b-256 | 9b39e3f8fe0578594effda513adb7c7bdb2f9878973a1ba26ae8f8cfd7c9da38 |
File details
Details for the file zcls-0.4.1-py2.py3-none-any.whl
.
File metadata
- Download URL: zcls-0.4.1-py2.py3-none-any.whl
- Upload date:
- Size: 139.8 kB
- Tags: Python 2, Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.4.0 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.7.4
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 6086549ff1f3b47bef40b9147bf983222532b2427af657cbedc3037155c4a6f0 |
|
MD5 | ec328955166c4e0e792c97eb4de4f896 |
|
BLAKE2b-256 | 9e16e989efe64ac1035363d9d9404d18b1f1a8816bec4dba286344041e8cc3ca |