xautodl/docs/README_CN.md

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自动深度学习库 (AutoDL-Projects) 是一个开源的，轻量级的，功能强大的项目。 该项目实现了多种网络结构搜索(NAS)和超参数优化(HPO)算法。

谁应该考虑使用AutoDL-Projects

• 想尝试不同AutoDL算法的初学者
• 想调研AutoDL在特定问题上的有效性的工程师
• 想轻松实现和实验新AutoDL算法的研究员

为什么我们要用AutoDL-Projects

• 最简化的python依赖库
• 所有算法都在一个代码库下
• 积极地维护

AutoDL-Projects 能力简述

目前，该项目提供了下列算法和以及对应的运行脚本。请点击每个算法对应的链接看他们的细节描述。

Type	ABBRV	Algorithms	Description
NAS	TAS	Network Pruning via Transformable Architecture Search	NeurIPS-2019-TAS.md
	DARTS	DARTS: Differentiable Architecture Search	ICLR-2019-DARTS.md
	GDAS	Searching for A Robust Neural Architecture in Four GPU Hours	CVPR-2019-GDAS.md
	SETN	One-Shot Neural Architecture Search via Self-Evaluated Template Network	ICCV-2019-SETN.md
	NAS-Bench-201	NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search	NAS-Bench-201.md
	NATS-Bench	NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size	NATS-Bench.md
...	ENAS / REA / REINFORCE / BOHB	Please check the original papers.	NAS-Bench-201.md NATS-Bench.md
HPO	HPO-CG	Hyperparameter optimization with approximate gradient	coming soon
Basic	ResNet	Deep Learning-based Image Classification	BASELINE.md

准备工作

请使用3.6以上的Python，更多的Python包参见requirements.txt.

请下载并且解压缩CIFAR和ImageNet到$TORCH_HOME.

引用

如果您发现该项目对您的科研或工程有帮助，请考虑引用下列的某些文献：

@inproceedings{dong2021autohas,
  title     = {{AutoHAS}: Efficient Hyperparameter and Architecture Search},
  author    = {Dong, Xuanyi and Tan, Mingxing and Yu, Adams Wei and Peng, Daiyi and Gabrys, Bogdan and Le, Quoc V},
  booktitle = {2nd Workshop on Neural Architecture Search at International Conference on Learning Representations (ICLR)},
  year      = {2021}
}
@article{dong2021nats,
  title   = {{NATS-Bench}: Benchmarking NAS Algorithms for Architecture Topology and Size},
  author  = {Dong, Xuanyi and Liu, Lu and Musial, Katarzyna and Gabrys, Bogdan},
  doi     = {10.1109/TPAMI.2021.3054824},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
  year    = {2021},
  note    = {\mbox{doi}:\url{10.1109/TPAMI.2021.3054824}}
}
@inproceedings{dong2020nasbench201,
  title     = {{NAS-Bench-201}: Extending the Scope of Reproducible Neural Architecture Search},
  author    = {Dong, Xuanyi and Yang, Yi},
  booktitle = {International Conference on Learning Representations (ICLR)},
  url       = {https://openreview.net/forum?id=HJxyZkBKDr},
  year      = {2020}
}
@inproceedings{dong2019tas,
  title     = {Network Pruning via Transformable Architecture Search},
  author    = {Dong, Xuanyi and Yang, Yi},
  booktitle = {Neural Information Processing Systems (NeurIPS)},
  year      = {2019}
  pages     = {760--771},
}
@inproceedings{dong2019one,
  title     = {One-Shot Neural Architecture Search via Self-Evaluated Template Network},
  author    = {Dong, Xuanyi and Yang, Yi},
  booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
  pages     = {3681--3690},
  year      = {2019}
}
@inproceedings{dong2019search,
  title     = {Searching for A Robust Neural Architecture in Four GPU Hours},
  author    = {Dong, Xuanyi and Yang, Yi},
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages     = {1761--1770},
  year      = {2019}
}

其他

如果你想要给这份代码库做贡献，请看CONTRIBUTING.md。 此外，使用规范请参考CODE-OF-CONDUCT.md。

许可证

The entire codebase is under MIT license