autodl-projects/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。 台项目目前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。

<tr> <!-- (6-th row) -->
<td align="center" valign="middle"> ... </td>
<td align="center" valign="middle"> ENAS / REA / REINFORCE / BOHB </td>
<td align="center" valign="middle"> Please check the original papers. </td>
<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/master/docs/NAS-Bench-201.md">NAS-Bench-201.md</a> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/master/docs/NATS-Bench.md">NATS-Bench.md</a> </td>
</tr>
<tr> <!-- (start second block) -->
<td rowspan="1" align="center" valign="middle" halign="middle"> HPO </td>
<td align="center" valign="middle"> HPO-CG </td>
<td align="center" valign="middle"> Hyperparameter optimization with approximate gradient </td>
<td align="center" valign="middle"> coming soon </a> </td>
</tr>
<tr> <!-- (start third block) -->
<td rowspan="1" align="center" valign="middle" halign="middle"> Basic </td>
<td align="center" valign="middle"> ResNet </td>
<td align="center" valign="middle"> Deep Learning-based Image Classification </td>
<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/master/docs/BASELINE.md">BASELINE.md</a> </a> </td>
</tr>

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

准备工作

Please install Python>=3.6 and PyTorch>=1.3.0. (You could also run this project in lower versions of Python and PyTorch, but may have bugs). Some visualization codes may require opencv.

CIFAR and ImageNet should be downloaded and extracted into $TORCH_HOME. Some methods use knowledge distillation (KD), which require pre-trained models. Please download these models from Google Drive (or train by yourself) and save into .latent-data.

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{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}
}
@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