--------- [](LICENSE.md) 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。

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 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](https://drive.google.com/open?id=1ANmiYEGX-IQZTfH8w0aSpj-Wypg-0DR-) (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, @article{dong2020nats, title={NATS-Bench: Benchmarking NAS algorithms for Architecture Topology and Size}, author={Dong, Xuanyi and Liu, Lu and Musial, Katarzyna and Gabrys, Bogdan}, journal={arXiv preprint arXiv:2009.00437}, year={2020} } 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](.github/CONTRIBUTING.md)。 此外，使用规范请参考[CODE-OF-CONDUCT.md](.github/CODE-OF-CONDUCT.md)。 # 许可证 The entire codebase is under [MIT license](LICENSE.md)