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TinyNAS

  • This repository is a collection of training-free neural architecture search methods developed by TinyML team, Data Analytics and Intelligence Lab, Alibaba DAMO Academy. Researchers and developers can use this toolbox to design their neural architectures with different budgets on CPU devices within 30 minutes.

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Features

It manages these modules with the help of ModelScope Registry and Configuration mechanism.

  • The Searcher is defined to be responsible for building and completing the entire search process. Through the combination of these modules and the corresponding configuration files, we can complete backbone search for different tasks (such as classification, detection, etc.) under different budget constraints (such as the number of parameters, FLOPs, delay, etc.).

  • Currently supported tasks: For each task, we provide several sample configurations and scripts as follows to help you get started quickly.


Installation


How to Use


Results

Results for Classification(Details

Backbone Param (MB) FLOPs (G) ImageNet TOP1 Structure Download
DeepMAD-R18 11.69 1.82 77.7% txt model
DeepMAD-R34 21.80 3.68 79.7% txt model
DeepMAD-R50 25.55 4.13 80.6% txt model
DeepMAD-29M-224 29 4.5 82.5% txt model
DeepMAD-29M-288 29 4.5 82.8% txt model
DeepMAD-50M 50 8.7 83.9% txt model
DeepMAD-89M 89 15.4 84.0% txt model
Zen-NAS-R18-like 10.8 1.7 78.44 txt model
Zen-NAS-R50-like 21.3 3.6 80.04 txt model
Zen-NAS-R152-like 53.5 10.5 81.59 txt model

The official code for Zen-NAS was originally released at https://github.com/idstcv/ZenNAS.


Results for low-precision backbones(Details

Backbone Param (MB) BitOps (G) ImageNet TOP1 Structure Download
MBV2-8bit 3.4 19.2 71.90% - -
MBV2-4bit 2.3 7 68.90% - -
Mixed19d2G 3.2 18.8 74.80% txt model
Mixed7d0G 2.2 6.9 70.80% txt model

Results for Object Detection(Details

Backbone Param (M) FLOPs (G) box APval box APS box APM box APL Structure Download
ResNet-50 23.5 83.6 44.7 29.1 48.1 56.6 - -
ResNet-101 42.4 159.5 46.3 29.9 50.1 58.7 - -
MAE-DET-S 21.2 48.7 45.1 27.9 49.1 58.0 txt model
MAE-DET-M 25.8 89.9 46.9 30.1 50.9 59.9 txt model
MAE-DET-L 43.9 152.9 47.8 30.3 51.9 61.1 txt model

Results for Action Recognition (Details

Backbone size FLOPs (G) SSV1 Top-1 SSV1 Top-5 Structure
X3D-S 160 1.9 44.6 74.4 -
X3D-S 224 1.9 47.3 76.6 -
E3D-S 160 1.9 47.1 75.6 txt
E3D-M 224 4.7 49.4 78.1 txt
E3D-L 312 18.3 51.1 78.7 txt

Note: If you find this useful, please support us by citing them.

@inproceedings{cvpr2023deepmad,
	title = {DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural Network},
	author = {Xuan Shen and Yaohua Wang and Ming Lin and Yilun Huang and Hao Tang and Xiuyu Sun and Yanzhi Wang},
	booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
	year = {2023},
	url = {https://arxiv.org/abs/2303.02165}
}

@inproceedings{icml23prenas,
	title={PreNAS: Preferred One-Shot Learning Towards Efficient Neural Architecture Search},
	author={Haibin Wang and Ce Ge and Hesen Chen and Xiuyu Sun},
	booktitle={International Conference on Machine Learning},
	year={2023},
	organization={PMLR}
}

@inproceedings{iclr23maxste,
	title     = {Maximizing Spatio-Temporal Entropy of Deep 3D CNNs for Efficient Video Recognition},
	author    = {Junyan Wang and Zhenhong Sun and Yichen Qian and Dong Gong and Xiuyu Sun and Ming Lin and Maurice Pagnucco and Yang Song },
	journal   = {International Conference on Learning Representations},
	year      = {2023},
}

@inproceedings{neurips23qescore,
	title     = {Entropy-Driven Mixed-Precision Quantization for Deep Network Design},
	author    = {Zhenhong Sun and Ce Ge and Junyan Wang and Ming Lin and Hesen Chen and Hao Li and Xiuyu Sun},
	journal   = {Advances in Neural Information Processing Systems},
	year      = {2022},
}

@inproceedings{icml22maedet,
	title={MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detection},
	author={Zhenhong Sun and Ming Lin and Xiuyu Sun and Zhiyu Tan and Hao Li and Rong Jin},
	booktitle={International Conference on Machine Learning},
	year={2022},
	organization={PMLR}
}

@inproceedings{iccv21zennas,
	title     = {Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition},
	author    = {Ming Lin and Pichao Wang and Zhenhong Sun and Hesen Chen and Xiuyu Sun and Qi Qian and Hao Li and Rong Jin},
	booktitle = {2021 IEEE/CVF International Conference on Computer Vision},
	year      = {2021},
}

License

This project is developed by Alibaba and licensed under the Apache 2.0 license.

This product contains third-party components under other open source licenses.

See the NOTICE file for more information.