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All Projects → AutoML-Research → AutoSTR

AutoML-Research / AutoSTR

Licence: other
H. Zhang, Q. Yao, M. Yang, Y. Xu, X. Bai. AutoSTR: Efficient Backbone Search for Scene Text Recognition. European Conference on Computer Vision (ECCV). 2020.

Programming Languages

python
139335 projects - #7 most used programming language

Labels

neural-architecture-searchscene-text-recognition

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AutoSTR: Efficient Backbone Search for Scene Text Recognition

We investigate how to obtain a strong feature sequence extractor for scene text recognition task by neural architecture search technology. The research paper can be found here ECCV. 2020.

overview

Requirements

python==3.6.7
pytorch==1.4.0
torchvision==0.2.1
lmdb
PyYAML
pillow
editdistance
...

Searching Network Architecture

python3 arch_search_exp.py --config_file configs/search.yaml

Retraining Compact Structure

python3 main.py --config_file configs/retrain.yaml

logs and checkpoints

The logs and checkpoints can be found in here with extraction code wp8w.

Citation

If you find this work helpful for your research, please cite the following paper:

@inproceedings{zhang2020efficient,
  title={AutoSTR: Efficient Backbone Search for Scene Text Recognition},
  author={Zhang, Hui and Yao, Quanming and Yang, Mingkun and Xu, Yongchao and Bai, Xiang},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2020}
}

@TechReport{yao2018taking,
  author      = {Yao, Quanming and Wang, Mengshuo},
  institution = {arXiv preprint},
  title       = {Taking Human out of Learning Applications: A Survey on Automated Machine Learning},
  year        = {2018},
}

Acknowledgement

We used the code part from aster.pytorch (https://github.com/ayumiymk/aster.pytorch) and proxylessnas(https://github.com/mit-han-lab/proxylessnas). Thanks for their excellent work very much.

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