Code for the paper "Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization", ICCV 2019, Seoul.

[Paper] [Poster]

Contact: [email protected] or [email protected]

• Python 3.6+
• PyTorch 0.4+

• RAP: http://rap.idealtest.org/
• PETA: http://mmlab.ie.cuhk.edu.hk/projects/PETA.html
• PA-100K: https://github.com/xh-liu/HydraPlus-Net

The original datasets should be processed to match the DataLoader.

We provide the label lists for training and testing.

python main.py --approach=inception_iccv --experiment=rap

python main.py --approach=inception_iccv --experiment=rap -e --resume='model_path'

We provide the pretrained models for reference, the results may slightly different with the values reported in our paper.

If this work is useful to your research, please cite:

@inproceedings{tang2019improving,
  title={Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization},
  author={Tang, Chufeng and Sheng, Lu and Zhang, Zhaoxiang and Hu, Xiaolin},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={4997--5006},
  year={2019}
}