Code for the CVPR 2020 paper (accepted as Oral) Cross-Batch Memory for Embedding Learning
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Great Improvement: XBM can improve R@1 by 12~25% on three large-scale datasets
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Memory Efficient: with less than 1GB for large-scale datasets
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Elegant Algorithm: with an implementation that can be achieved in only several lines
pip install -r requirements.txt
python setup.py develop buildCUDA_VISIBLE_DEVICES=0 python3 tools/train_net.py --cfg configs/sample_config.yaml For any questions, please feel free to reach
If you use this method or this code in your research, please cite as:
@inproceedings{wang2020xbm,
title={Cross-Batch Memory for Embedding Learning},
author={Wang, Xun and Zhang, Haozhi and Huang, Weilin and Scott, Matthew R},
booktitle={CVPR},
year={2020}
}
@inproceedings{wang2019multi,
title={Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning},
author={Wang, Xun and Han, Xintong and Huang, Weilin and Dong, Dengke and Scott, Matthew R},
booktitle={CVPR},
year={2019}
}
XBM is CC-BY-NC 4.0 licensed, as found in the LICENSE file. It is released for academic research / non-commercial use only. If you wish to use for commercial purposes, please contact [email protected].
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