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License: CC BY-NC 4.0

Multi-Similarity Loss for Deep Metric Learning (MS-Loss)

Code for the CVPR 2019 paper Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning

Performance compared with SOTA methods on CUB-200-2011

Rank@K 1 2 4 8 16 32
Clustering64 48.2 61.4 71.8 81.9 - -
ProxyNCA64 49.2 61.9 67.9 72.4 - -
Smart Mining64 49.8 62.3 74.1 83.3 -
Our MS-Loss64 57.4 69.8 80.0 87.8 93.2 96.4
HTL512 57.1 68.8 78.7 86.5 92.5 95.5
ABIER512 57.5 68.7 78.3 86.2 91.9 95.5
Our MS-Loss512 65.7 77.0 86.3 91.2 95.0 97.3

Prepare the data and the pretrained model

The following script will prepare the CUB dataset for training by downloading to the ./resource/datasets/ folder; which will then build the data list (train.txt test.txt):

./scripts/prepare_cub.sh

Download the imagenet pretrained model of bninception and put it in the folder: ~/.torch/models/.

Installation

pip install -r requirements.txt
python setup.py develop build

Train and Test on CUB200-2011 with MS-Loss

./scripts/run_cub.sh

Trained models will be saved in the ./output/ folder if using the default config.

Best recall@1 higher than 66 (65.7 in the paper).

Contact

For any questions, please feel free to reach

Citation

If you use this method or this code in your research, please cite as:

@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={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={5022--5030},
year={2019}
}

License

MS-Loss 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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MS-Loss: Multi-Similarity Loss for Deep Metric Learning

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