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.shDownload the imagenet pretrained model of bninception and put it in the folder: ~/.torch/models/.
Installation
pip install -r requirements.txt
python setup.py develop buildTrain and Test on CUB200-2011 with MS-Loss
./scripts/run_cub.shTrained 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].