============= This code has the source code for the paper "Re-ranking Person Re-identification with k-reciprocal Encoding".
If you find this code useful in your research, please consider citing:
@article{zhong2017re,
title={Re-ranking Person Re-identification with k-reciprocal Encoding},
author={Zhong, Zhun and Zheng, Liang and Cao, Donglin and Li, Shaozi},
booktitle={CVPR},
year={2017}
}
Requirements for Caffe and matcaffe (see: Caffe installation instructions)
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Build Caffe and matcaffe
cd $Re-ranking_ROOT/caffe # Now follow the Caffe installation instructions here: # http://caffe.berkeleyvision.org/installation.html make -j8 && make matcaffe
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Download pre-computed imagenet models, Market-1501 dataset and CUHK03 dataset
Please download the pre-trained imagenet models and put it in the "data/imagenet_models" folder.
Please download Market-1501 dataset and unzip it in the "evaluation/data/Market-1501" folder.
Please download CUHK03 dataset and unzip it in the "evaluation/data/CUHK03" folder.The new training/testing protocol split for CUHK03 in our paper is in the "evaluation/data/CUHK03/" folder.
- cuhk03_new_protocol_config_detected.mat
- cuhk03_new_protocol_config_labeled.mat
- Training
cd $Re-ranking_ROOT
# train IDE ResNet_50 for Market-1501
./experiments/Market-1501/train_IDE_ResNet_50.sh
# train IDE ResNet_50 for CUHK03
./experiments/CUHK03/train_IDE_ResNet_50_labeled.sh
./experiments/CUHK03/train_IDE_ResNet_50_detected.sh- Feature Extraction
cd $Re-ranking_ROOT/evaluation
# extract feature for Market-1501
matlab Market_1501_extract_feature.m
# extract feature for CUHK03
matlab CUHK03_extract_feature.m- Evaluation with our re-ranking method
# evaluation for Market-1501
matlab Market_1501_evaluation.m
# evaluation for CUHK03
matlab CUHK03_evaluation.mYou can download our pre-trained IDE models and IDE features, and put them in the "out_put" and "evaluation/feat" folder, respectively.
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IDE models [Baiduyun] [Google drive]
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IDE features [Baiduyun] [Google drive]
Using the above IDE models and IDE features, you can reproduce the results with our re-ranking method as follows:
- Market-1501
| Methods | Rank@1 | mAP |
|---|---|---|
| IDE_ResNet_50 + Euclidean | 78.92% | 55.03% |
| IDE_ResNet_50 + Euclidean + re-ranking | 81.44% | 70.39% |
| IDE_ResNet_50 + XQDA | 77.58% | 56.06% |
| IDE_ResNet_50 + XQDA + re-ranking | 80.70% | 69.98% |
For Market-1501, these results are better than those reported in our paper, since we add a dropout = 0.5 layer after pool5.
- CUHK03
| Labeled | Labeled | detected | detected | |
|---|---|---|---|---|
| Methods | Rank@1 | mAP | Rank@1 | mAP |
| IDE_CaffeNet + Euclidean | 15.6% | 14.9% | 15.1% | 14.2% |
| IDE_CaffeNet + Euclidean + re-ranking | 19.1% | 21.3% | 19.3% | 20.6% |
| IDE_CaffeNet + XQDA | 21.9% | 20.0% | 21.1% | 19.0% |
| IDE_CaffeNet + XQDA + re-ranking | 25.9% | 27.8% | 26.4% | 26.9% |
| IDE_ResNet_50 + Euclidean | 22.2% | 21.0% | 21.3% | 19.7% |
| IDE_ResNet_50 + Euclidean + re-ranking | 26.6% | 28.9% | 24.9% | 27.3% |
| IDE_ResNet_50 + XQDA | 32.0% | 29.6% | 31.1% | 28.2% |
| IDE_ResNet_50 + XQDA + re-ranking | 38.1% | 40.3% | 34.7% | 37.4% |
If you have any questions about this code, please do not hesitate to contact us.