Re-ranking Person Re-identification with k-reciprocal Encoding

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This code has the IDE baseline for the Market-1501 and CUHK03 new training/testing protocol.

The re-ranking code is available upon request.

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: Caffe

Requirements for Caffe and matcaffe (see: Caffe installation instructions)

Installation

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

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 for CUHK03

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 and testing IDE model

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.m

Results

You can download our pre-trained IDE models and IDE features, and put them in the "out_put" and "evaluation/feat" folder, respectively.

IDE models [Baiduyun] [Google drive]
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 + XQDA	77.58%	56.06%

For Market-1501, these results are better than those reported in our paper, since we add a dropout = 0.5 layer after pool5.

CUHK03 under the new training/testing protocol

	Labeled	Labeled	detected	detected
Methods	Rank@1	mAP	Rank@1	mAP
IDE_CaffeNet + Euclidean	15.6%	14.9%	15.1%	14.2%
IDE_CaffeNet + XQDA	21.9%	20.0%	21.1%	19.0%
IDE_ResNet_50 + Euclidean	22.2%	21.0%	21.3%	19.7%
IDE_ResNet_50 + XQDA	32.0%	29.6%	31.1%	28.2%

Contact us

If you have any questions about this code, please do not hesitate to contact us.

Zhun Zhong

Liang Zheng

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