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

================================================================

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}
}

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The new training/testing protocol for CUHK03

The new protocol splits the CUHK03 dataset into training set and testing set, which consist of 767 identities and 700 identities respectively. In testing, we randomly select one image from each camera as the query for each identity and use the rest of images to construct the gallery set.

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

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IDE Baseline

Requirements: Caffe

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

Installation

  1. 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

  2. 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.

Training and testing IDE model

  1. 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
  1. 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
  1. Evaluation
# 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.

Using the above IDE models and IDE features, you can reproduce the results 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