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Code for IDE baseline on Market-1501

============= This code was used for experiments with ID-discriminative Embedding (IDE) for Market-1501 dataset.

Thanks Liboyue, give us suggestions for improvement.

If you find this code useful in your research, please consider citing:

@article{zheng2016person,
title={Person Re-identification: Past, Present and Future},
author={Zheng, Liang and Yang, Yi and Hauptmann, Alexander G},
journal={arXiv preprint arXiv:1610.02984},
year={2016}
}

@inproceedings{zheng2015scalable,
title={Scalable Person Re-identification: A Benchmark},
author={Zheng, Liang and Shen, Liyue and Tian, Lu and Wang, Shengjin and Wang, Jingdong and Tian, Qi},
booktitle={Computer Vision, IEEE International Conference on},
year={2015}
}

Requirements: Caffe

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

Installation

Clone the IDE repository

# Make sure to clone with --recursive
git clone --recursive https://github.com/zhunzhong07/IDE-baseline-Market-1501

Build Caffe and matcaffe

cd $IDE_ROOT/caffe
# Now follow the Caffe installation instructions here:
# http://caffe.berkeleyvision.org/installation.html
make -j8 && make matcaffe

Download pre-computed models and Market-1501 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 "market_evaluation/dataset" folder.

Training and testing IDE model

Training

cd $IDE_ROOT
 # train IDE on CaffeNet
./experiments/market/train_IDE_CaffeNet.sh  
# train IDE ResNet_50
./experiments/market/train_IDE_ResNet_50.sh
# The IDE models are saved under: "out/market_train"
# If you encounter this problem: bash: ./experiments/market/train_IDE_CaffeNet.sh: Permission denied
# Please execute: chmod 777 -R experiments/

Feature Extraction

cd $IDE_ROOT/market_evaluation
Run Matlab: extract_feature.m
# The IDE features are saved under: "market_evaluation/feat"

Evaluation

  Run Matlab: baseline_evaluation_IDE.m

Results

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

Using the models and features above, you can reproduce the results as follows:

Methods	Rank@1	mAP
IDE_CaffeNet + Euclidean	59.53%	32.85%
IDE_CaffeNet + XQDA	62.00%	37.55%
IDE_CaffeNet + KISSME	61.02%	36.72%
IDE_ResNet_50 + Euclidean	75.62%	50.68%
IDE_ResNet_50 + XQDA	76.01%	52.98%
IDE_ResNet_50 + KISSME	77.52%	53.88%

If you add a dropout = 0.5 layer after pool5, you will get a better performance for ResNet_50:

Methods	Rank@1	mAP
IDE_ResNet_50 + dropout(0.5) + Euclidean	78.92%	55.03%
IDE_ResNet_50 + dropout(0.5) + XQDA	77.35%	56.01%
IDE_ResNet_50 + dropout(0.5) + KISSME	78.80%	56.13%

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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