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re_identification

README.md

Unsupervised Domain Adaptation for Person Re-Identification

Installation

It’s suggested to use pytorch==1.7.1 and torchvision==0.8.2 in order to reproduce the benchmark results.

Example scripts support all models in PyTorch-Image-Models. You also need to install timm to use PyTorch-Image-Models.

pip install timm

Dataset

Following datasets can be downloaded automatically:

Supported Methods

Supported methods include:

Usage

The shell files give the script to reproduce the benchmark with specified hyper-parameters. For example, if you want to train MMT on Market1501 -> DukeMTMC task, use the following script

# Train MMT on Market1501 -> DukeMTMC task using ResNet 50.
# Assume you have put the datasets under the path `data/market1501` and `data/dukemtmc`, 
# or you are glad to download the datasets automatically from the Internet to this path

# MMT involves two training steps:
# step1: pretrain
CUDA_VISIBLE_DEVICES=0 python baseline.py data -s Market1501 -t DukeMTMC -a reid_resnet50 \
--iters-per-epoch 800 --print-freq 80 --finetune --seed 0 --log logs/baseline/Market2DukeSeed0
CUDA_VISIBLE_DEVICES=0 python baseline.py data -s Market1501 -t DukeMTMC -a reid_resnet50 \
--iters-per-epoch 800 --print-freq 80 --finetune --seed 1 --log logs/baseline/Market2DukeSeed1

# step2: train mmt
CUDA_VISIBLE_DEVICES=0,1,2,3 python mmt.py data -t DukeMTMC -a reid_resnet50 \
--pretrained-model-1-path logs/baseline/Market2DukeSeed0/checkpoints/best.pth \
--pretrained-model-2-path logs/baseline/Market2DukeSeed1/checkpoints/best.pth \
--finetune --seed 0 --log logs/mmt/Market2Duke

Experiment and Results

In our experiments, we adopt modified resnet architecture from MMT. For a fair comparison, we use standard cross entropy loss and triplet loss in all methods. For methods that utilize clustering algorithms, we adopt kmeans or DBSCAN and report both results.

Notations

  • Avg means the mAP (mean average precision) reported by TLlib.
  • Baseline_Cluster represents the strong baseline in MMT.

Cross dataset mAP on ResNet-50

Methods Avg Market2Duke Duke2Market Market2MSMT MSMT2Market Duke2MSMT MSMT2Duke
Baseline 27.1 32.4 31.4 8.2 36.7 11.0 43.1
IBN 30.0 35.2 36.5 11.3 38.7 14.1 44.3
SPGAN 30.7 34.4 35.4 14.1 40.2 16.1 43.8
Baseline_Cluster(kmeans) 45.1 52.8 59.5 19.0 62.6 20.3 56.2
Baseline_Cluster(dbscan) 54.9 62.5 73.5 25.2 77.9 25.3 65.0
MMT(kmeans) 55.4 63.7 72.5 26.2 75.8 28.0 66.1
MMT(dbscan) 60.0 68.2 80.0 28.2 82.5 31.2 70.0

Citation

If you use these methods in your research, please consider citing.

@inproceedings{IBN-Net,  
    author = {Xingang Pan, Ping Luo, Jianping Shi, and Xiaoou Tang},  
    title = {Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net},  
    booktitle = {ECCV},  
    year = {2018}  
}

@inproceedings{SPGAN,
    title={Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification},
    author={Deng, Weijian and Zheng, Liang and Ye, Qixiang and Kang, Guoliang and Yang, Yi and Jiao, Jianbin},
    booktitle={CVPR},
    year={2018}
}

@inproceedings{MMT,
    title={Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification},
    author={Yixiao Ge and Dapeng Chen and Hongsheng Li},
    booktitle={ICLR},
    year={2020},
}