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[CVPR2024] Distribution-aware Knowledge Prototyping for Non-exemplar Lifelong Person Re-identification (DKP)

The official repository for Distribution-aware Knowledge Prototyping for Non-exemplar Lifelong Person Re-identification.

Framework

Installation

conda create -n IRL python=3.7
conda activate IRL
pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117
pip install -r requirement.txt

Prepare Datasets

Download the person re-identification datasets Market-1501, MSMT17, CUHK03, SenseReID. Other datasets can be prepared following Torchreid_Datasets_Doc and light-reid. Then unzip them and rename them under the directory like

PRID
├── CUHK01
│   └──..
├── CUHK02
│   └──..
├── CUHK03
│   └──..
├── CUHK-SYSU
│   └──..
├── DukeMTMC-reID
│   └──..
├── grid
│   └──..
├── i-LIDS_Pedestrain
│   └──..
├── MSMT17_V2
│   └──..
├── Market-1501
│   └──..
├── prid2011
│   └──..
├── SenseReID
│   └──..
└── viper
    └──..

Quick Start

Training + evaluation:

`python continual_train.py --data-dir path/to/PRID`
(for example, `python continual_train.py --data-dir ../DATA/PRID`)

Evaluation from checkpoint:

`python continual_train.py --data-dir path/to/PRID --test_folder /path/to/pretrained/folder --evaluate`

Results

The following results were obtained with NVIDIA 4090 GPU:

Results

Citation

If you find this code useful for your research, please cite our paper.

[1] Kunlun Xu, Xu Zou, Yuxin Peang, Jiahuan Zhou. Distribution-aware Knowledge Prototyping for Non-exemplar Lifelong Person Re-identification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.

Acknowledgement

Our code is based on the PyTorch implementation of PatchKD and PTKP.

Contact

For any questions, feel free to contact us (xkl@stu.pku.edu.cn).

Welcome to our Laboratory Homepage and OV<sup>3</sup> Lab for more information about our papers, source codes, and datasets.