The official repository for Distribution-aware Knowledge Prototyping for Non-exemplar Lifelong Person Re-identification.
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
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
└──..
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`
The following results were obtained with NVIDIA 4090 GPU:
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.
Our code is based on the PyTorch implementation of PatchKD and PTKP.
For any questions, feel free to contact us (xkl@stu.pku.edu.cn).
Welcome to our Laboratory Homepage and OV3 Lab for more information about our papers, source codes, and datasets.