offical implement of our Lifelong Person Re-Identification via Adaptive Knowledge Accumulation in CVPR2021

Last update: Dec 8, 2022

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Overview

LifelongReID

Offical implementation of our Lifelong Person Re-Identification via Adaptive Knowledge Accumulation in CVPR2021 by Nan Pu, Wei Chen, Yu Liu, Erwin M. Bakker and Michael S. Lew.

We provide a lifelong person reid toolbox lreid in this repo.

More details please see our paper.

Citation

@InProceedings{pu_cvpr2021,
author = {Pu, Nan and Chen, Wei and Liu, Yu and Bakker, Erwin M. and Lew, Michael S.},
title = {Lifelong Person Re-Identification via Adaptive Knowledge Accumulation},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2021}
}

Install

Enviornment

conda create -n lreid python=3.7
conda activate lreid
conda install pytorch==1.4.0 torchvision==0.5.0 cudatoolkit=10.0 -c pytorch
conda install opencv
pip install Cython sklearn numpy prettytable easydict tqdm matplotlib

For visualization, you might need to install visdom:

pip install visdom

If you want to use fp16, please follow https://github.com/NVIDIA/apex to install apex, which is just a optional pakage. The following codes work in our enviroment, but it could not work on other enviroment.

git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

lreid toolbox

Then, you could clone our project and install lreid

git clone https://github.com/TPCD/LifelongReID
cd LifelongReID
python setup.py develop

Dataset prepration

Please follow Torchreid_Dataset_Doc to download datasets and unzip them to your data path (we refer to 'machine_dataset_path' in train_test.py). Alternatively, you could download some of unseen-domain datasets in DualNorm.

Train & Test

python train_test.py

Acknowledgement

The code is based on the PyTorch implementation of the Torchreid and Person_reID_baseline_pytorch.

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Comments

Unsatisfactory Results on CUHK, DUKE and MSMT17

Hi Nan, Thanks for sharing code. I managed to reproduce the results on all SEEN domains, but found, except for Market1501, there are great differences camparing with the results reported in the paper. So, how can I reproduce the results on CUHK, DUKE and MSMT17? Do I need to modify the dataloader or something?

opened by hh23333 0
Results on CUHK-SYSU and MSMT17

Thanks for your code. I managed to reproduce the results for Market, Duke and CUHK03. However, I tried to add CUHK-SYSU and MSMT17 into argument --test_dataset, which could not give results mentioned in the paper. The test_mode argument seems to be not working in test_p_s.py, while test_continual_operation_neck.py only support market/duke/mix. Could you please share more details on how to test CUHK-SYSU and MSMT17 datasets?

opened by chenhao2345 13
What about the change of results in Market during adding new domains?

Thank you for your sharing! In the paper, you give the final performance of Market, but I am curious about the performance change of Market during training, i.e., will the performance on Market decrease after the training on SYSU?

opened by pansanity666 3

Owner

PeterPu

Growth is like training a deep neural network, which needs supervision and finetunes.

GitHub

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