IDM has been extended to IDM++. IDM++ is a strong cross-domain person re-ID method, which achieves new state of the art under both the unsupervised domain adaptation (UDA) and domain generalization (DG) re-ID scenarios. The code will be updated.
If you find our work is useful for your research, please kindly cite our paper
@inproceedings{dai2021idm,
title={IDM: An Intermediate Domain Module for Domain Adaptive Person Re-ID},
author={Dai, Yongxing and Liu, Jun and Sun, Yifan and Tong, Zekun and Zhang, Chi and Duan, Ling-Yu},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
year={2021}
}
@article{dai2022bridging,
title={Bridging the Source-to-target Gap for Cross-domain Person Re-Identification with Intermediate Domains},
author={Dai, Yongxing and Sun, Yifan and Liu, Jun and Tong, Zekun and Yang, Yi and Duan, Ling-Yu},
journal={arXiv preprint arXiv:2203.01682},
year={2022}
}
If you have any questions, please leave an issue or contact me: [email protected]
This repository is the official implementation for IDM: An Intermediate Domain Module for Domain Adaptive Person Re-ID, which is accepted by ICCV 2021 (Oral).
IDM achieves state-of-the-art performances on the unsupervised domain adaptation task for person re-ID.
git clone https://github.com/SikaStar/IDM.git
cd IDM/idm/evaluation_metrics/rank_cylib && make allcd examples && mkdir dataDownload the person re-ID datasets Market-1501, DukeMTMC-ReID, MSMT17, PersonX, and UnrealPerson. Then unzip them under the directory like
IDM/examples/data
├── dukemtmc
│ └── DukeMTMC-reID
├── market1501
│ └── Market-1501-v15.09.15
├── msmt17
│ └── MSMT17_V1
├── personx
│ └── PersonX
└── unreal
├── list_unreal_train.txt
└── unreal_vX.Y
When training with the backbone of IBN-ResNet, you need to download the ImageNet-pretrained model from this link and save it under the path of logs/pretrained/.
mkdir logs && cd logs
mkdir pretrainedThe file tree should be
IDM/logs
└── pretrained
└── resnet50_ibn_a.pth.tar
ImageNet-pretrained models for ResNet-50 will be automatically downloaded in the python script.
We utilize 4 GTX-2080TI GPUs for training. Note that
- The source and target domains are trained jointly.
- For baseline methods, use
-a resnet50for the backbone of ResNet-50, and-a resnet_ibn50afor the backbone of IBN-ResNet. - For IDM, use
-a resnet50_idmto insert IDM into the backbone of ResNet-50, and-a resnet_ibn50a_idmto insert IDM into the backbone of IBN-ResNet. - For strong baseline, use
--use-xbmto implement XBM (a variant of Memory Bank).
To train the baseline methods in the paper, run commands like:
# Naive Baseline
CUDA_VISIBLE_DEVICES=0,1,2,3 sh scripts/run_naive_baseline.sh ${source} ${target} ${arch}
# Strong Baseline
CUDA_VISIBLE_DEVICES=0,1,2,3 sh scripts/run_strong_baseline.sh ${source} ${target} ${arch}Some examples:
### market1501 -> dukemtmc ###
# ResNet-50
CUDA_VISIBLE_DEVICES=0,1,2,3 sh scripts/run_strong_baseline.sh market1501 dukemtmc resnet50
# IBN-ResNet-50
CUDA_VISIBLE_DEVICES=0,1,2,3 sh scripts/run_strong_baseline.sh market1501 dukemtmc resnet_ibn50aTo train the models with our IDM, run commands like:
# Naive Baseline + IDM
CUDA_VISIBLE_DEVICES=0,1,2,3 \
sh scripts/run_idm.sh ${source} ${target} ${arch} ${stage} ${mu1} ${mu2} ${mu3}
# Strong Baseline + IDM
CUDA_VISIBLE_DEVICES=0,1,2,3 \
sh scripts/run_idm_xbm.sh ${source} ${target} ${arch} ${stage} ${mu1} ${mu2} ${mu3}- Defaults:
--stage 0 --mu1 0.7 --mu2 0.1 --mu3 1.0
Some examples:
### market1501 -> dukemtmc ###
# ResNet-50 + IDM
CUDA_VISIBLE_DEVICES=0,1,2,3 \
sh scripts/run_idm_xbm.sh market1501 dukemtmc resnet50_idm 0 0.7 0.1 1.0
# IBN-ResNet-50 + IDM
CUDA_VISIBLE_DEVICES=0,1,2,3 \
sh scripts/run_idm_xbm.sh market1501 dukemtmc resnet_ibn50a_idm 0 0.7 0.1 1.0We utilize 1 GTX-2080TI GPU for testing. Note that
- use
--dsbnfor domain adaptive models, and add--test-sourceif you want to test on the source domain; - use
-a resnet50for the backbone of ResNet-50, and-a resnet_ibn50afor the backbone of IBN-ResNet. - use
-a resnet50_idmfor ResNet-50 + IDM, and-a resnet_ibn50a_idmfor IBN-ResNet + IDM.
To evaluate the baseline model on the target-domain dataset, run:
CUDA_VISIBLE_DEVICES=0 \
python3 examples/test.py --dsbn -d ${dataset} -a ${arch} --resume ${resume} To evaluate the baseline model on the source-domain dataset, run:
CUDA_VISIBLE_DEVICES=0 \
python3 examples/test.py --dsbn --test-source -d ${dataset} -a ${arch} --resume ${resume} To evaluate the IDM model on the target-domain dataset, run:
CUDA_VISIBLE_DEVICES=0 \
python3 examples/test.py --dsbn-idm -d ${dataset} -a ${arch} --resume ${resume} --stage ${stage} To evaluate the IDM model on the source-domain dataset, run:
CUDA_VISIBLE_DEVICES=0 \
python3 examples/test.py --dsbn-idm --test-source -d ${dataset} -a ${arch} --resume ${resume} --stage ${stage} Some examples:
### market1501 -> dukemtmc ###
# evaluate the target domain "dukemtmc" on the strong baseline model
CUDA_VISIBLE_DEVICES=0 \
python3 examples/test.py --dsbn -d dukemtmc -a resnet50 \
--resume logs/resnet50_strong_baseline/market1501-TO-dukemtmc/model_best.pth.tar
# evaluate the source domain "market1501" on the strong baseline model
CUDA_VISIBLE_DEVICES=0 \
python3 examples/test.py --dsbn --test-source -d market1501 -a resnet50 \
--resume logs/resnet50_strong_baseline/market1501-TO-dukemtmc/model_best.pth.tar
# evaluate the target domain "dukemtmc" on the IDM model (after stage-0)
python3 examples/test.py --dsbn-idm -d dukemtmc -a resnet50_idm \
--resume logs/resnet50_idm_xbm/market1501-TO-dukemtmc/model_best.pth.tar --stage 0
# evaluate the target domain "dukemtmc" on the IDM model (after stage-0)
python3 examples/test.py --dsbn-idm --test-source -d market1501 -a resnet50_idm \
--resume logs/resnet50_idm_xbm/market1501-TO-dukemtmc/model_best.pth.tar --stage 0
Our code is based on MMT and SpCL. Thanks for Yixiao's wonderful works.
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