Intermediate Domain Module (IDM)
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.
Requirements
Installation
git clone https://github.com/SikaStar/IDM.git
cd IDM/idm/evaluation_metrics/rank_cylib && make all
Prepare Datasets
cd examples && mkdir data
Download 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
Prepare ImageNet Pre-trained Models for IBN-Net
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 pretrained
The 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.
Training
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).
Baseline Methods
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_ibn50a
Training with IDM
To 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 1.0
Evaluation
We 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
Acknowledgement
Our code is based on MMT and SpCL. Thanks for Yixiao's wonderful works.
Citation
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}
}
If you have any questions, please leave an issue or contact me: [email protected]
37 Dec 30, 2022
0 May 06, 2022
603 Dec 22, 2022
170.1k Jan 05, 2023
29 Dec 25, 2022
130 Dec 05, 2022
13 Jan 02, 2023
1 Jan 12, 2022
270 Dec 31, 2022
77 Dec 16, 2022
16 Jun 30, 2022
105 Dec 26, 2022
17 Aug 13, 2022
89 Dec 02, 2022
10 Oct 10, 2022
16 Dec 29, 2022
43 Dec 29, 2022
175 Dec 29, 2022
11 Nov 12, 2022
4.2k Jan 01, 2023