IDM: An Intermediate Domain Module for Domain Adaptive Person Re-ID,

Related tags

Deep LearningIDM
Overview

Python >=3.7 PyTorch >=1.1

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 resnet50 for the backbone of ResNet-50, and -a resnet_ibn50a for the backbone of IBN-ResNet.
  • For IDM, use -a resnet50_idm to insert IDM into the backbone of ResNet-50, and -a resnet_ibn50a_idm to insert IDM into the backbone of IBN-ResNet.
  • For strong baseline, use --use-xbm to 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 --dsbn for domain adaptive models, and add --test-source if you want to test on the source domain;
  • use -a resnet50 for the backbone of ResNet-50, and -a resnet_ibn50a for the backbone of IBN-ResNet.
  • use -a resnet50_idm for ResNet-50 + IDM, and -a resnet_ibn50a_idm for 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]

Owner
Yongxing Dai
I am now a fourth-year PhD student at National Engineering Lab for Video Technology in Peking University, Beijing, China
Yongxing Dai
GitHub Repository
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