[BMVC 2021] The official implementation of "DomainMix: Learning Generalizable Person Re-Identification Without Human Annotations"

[paper] [demo] [Chinese blog]

DomainMix works fine on both PaddlePaddle and PyTorch.

Framework:

• Python 3.7
• Pytorch 1.7.0
• sklearn 0.23.2
• PIL 5.4.1
• Numpy 1.19.4
• Torchvision 0.8.1

• Test our models: 1 Tesla V100 GPU.
• Train new models: 4 Telsa V100 GPUs.
• Note that the required for GPU is not very strict, and 6G memory per GPU is minimum.

1. Dataset

We evaluate our algorithm on RandPerson, Market-1501, CUHK03-NP and MSMT17. You should download them by yourselves and prepare the directory structure like this:

*DATA_PATH
      *data
         *randperson_subset
             *randperson_subset
                 ...
         *market1501
             *Market-1501-v15.09.15
                 *bounding_box_test
                 ...
         *cuhk03_np
             *detected
             *labeled
         *msmt17
             *MSMT17_V1
                 *test
                 *train
                 ...

2. Pretrained Models

We use ResNet-50 and IBN-ResNet-50 as backbones. The pretrained models for ResNet-50 will be downloaded automatically. When training with the backbone of IBN-ResNet-50, you should download the pretrained models from here, and save it like this:

*DATA_PATH
      *logs
         *pretrained
             resnet50_ibn_a.pth.tar

3. Our Trained Models

We provide our trained models as follows. They should be saved in ./logs/trained

Market1501:

DomainMix(43.5% mAP) DomainMix-IBN(45.7% mAP)

CUHK03-NP:

DomainMix(16.7% mAP) DomainMix-IBN(18.3% mAP)

MSMT17:

DomainMix(9.3% mAP) DomainMix-IBN(12.1% mAP)

We use RandPerson+MSMT->Market as an example, other DG tasks will follow similar pipelines.

CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py \
-dsy randperson_subset -dre msmt17 -dun market1501 \
-a resnet50 --margin 0.0 --num-instances 4 -b 64 -j 4 --warmup-step 5 \
--lr 0.00035 --milestones 10 15 30 40 50 --iters 2000 \
--epochs 60 --eval-step 1 --logs-dir logs/randperson_subsetmsTOm/domainmix

We use RandPerson+MSMT->Market as an example, other DG tasks will follow similar pipelines.

CUDA_VISIBLE_DEVICES=0 python test.py -b 256 -j 8 --dataset-target market1501 -a resnet50 \
--resume logs/trained/model_best_435.pth.tar

Some parts of our code are from MMT and SpCL. Thanks Yixiao Ge for her contribution.

If you find this code useful for your research, please cite our paper

@inproceedings{wang2021domainmix,
  title={DomainMix: Learning Generalizable Person Re-Identification Without Human Annotations},
  author={Wenhao Wang and Shengcai Liao and Fang Zhao and Kangkang Cui and Ling Shao},
  booktitle={British Machine Vision Conference},
  year={2021}
}