PFD：Pose-guided Feature Disentangling for Occluded Person Re-identification based on Transformer
This repo is the official implementation of "Pose-guided Feature Disentangling for Occluded Person Re-identification based on Transformer(PFD), Tao Wang, Hong Liu, Pinghao Song, Tianyu Guo& Wei Shi" in PyTorch.
Pipeline
Dependencies
-
timm==0.3.2
-
torch==1.6.0
-
numpy==1.20.2
-
yacs==0.1.8
-
opencv_python==4.5.2.54
-
torchvision==0.7.0
-
Pillow==8.4.0
Installation
pip install -r requirements.txt
If you find some packages are missing, please install them manually.
Prepare Datasets
mkdir data
Please download the dataset, and then rename and unzip them under the data
data
|--market1501
|
|--Occluded_Duke
|
|--Occluded_REID
|
|--MSMT17
|
|--dukemtmcreid
Prepare ViT Pre-trained and HRNet Pre-trained Models
mkdir data
The ViT Pre-trained model can be found in ViT_Base, The HRNet Pre-trained model can be found in HRNet, please download it and put in the './weights' dictory.
Training
We use One GeForce GTX 1080Ti GPU for Training Before train the model, please modify the parameters in config file, please refer to Arguments in TransReID
python occ_train.py --config_file {config_file path}
#example
python occ_train.py --config_file 'configs/OCC_Duke/skeleton_pfd.yml'
Test the model
First download the Occluded-Duke model:Occluded-Duke
To test on pretrained model on Occ-Duke: Modify the pre-trained model path (PRETRAIN_PATH:ViT_Base, POSE_WEIGHT:HRNet, WEIGHT:Occluded-Duke) in yml, and then run:
## OccDuke for example
python test.py --config_file 'configs/OCC_Duke/skeleton_pfd.yml'
Occluded-Duke Results
| Model | Image Size | Rank-1 | mAP |
|---|---|---|---|
| HOReID | 256*128 | 55.1 | 43.8 |
| PAT | 256*128 | 64.5 | 53.6 |
| TransReID | 256*128 | 64.2 | 55.7 |
| PFD | 256*128 | 67.7 | 60.1 |
| TransReID* | 256*128 | 66.4 | 59.2 |
| PFD* | 256*128 | 69.5 | 61.8 |
$*$means the encoder is with a small step sliding-window setting
Occluded-REID Results
| Model | Image Size | Rank-1 | mAP |
|---|---|---|---|
| HOReID | 256*128 | 80.3 | 70.2 |
| PAT | 256*128 | 81.6 | 72.1 |
| PFD | 256*128 | 79.8 | 81.3 |
Market-1501 Results
| Model | Image Size | Rank-1 | mAP |
|---|---|---|---|
| HOReID | 256*128 | 80.3 | 70.2 |
| PAT | 256*128 | 95.4 | 88.0 |
| TransReID | 256*128 | 95.4 | 88.0 |
| PFD | 256*128 | 95.5 | 89.6 |
Citation
If you find our work useful in your research, please consider citing this paper! (preprint version will be available soon)
@inproceedings{wang2022pfd,
Title= {Pose-guided Feature Disentangling for Occluded Person Re-identification based on Transformer},
Author= {Tao Wang, Hong Liu, Pinhao Song, Tianyu Guo and Wei Shi},
Booktitle= {AAAI},
Year= {2022}
}
Acknowledgement
Our code is extended from the following repositories. We thank the authors for releasing the codes.
License
This project is licensed under the terms of the MIT license.
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跟论文中的结果大约相差2%，我使用的时pytorch1.7.1, cuda10.2, python3.7.13;所以我想知道这是什么原因造成的呢？
期待您的回复。
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