A strong baseline for Occluded person re-identification
[Paper] [[Video]] [[Poster]]
- [2024.09.14] We release the first version of our codebase. Please update frequently as we will add more documentation during the next few weeks.
Make sure conda is installed.
# clone this repository
git clone https://github.com/Cuixxx/ProFD.git
# create conda environment
cd ProFD/ # enter project folder
conda create --name profd python=3.10
conda activate profd
# install dependencies
# make sure `which python` and `which pip` point to the correct path
pip install -r requirements.txt
# install torch and torchvision (select the proper cuda version to suit your machine)
conda install pytorch torchvision cudatoolkit=9.0 -c pytorch
# install torchreid (don't need to re-build it if you modify the source code)
python setup.py develop
You can download the human parsing labels on GDrive.
These labels were generated using the PifPaf pose estimation model and then filtered using segmentation masks from Mask-RCNN.
We provide the labels for five datasets: Market-1501, DukeMTMC-reID, Occluded-Duke, Occluded-ReID and P-DukeMTMC.
After downloading, unzip the file and put the masks folder under the corresponding dataset directory.
For instance, Market-1501 should look like this:
Market-1501-v15.09.15
├── bounding_box_test
├── bounding_box_train
├── masks
│ └── pifpaf_maskrcnn_filtering
│ ├── bounding_box_test
│ ├── bounding_box_train
│ └── query
└── query
Make also sure to set data.root config to your dataset root directory path, i.e., all your datasets folders (Market-1501-v15.09.15, DukeMTMC-reID, Occluded_Duke, P-DukeMTMC-reID, Occluded_REID) should be under this path.
We plan to add automatic download of these labels in the future.
We also plan to release the python script to generate these labels for any given dataset.
You can create human parsing labels for your own dataset using the following command:
conda activate profd
python scripts/get_labels --source [Dataset Path]
The labels will be saved under the source directory in the masks folder as per the code convention.
You can test the above downloaded models using the following command:
conda activate profd
python scripts/main.py --config-file configs/clipreid/clipreid_<target_dataset>_test.yaml
For instance, for the Market-1501 dataset:
conda activate profd
python scripts/main.py --config-file configs/clipreid/clipreid_market1501_test.yaml
Configuration files for other datasets are available under configs/clipreid/.
Make sure the model.load_weights in these yaml config files points to the pre-trained weights you just downloaded.
Training configs for five datasets (Market-1501, DukeMTMC-reID, Occluded-Duke, Occluded-ReID and P-DukeMTMC) are provided in the configs/clipreid/ folder.
A training procedure can be launched with:
conda activate profd
python ./scripts/main.py --config-file configs/clipreid/clipreid_<target_dataset>_train.yaml
For instance, for the Occluded-Duke dataset:
conda activate profd
python scripts/main.py --config-file configs/clipreid/clipreid_occ_duke_train.yaml
Make sure to download and install the human parsing labels for your training dataset before runing this command.
If you have any question/suggestion, or find any bug/issue with the code, please raise a GitHub issue in this repository, I'll be glab to help you as much as I can! I'll try to update the documentation regularly based on your questions.
If you use this repository for your research or wish to refer to our method ProFD, please use the following BibTeX entry:
@inproceedings{cui2024profd,
title={ProFD: Prompt-Guided Feature Disentangling for Occluded Person Re-Identification},
author={Cui, Can and Huang, Siteng and Song, Wenxuan and Ding, Pengxiang and Min, Zhang and Wang, Donglin},
booktitle={ACM Multimedia 2024},
year={2024}
}