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Cross-Platform Video Person ReID: A New Benchmark Dataset and Adaptation Approach(PDF)
Installation
conda create -n vslaclip python=3.8
conda activate vslaclip
conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=10.2 -c pytorch
pip install yacs
pip install timm
pip install scikit-image
pip install tqdm
pip install ftfy
pip install regex
Training
For example, if you want to run for the ls-vid, you need to modify the config file to
DATASETS:
NAMES: ('lsvid')
ROOT_DIR: ('your_dataset_dir')
OUTPUT_DIR: 'your_output_dir'
Then, if you want to use weight of VIFI-CLIP to initialize model, you need to down the weight form link and modify config file as:
MODEL:
VIFI_WEIGHT : 'your_dataset_dir/vifi_weight.pth'
USE_VIFI_WEIGHT : True
If you want to run FT-CLIP (fine tune image encoder):
CUDA_VISIBLE_DEVICES=0 python train_fine_tune.py --config_file configs/ft/vit_ft.yml
if you want to run VSLA-CLIP:
CUDA_VISIBLE_DEVICES=0 python train_reidadapter.py --config_file configs/adapter/vit_adapter.yml
Evaluation
For example, if you want to test VSLA-CLIP for LS-VID
CUDA_VISIBLE_DEVICES=0 python test.py --config_file 'your_config_file' TEST.WEIGHT 'your_trained_checkpoints_path/ViT-B-16_120.pth'
Weights
| Dataset | LS-VID | MARS | iLIDS | G2A |
|---|---|---|---|---|
| VSLA-CLIP‡ | model | model | model | model |
Citation
@inproceedings{vsla-clip,
author = {S. Zhang and W. Luo and D. Cheng and Q. Yang and L. Ran and Y. Xing and Y. Zhang},
title = {Cross-Platform Video Person ReID: A New Benchmark Dataset and Adaptation Approach},
year = {2024},
booktitle = {ECCV}
}