Unstructured Feature Decoupling for Vehicle Re-Identification pdf
- 2022.7 We release the code of UFDN.
pip install -r requirements.txt
(we use /torch 1.7.1 /torchvision 0.8.2 /timm 0.3.2 /cuda 11.0 / 16G or 32G V100 for training and evaluation.)
mkdir data
Download the vehicle datasets VehicleID, VeRi-776, VERIWILD. Then unzip them and rename them under the directory like
data
└── VeRi
└── images ..
└── VehicleID
└── images ..
└── VERI-WILD
└── images ..
You need to download the ImageNet pretrained transformer model : Res50, Swin-tiny.
We utilize 1 GPU for training VeRi-776 Dataset
sh experiments/train_res50_UFDN_776.sh or train_swin_UFDN_776.sh
We utilize 1 GPU for training VehicleID Dataset
sh experiments/train_res50_UFDN_VehicleID.sh or train_swin_UFDN_VehicleID.sh
We have reproduced the performance to verify the reproducibility. The reproduced results may have a gap of about 0.1-0.2% with the numbers in the paper.
method | backbone | dataset | Result | log | model |
---|---|---|---|---|---|
UFDN | Res50 | VeRi-776 | 81.5%/96.5% | log | model |
UFDN | Swin-tiny | VeRi-776 | 80.8%/96.5% | log | model |
Codebase from reid-strong-baseline , pytorch-image-models, TransReID
If you have any question, please feel free to contact us. E-mail: [email protected] , [email protected]
If you find this code useful for your research, please cite our paper
@InProceedings{Qian_2022_ECCV,
author = {Qian, Wen and Luo, Hao and Peng, Silong and Wang, Fan and Chen, Chen and Li, Hao},
title = {Unstructured Feature Decoupling for Vehicle Re-Identification},
booktitle = { European Conference on Computer Vision (ECCV)},
month = {October},
year = {2022},
}