CVPR 2022 paper 《Exploring Endogenous Shift for Cross-domain Detection: A Large-scale Benchmark and Perturbation Suppression Network》
Please go to the webpage and download according to the prompts.
- Python 3.6
- Pytorch 0.4.1
- CUDA 8.0 or higher
pip install -r requirements.txt
cd lib
sh make.sh
The scripts
folder has all the training scripts. For example, if you want to train an experiment from domain1 to domain2, just run:
sh scripts/train-1-2-fc.sh
The scripts
folder has all the testing scripts. For example, if you want to test a model trained from domain1 to domain2, just run:
sh scripts/test-all-1-2.sh
If this work helps your research, please cite the following paper.
@inproceedings{Tao:CVPR22,
author = {Renshuai Tao and Hainan Li and Tianbo Wang and Yanlu Wei and Yifu Ding and Bowei Jin and, Hongping Zhi and Xianglong Liu and Aishan Liu},
title = {Exploring Endogenous Shift for Cross-domain Detection: A Large-scale Benchmark and Perturbation Suppression Network},
booktitle = {IEEE CVPR},
year = {2022},
}