This repo is based on YOLOv5 repo. Please follow that repo for installation and preparation. The version I built for this project is YOLO v5 3.0. The proposed methods are also easy to be migrated into advanced YOLO versions.
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Modify the config of the data in the data subfolders. Please refer to the instructions in the yaml file.
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The command below can reproduce the corresponding results mentioned in the paper.
python train_MMD.py --img 640 --batch 12 --epochs 300 --data ./data/city_and_foggy8_3.yaml --cfg ./models/yolov5x.yaml --hyp ./data/hyp_aug/m1.yaml --weights '' --name "test"The codes have been released but need further construction. If you are intersted in more details of the ablation studies, you can refer to the folder "train_files_for_abl". I have listed nearly every train.py in this folder. I hope you find them helpful.
I will try my best to update :(. You can also check our previous work AcroFOD "https://github.com/Hlings/AcroFOD".
- If you find this paper/repository useful, please consider citing our paper:
@inproceedings{gao2023asyfod,
title={AsyFOD: An Asymmetric Adaptation Paradigm for Few-Shot Domain Adaptive Object Detection},
author={Gao, Yipeng and Lin, Kun-Yu and Yan, Junkai and Wang, Yaowei and Zheng, Wei-Shi},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={3261--3271},
year={2023}
}