Jian Wei, Qinzhao Wang

• torch == 1.12.0
• Python 3

This repository is developed using python 3.9 on Ubuntu 18.04 LTS. The CUDA nad CUDNN version is 11.7 and 8.0 respectively. We use one NVIDIA 3090 GPU card for training and testing. Other platforms or GPU cards are not fully tested.

The pretrain weights yolov5l in avalibale https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l.pt

The usage of YOLO-G is same as YOLO. Take an example:

# to train YOLO-G on cityscape-->foggy cityscapes:
sh train_GRL.sh
# To validate YOLO-G on foggy cityscape:
python val_GRL.py --weight ./runs/train/**.pt  --data ./data/domain/cityscapes_foggycityscapes.yaml
# To detect YOLO-G on foggy cityscapes:
python detect.py --weight ./runs/train/**.pt --source /your_image_folder

Refer to SSDA-YOLO

If you find this code useful in your research, please consider citing us:

@article{YOLO-G,
	title={YOLO-G: Improved YOLO for Cross Domain Object Detection},
	author={Jian Wei, Qinzhao Wang},
	year={2023}
}