Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions
Accepted by AAAI 2022 [arxiv]
Wenyu Liu, Gaofeng Ren, Runsheng Yu, Shi Guo, Jianke Zhu, Lei Zhang
Installation
git clone https://github.com/wenyyu/Image-Adaptive-YOLO.git
cd Image-Adaptive-YOLO
# Require python3 and tensorflow
pip install -r ./docs/requirements.txt
Datasets and Models
PSCAL VOC RTTS ExDark Voc_foggy_test & Voc_dark_test & Models (key: iayl)
Quick test
# put checkpoint model in the corresponding directory
# change the data and model paths in core/config.py
python evaluate.py
Train and Evaluate on the datasets
- Prepare the training and testing datasets, edit core/config.py to configure
python train.py # we trained our model from scratch.
python evaluate.py
cd mAP & python main.py
- Train with your own dataset
reference the implementation tensorflow-yolov3 to prepare the files.
Acknowledgments
The code is based on tensorflow-yolov3, exposure.
Citation
@inproceedings{liu2022imageadaptive,
title={Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions},
author={Liu, Wenyu and Ren, Gaofeng and Yu, Runsheng and Guo, Shi and Zhu, Jianke and Zhang, Lei},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
year={2022}
}
