Official Pytorch implementation for our AAAI 2023 paper HOTCOLD Block: Fooling Thermal Infrared Detectors with a Novel Wearable Design.
- python 3.9
- Pytorch 1.10
- At least 1x12GB NVIDIA GPU
git clone https://github.com/weihui1308/HOTCOLDBlock
cd HOTCOLDBlock-main
pip install -r requirements.txt
- Download the complete FLIR ADAS Dataset and convert its annotation format to the YOLO format.
- Filter out instances of "person" from the dataset, and keep only those instances with a height greater than 120 pixels.
- We have placed the conversion script json2yolo.py in the dataset folder.
- Put the obtained dataset in YOLO format in the "dataset/FLIR_ADAS" folder.
- Download the YOLOv5 pre-trained model. In this work, we use the YOLOv5s.pt.
- Fine-tune the pre-trained YOLOv5 model on the "dataset/FLIR_ADAS". You can download the model of our training at Google Drive.
Once you have setup your path, you can run an experiment like so:
python main.py --epochs 5
The terminal will print the gbest_position and gbest_value.
If you find this repository useful, please consider citing our paper:
@inproceedings{wei2023hotcold,
title={HOTCOLD Block: Fooling Thermal Infrared Detectors with a Novel Wearable Design},
author={Hui Wei and Zhixiang Wang and Xuemei Jia and Yinqiang Zheng and Hao Tang and Shin'ichi Satoh and Zheng Wang},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
year={2023}
}
We would like to acknowledge the YOLOv5 open-source library (https://github.com/ultralytics/yolov5). YOLOv5 is a powerful object detection algorithm that has greatly facilitated our development efforts. We are grateful to the developers and contributors of YOLOv5 for making their work available to the community.