This repository contains supplementary materials and code related to the paper titled "LADA: Semi-Supervised Domain Adaptation for Object Detection," which presents a new suggested labels for opensource hpwren dataset.
The paper "LADA: Learning Artificially Driven Agents" introduces a novel protocol for wildfire detection, leveraging semi-supervised Domain Adaptation for object detection, accompanied by a corresponding dataset designed for use by both academics and industries. Our dataset encompasses 30 times more diverse labeled scenes for the current largest benchmark wildfire dataset, HPWREN, and introduces a new labeling policy for wildfire detection.
hpwren_source.json: this json file contains bbox labels in coco-json format for source domain labels. It offers merged bbox as suggested in paper.hpwren_source_original.json: this json file contains bbox labels in coco-json format for source domain labels. It offers splitted bbox.target_label_x%.json: this json file contains bbox labels in coco-json format for x% labels for target domain labels. It offers merged bbox as suggested in paper.target_label_x%_original.json: this json file contains bbox labels in coco-json format for x% labels for target domain labels. It offers splitted bbox.
If you find the LADA framework or the insights from our paper useful in your research, please consider citing:
@article{lada, title={LADA: Semi-Supervised Domain Adaptation for Object Detection}, author={ Jang, JooYoung and Cha, Youngseo and Kim, Jisu and Lee, SooHyung and Lee, Geonu and Cho, Minkook and Hwang, Young and Kwak, Nojun}, journal={Tackling Climate Change with Machine Learning (ICLR 2024 Workshop), May 2024.}, year={2024}, }