We present Open-CD, a change detection toolbox that contains a rich set of change detection methods as well as related components and modules. The toolbox started from a series of open source general vision task tools, including OpenMMLab Toolkits, PyTorch Image Models, etc. It gradually evolves into a unified platform that covers many popular change detection methods and contemporary modules. It not only includes training and inference codes, but also provides some useful scripts for data analysis. We believe this toolbox is by far the most complete change detection toolbox. In this report, we introduce the various features, supported methods and applications of Open-CD. In addition, we also conduct a benchmarking study on different methods and components. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new change detectors. Code and models are available atthis https URL. Pioneeringly, this report also includes brief descriptions of the algorithms supported in Open-CD, mainly contributed by their authors. We sincerely encourage researchers in this field to participate in this project and work together to create a more open community. This toolkit and report will be kept updated.
View on arXiv@article{li2025_2407.15317,
title={ Open-CD: A Comprehensive Toolbox for Change Detection },
author={ Kaiyu Li and Jiawei Jiang and Andrea Codegoni and Chengxi Han and Yupeng Deng and Keyan Chen and Zhuo Zheng and Hao Chen and Ziyuan Liu and Yuantao Gu and Zhengxia Zou and Zhenwei Shi and Sheng Fang and Deyu Meng and Zhi Wang and Xiangyong Cao },
journal={arXiv preprint arXiv:2407.15317},
year={ 2025 }
}