We present MMDetection, an object detection toolbox that contains a rich set of object detection and instance segmentation methods as well as related components and modules. The toolbox started from a codebase of MMDet team who won the detection track of COCO Challenge 2018. It gradually evolves into a unified platform that covers many popular detection methods and contemporary modules. It not only includes training and inference codes, but also provides weights for more than 200 network models. We believe this toolbox is by far the most complete detection toolbox. In this paper, we introduce the various features of this toolbox. In addition, we also conduct a benchmarking study on different methods, components, and their hyper-parameters. 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 detectors. Code and models are available at https://github.com/open-mmlab/mmdetection. The project is under active development and we will keep this document updated.
@article{arxiv.1906.07155,
title = {MMDetection: Open MMLab Detection Toolbox and Benchmark},
author = {Kai Chen and Jiaqi Wang and Jiangmiao Pang and Yuhang Cao and Yu Xiong and Xiaoxiao Li and Shuyang Sun and Wansen Feng and Ziwei Liu and Jiarui Xu and Zheng Zhang and Dazhi Cheng and Chenchen Zhu and Tianheng Cheng and Qijie Zhao and Buyu Li and Xin Lu and Rui Zhu and Yue Wu and Jifeng Dai and Jingdong Wang and Jianping Shi and Wanli Ouyang and Chen Change Loy and Dahua Lin},
journal= {arXiv preprint arXiv:1906.07155},
year = {2019}
}