  We present an open-source toolbox, named MMRotate, which provides a coherent
algorithm framework of training, inferring, and evaluation for the popular
rotated object detection algorithm based on deep learning. MMRotate implements
18 state-of-the-art algorithms and supports the three most frequently used
angle definition methods. To facilitate future research and industrial
applications of rotated object detection-related problems, we also provide a
large number of trained models and detailed benchmarks to give insights into
the performance of rotated object detection. MMRotate is publicly released at
https://github.com/open-mmlab/mmrotate.
