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.
翻译:我们提出了一个名为MMMROTate的开放源码工具箱,它为基于深层学习的大众旋转物体探测算法的培训、推断和评价提供了一个连贯的算法框架,MMROTate实施了18种最先进的算法,支持三种最常用的角度定义方法,为便利今后对旋转物体探测相关问题的研究和工业应用,我们还提供了大量经过培训的模型和详细基准,以深入了解旋转物体探测的性能。MMMROTate在https://github.com/open-mmlab/mmrotate上公开发布。