MMRotate: A Rotated Object Detection Benchmark using PyTorch
Yue Zhou
Xue Yang
Gefan Zhang
Jiabao Wang
Yanyi Liu
Liping Hou
Xue Jiang
Xingzhao Liu
Junchi Yan
Chengqi Lyu
Wenwei Zhang
Kai Chen

Abstract
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.
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