MMRotate: A Rotated Object Detection Benchmark using Pytorch

• URL: http://arxiv.org/abs/2204.13317v1
• Date: Thu, 28 Apr 2022 07:31:00 GMT
• Title: MMRotate: A Rotated Object Detection Benchmark using Pytorch
• Authors: 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 summary: We present an open-source toolbox, named MMRotate, which provides a coherent algorithm framework for rotated object detection. MMRotate implements 18 state-of-the-art algorithms and supports the three most frequently used angle definition methods. We also provide a large number of trained models and detailed benchmarks to give insights into the performance of rotated object detection.
• Score: 68.3869241353113
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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