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Computer Science > Computer Vision and Pattern Recognition

arXiv:2204.13317 (cs)
[Submitted on 28 Apr 2022 (v1), last revised 19 Jul 2022 (this version, v4)]

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
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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 this https URL.
Comments: 5 pages, 2 tables, MMRotate is accepted by ACM MM 2022 (OS Track). Yue Zhou and Xue Yang provided equal contribution. The code is publicly released at this https URL
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2204.13317 [cs.CV]
  (or arXiv:2204.13317v4 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2204.13317
Related DOI: https://doi.org/10.1145/3503161.3548541

Submission history

From: Xue Yang [view email]
[v1] Thu, 28 Apr 2022 07:31:00 UTC (259 KB)
[v2] Tue, 12 Jul 2022 07:04:53 UTC (263 KB)
[v3] Thu, 14 Jul 2022 00:44:42 UTC (263 KB)
[v4] Tue, 19 Jul 2022 08:05:58 UTC (263 KB)
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