MMRotate
| Autor: | 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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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Proceedings of the 30th ACM International Conference on Multimedia. |
| DOI: | 10.1145/3503161.3548541 |
| Popis: | 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. Comment: 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 https://github.com/open-mmlab/mmrotate |
| Databáze: | OpenAIRE |
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