MMRotate: A Rotated Object Detection Benchmark using PyTorch

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.

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

References in corpus (3)

• R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection
• G-Rep: Gaussian Representation for Arbitrary-Oriented Object Detection
• FOTS: Fast Oriented Text Spotting with a Unified Network

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