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.

arXiv comment5 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 (13)

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• CMID: A Unified Self-Supervised Learning Framework for Remote Sensing Image Understanding
• PETDet: Proposal Enhancement for Two-Stage Fine-Grained Object Detection
• RQFormer: Rotated Query Transformer for End-to-End Oriented Object Detection
• On the Robustness of Object Detection Models on Aerial Images
• An Approach to Systematic Data Acquisition and Data-Driven Simulation for the Safety Testing of Automated Driving Functions
• Density-Guided Dense Pseudo Label Selection For Semi-supervised Oriented Object Detection
• Structure Diagram Recognition in Financial Announcements
• Enhancing Rotated Object Detection via Anisotropic Gaussian Bounding Box and Bhattacharyya Distance
• Data-Driven Vision-Language Models for Remote Sensing: A Survey
• Investigating Long-term Training for Remote Sensing Object Detection
• From Real-World Traffic Data to Relevant Critical Scenarios
• DTKDP: A Dual Teacher Knowledge Distillation and Pruning Framework for Lightweight Oriented SAR Ship Detection