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MMRotate is an open-source toolbox for rotated object detection based on PyTorch.It is a part of theOpenMMLab project.

The master branch works withPyTorch 1.6+.

We are excited to announce our latest work on real-time object recognition tasks,RTMDet, a family of fully convolutional single-stage detectors. RTMDet not only achieves the best parameter-accuracy trade-off on object detection from tiny to extra-large model sizes but also obtains new state-of-the-art performance on instance segmentation and rotated object detection tasks. Details can be found in thetechnical report. Pre-trained models arehere.

0.3.4 was released in 01/02/2023:

• Fix compatibility with numpy, scikit-learn, and e2cnn.
• Support empty patch in Rotate Transform
• use iof for RRandomCrop validation

Please refer tochangelog.md for details and release history.

MMRotate depends onPyTorch,MMCV andMMDetection.Below are quick steps for installation.Please refer toInstall Guide for more detailed instruction.

conda create -n open-mmlab python=3.7 pytorch==1.7.0 cudatoolkit=10.1 torchvision -c pytorch -yconda activate open-mmlabpip install openmimmim install mmcv-fullmim install mmdetgit clone https://github.com/open-mmlab/mmrotate.gitcd mmrotatepip install -r requirements/build.txtpip install -v -e.

Please seeget_started.md for the basic usage of MMRotate.We providecolab tutorial, and other tutorials for:

• learn the basics
• learn the config
• customize dataset
• customize model
• useful tools

Results and models are available in theREADME.md of each method's config directory.A summary can be found in theModel Zoo page.

Please refer todata_preparation.md to prepare the data.

Please refer toFAQ for frequently asked questions.

We appreciate all contributions to improve MMRotate. Please refer toCONTRIBUTING.md for the contributing guideline.

MMRotate is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new methods.

If you use this toolbox or benchmark in your research, please cite this project.

@inproceedings{zhou2022mmrotate,title   ={MMRotate: A Rotated Object Detection Benchmark using PyTorch},author  ={Zhou, Yue and Yang, Xue and Zhang, Gefan and Wang, Jiabao and Liu, Yanyi and             Hou, Liping and Jiang, Xue and Liu, Xingzhao and Yan, Junchi and Lyu, Chengqi and             Zhang, Wenwei and Chen, Kai},booktitle={Proceedings of the 30th ACM International Conference on Multimedia},year={2022}}

This project is released under theApache 2.0 license.

• MMCV: OpenMMLab foundational library for computer vision.
• MIM: MIM installs OpenMMLab packages.
• MMClassification: OpenMMLab image classification toolbox and benchmark.
• MMDetection: OpenMMLab detection toolbox and benchmark.
• MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
• MMRotate: OpenMMLab rotated object detection toolbox and benchmark.
• MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
• MMOCR: OpenMMLab text detection, recognition, and understanding toolbox.
• MMPose: OpenMMLab pose estimation toolbox and benchmark.
• MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.
• MMSelfSup: OpenMMLab self-supervised learning toolbox and benchmark.
• MMRazor: OpenMMLab model compression toolbox and benchmark.
• MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.
• MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
• MMTracking: OpenMMLab video perception toolbox and benchmark.
• MMFlow: OpenMMLab optical flow toolbox and benchmark.
• MMEditing: OpenMMLab image and video editing toolbox.
• MMGeneration: OpenMMLab image and video generative models toolbox.
• MMDeploy: OpenMMLab model deployment framework.