Package: mmrotate
OpenMMLab websiteHOTOpenMMLab platformTRY IT OUT📘Documentation|🛠️Installation|👀Model Zoo|🆕Update News|🚀Ongoing Projects|🤔Reporting IssuesEnglish |简体中文IntroductionMMRotate 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+.https://user-images.githubusercontent.com/10410257/154433305-416d129b-60c8-44c7-9ebb-5ba106d3e9d5.MP4Major FeaturesSupport multiple angle representationsMMRotate provides three mainstream angle representations to meet different paper settings.Modular DesignWe decompose the rotated object detection framework into different components,
which makes it much easy and flexible to build a new model by combining different modules.Strong baseline and State of the artThe toolbox provides strong baselines and state-of-the-art methods in rotated object detection.What's NewHighlightWe 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.TaskDatasetAPFPS(TRT FP16 BS1 3090)Object DetectionCOCO52.8322Instance SegmentationCOCO44.6188Rotated Object DetectionDOTA78.9(single-scale)/81.3(multi-scale)1210.3.4was released in 01/02/2023:Fix compatibility with numpy, scikit-learn, and e2cnn.Support empty patch in Rotate Transformuse iof for RRandomCrop validationPlease refer tochangelog.mdfor details and release history.InstallationMMRotate depends onPyTorch,MMCVandMMDetection.
Below are quick steps for installation.
Please refer toInstall Guidefor more detailed instruction.condacreate-nopen-mmlabpython=3.7pytorch==1.7.0cudatoolkit=10.1torchvision-cpytorch-y
condaactivateopen-mmlab
pipinstallopenmim
miminstallmmcv-full
miminstallmmdet
gitclonehttps://github.com/open-mmlab/mmrotate.gitcdmmrotate
pipinstall-rrequirements/build.txt
pipinstall-v-e.Get StartedPlease seeget_started.mdfor the basic usage of MMRotate.
We providecolab tutorial, and other tutorials for:learn the basicslearn the configcustomize datasetcustomize modeluseful toolsModel ZooResults and models are available in theREADME.mdof each method's config directory.
A summary can be found in theModel Zoopage.Supported algorithms:Rotated RetinaNet-OBB/HBB(ICCV'2017)Rotated FasterRCNN-OBB(TPAMI'2017)Rotated RepPoints-OBB(ICCV'2019)Rotated FCOS(ICCV'2019)RoI Transformer(CVPR'2019)Gliding Vertex(TPAMI'2020)Rotated ATSS-OBB(CVPR'2020)CSL(ECCV'2020)R3Det(AAAI'2021)S2A-Net(TGRS'2021)ReDet(CVPR'2021)Beyond Bounding-Box(CVPR'2021)Oriented R-CNN(ICCV'2021)GWD(ICML'2021)KLD(NeurIPS'2021)SASM(AAAI'2022)Oriented RepPoints(CVPR'2022)KFIoU(arXiv)G-Rep(stay tuned)Data PreparationPlease refer todata_preparation.mdto prepare the data.FAQPlease refer toFAQfor frequently asked questions.ContributingWe appreciate all contributions to improve MMRotate. Please refer toCONTRIBUTING.mdfor the contributing guideline.AcknowledgementMMRotate 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.CitationIf 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 andHou, Liping and Jiang, Xue and Liu, Xingzhao and Yan, Junchi and Lyu, Chengqi andZhang, Wenwei and Chen, Kai},booktitle={Proceedings of the 30th ACM International Conference on Multimedia},year={2022}}LicenseThis project is released under theApache 2.0 license.Projects in OpenMMLabMMCV: 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.