Package: mmyolo
OpenMMLab websiteHOTOpenMMLab platformTRY IT OUT📘Documentation|🛠️Installation|👀Model Zoo|🆕Update News|🤔Reporting IssuesEnglish |简体中文📄 Table of Contents🥳 🚀 What's New✨ Highlight📖 Introduction🛠️ Installation👨‍🏫 Tutorial📊 Overview of Benchmark and Model Zoo❓ FAQ🙌 Contributing🤝 Acknowledgement🖊️ Citation🎫 License🏗️ Projects in OpenMMLab🥳 🚀 What's New🔝💎v0.6.0was released on 15/8/2023:Support YOLOv5 instance segmentationSupport YOLOX-Pose based on MMPoseAdd 15 minutes instance segmentation tutorial.YOLOv5 supports using mask annotation to optimize bboxAdd Multi-scale training and testing docsFor release history and update details, please refer tochangelog.✨ Highlight🔝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.TaskDatasetAPFPS(TRT FP16 BS1 3090)Object DetectionCOCO52.8322Instance SegmentationCOCO44.6188Rotated Object DetectionDOTA78.9(single-scale)/81.3(multi-scale)121MMYOLO currently implements the object detection and rotated object detection algorithm, but it has a significant training acceleration compared to the MMDeteciton version. The training speed is 2.6 times faster than the previous version.📖 Introduction🔝MMYOLO is an open source toolbox for YOLO series algorithms based on PyTorch andMMDetection. It is a part of theOpenMMLabproject.The master branch works withPyTorch 1.6+.Major features🕹️Unified and convenient benchmarkMMYOLO unifies the implementation of modules in various YOLO algorithms and provides a unified benchmark. Users can compare and analyze in a fair and convenient way.📚Rich and detailed documentationMMYOLO provides rich documentation for getting started, model deployment, advanced usages, and algorithm analysis, making it easy for users at different levels to get started and make extensions quickly.🧩Modular DesignMMYOLO decomposes the framework into different components where users can easily customize a model by combining different modules with various training and testing strategies.The figure above is contributed by RangeKing@GitHub, thank you very much!And the figure of P6 model is inmodel_design.md.🛠️ Installation🔝MMYOLO relies on PyTorch, MMCV, MMEngine, and MMDetection. Below are quick steps for installation. Please refer to theInstall Guidefor more detailed instructions.condacreate-nmmyolopython=3.8pytorch==1.10.1torchvision==0.11.2cudatoolkit=11.3-cpytorch-y
condaactivatemmyolo
pipinstallopenmim
miminstall"mmengine>=0.6.0"miminstall"mmcv>=2.0.0rc4,<2.1.0"miminstall"mmdet>=3.0.0,<4.0.0"gitclonehttps://github.com/open-mmlab/mmyolo.gitcdmmyolo# Install albumentationspipinstall-rrequirements/albu.txt# Install MMYOLOmiminstall-v-e.👨‍🏫 Tutorial🔝MMYOLO is based on MMDetection and adopts the same code structure and design approach. To get better use of this, please readMMDetection Overviewfor the first understanding of MMDetection.The usage of MMYOLO is almost identical to MMDetection and all tutorials are straightforward to use, you can also learn aboutMMDetection User Guide and Advanced Guide.For different parts from MMDetection, we have also prepared user guides and advanced guides, please read ourdocumentation.Get StartedOverviewDependenciesInstallation15 minutes object detection15 minutes rotated object detection15 minutes instance segmentationResources summaryRecommended TopicsHow to contribute code to MMYOLOTraining testing tricksMMYOLO model designAlgorithm principles and implementationReplace the backbone networkMMYOLO model complexity analysisAnnotation-to-deployment workflow for custom datasetVisualizationModel deploymentTroubleshooting stepsMMYOLO application examplesMM series repo essential basicsDataset preparation and descriptionCommon UsageResume trainingEnabling and disabling SyncBatchNormEnabling AMPMulti-scale training and testingTTA Related NotesAdd plugins to the backbone networkFreeze layersOutput model predictionsSet random seedModule combinationCross-library calls using mimApply multiple NecksSpecify specific device training or inferenceSingle and multi-channel application examplesUseful ToolsBrowse coco jsonBrowse datasetPrint configDataset analysisOptimize anchorsExtract subcocoVisualization schedulerDataset convertersDownload datasetLog analysisModel convertersBasic TutorialsLearn about configs with YOLOv5Data flowRotated detectionCustom InstallationCommon Warning NotesFAQAdvanced TutorialsMMYOLO cross-library applicationDescriptionsChangelogCompatibilityConventionsCode Style📊 Overview of Benchmark and Model Zoo🔝Results and models are available in themodel zoo.Supported TasksObject detectionRotated object detectionSupported AlgorithmsYOLOv5YOLOv5u(Inference only)YOLOXRTMDetRTMDet-RotatedYOLOv6YOLOv7PPYOLOEYOLOv8Supported DatasetsCOCO DatasetVOC DatasetCrowdHuman DatasetDOTA 1.0 DatasetModule ComponentsBackbonesNecksLossCommonYOLOv5CSPDarknetYOLOv8CSPDarknetYOLOXCSPDarknetEfficientRepCSPNeXtYOLOv7BackbonePPYOLOECSPResNetmmdet backbonemmcls backbonetimmYOLOv5PAFPNYOLOv8PAFPNYOLOv6RepPAFPNYOLOXPAFPNCSPNeXtPAFPNYOLOv7PAFPNPPYOLOECSPPAFPNIoULossmmdet loss❓ FAQ🔝Please refer to theFAQfor frequently asked questions.🙌 Contributing🔝We appreciate all contributions to improving MMYOLO. Ongoing projects can be found in ourGitHub Projects. Welcome community users to participate in these projects. Please refer toCONTRIBUTING.mdfor the contributing guideline.🤝 Acknowledgement🔝MMYOLO 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 feedback.
We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to re-implement existing methods and develop their own new detectors.🖊️ Citation🔝If you find this project useful in your research, please consider citing:@misc{mmyolo2022,
    title={{MMYOLO: OpenMMLab YOLO}series toolbox and benchmark},
    author={MMYOLO Contributors},
    howpublished ={\url{https://github.com/open-mmlab/mmyolo}},
    year={2022}}🎫 License🔝This project is released under theGPL 3.0 license.🏗️ Projects in OpenMMLab🔝MMEngine: OpenMMLab foundational library for training deep learning models.MMCV: OpenMMLab foundational library for computer vision.MMPreTrain: OpenMMLab pre-training toolbox and benchmark.MMagic: OpenMMLabAdvanced,Generative andIntelligentCreation toolbox.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.MMYOLO: OpenMMLab YOLO series 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.MIM: MIM installs OpenMMLab packages.MMEval: OpenMMLab machine learning evaluation library.Playground: A central hub for gathering and showcasing amazing projects built upon OpenMMLab.