Package: mmdet
OpenMMLab websiteHOTOpenMMLab platformTRY IT OUT📘Documentation|🛠️Installation|👀Model Zoo|🆕Update News|🚀Ongoing Projects|🤔Reporting IssuesEnglish |简体中文IntroductionMMDetection is an open source object detection toolbox based on PyTorch. It is
a part of theOpenMMLabproject.The main branch works withPyTorch 1.8+.Major featuresModular DesignWe decompose the detection framework into different components and one can easily construct a customized object detection framework by combining different modules.Support of multiple tasks out of boxThe toolbox directly supports multiple detection tasks such asobject detection,instance segmentation,panoptic segmentation, andsemi-supervised object detection.High efficiencyAll basic bbox and mask operations run on GPUs. The training speed is faster than or comparable to other codebases, includingDetectron2,maskrcnn-benchmarkandSimpleDet.State of the artThe toolbox stems from the codebase developed by theMMDetteam, who wonCOCO Detection Challengein 2018, and we keep pushing it forward.
The newly releasedRTMDetalso obtains new state-of-the-art results on real-time instance segmentation and rotated object detection tasks and the best parameter-accuracy trade-off on object detection.Apart from MMDetection, we also releasedMMEnginefor model training andMMCVfor computer vision research, which are heavily depended on by this toolbox.What's NewHighlightv3.3.0was released in 5/1/2024:MM-Grounding-DINO: An Open and Comprehensive Pipeline for Unified Object Grounding and DetectionGrounding DINO is a grounding pre-training model that unifies 2d open vocabulary object detection and phrase grounding, with wide applications. However, its training part has not been open sourced. Therefore, we propose MM-Grounding-DINO, which not only serves as an open source replication version of Grounding DINO, but also achieves significant performance improvement based on reconstructed data types, exploring different dataset combinations and initialization strategies. Moreover, we conduct evaluations from multiple dimensions, including OOD, REC, Phrase Grounding, OVD, and Fine-tune, to fully excavate the advantages and disadvantages of Grounding pre-training, hoping to provide inspiration for future work.code:mm_grounding_dino/README.mdWe 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)121InstallationPlease refer toInstallationfor installation instructions.Getting StartedPlease seeOverviewfor the general introduction of MMDetection.For detailed user guides and advanced guides, please refer to ourdocumentation:User GuidesTrain & TestLearn about ConfigsInference with existing modelsDataset PrepareTest existing models on standard datasetsTrain predefined models on standard datasetsTrain with customized datasetsTrain with customized models and standard datasetsFinetuning ModelsTest Results SubmissionWeight initializationUse a single stage detector as RPNSemi-supervised Object DetectionUseful ToolsAdvanced GuidesBasic ConceptsComponent CustomizationHow toWe also provide object detection colab tutorialand instance segmentation colab tutorial.To migrate from MMDetection 2.x, please refer tomigration.Overview of Benchmark and Model ZooResults and models are available in themodel zoo.ArchitecturesObject DetectionInstance SegmentationPanoptic SegmentationOtherFast R-CNN (ICCV'2015)Faster R-CNN (NeurIPS'2015)RPN (NeurIPS'2015)SSD (ECCV'2016)RetinaNet (ICCV'2017)Cascade R-CNN (CVPR'2018)YOLOv3 (ArXiv'2018)CornerNet (ECCV'2018)Grid R-CNN (CVPR'2019)Guided Anchoring (CVPR'2019)FSAF (CVPR'2019)CenterNet (CVPR'2019)Libra R-CNN (CVPR'2019)TridentNet (ICCV'2019)FCOS (ICCV'2019)RepPoints (ICCV'2019)FreeAnchor (NeurIPS'2019)CascadeRPN (NeurIPS'2019)Foveabox (TIP'2020)Double-Head R-CNN (CVPR'2020)ATSS (CVPR'2020)NAS-FCOS (CVPR'2020)CentripetalNet (CVPR'2020)AutoAssign (ArXiv'2020)Side-Aware Boundary Localization (ECCV'2020)Dynamic R-CNN (ECCV'2020)DETR (ECCV'2020)PAA (ECCV'2020)VarifocalNet (CVPR'2021)Sparse R-CNN (CVPR'2021)YOLOF (CVPR'2021)YOLOX (CVPR'2021)Deformable DETR (ICLR'2021)TOOD (ICCV'2021)DDOD (ACM MM'2021)RTMDet (ArXiv'2022)Conditional DETR (ICCV'2021)DAB-DETR (ICLR'2022)DINO (ICLR'2023)GLIP (CVPR'2022)DDQ (CVPR'2023)DiffusionDet (ArXiv'2023)EfficientDet (CVPR'2020)ViTDet (ECCV'2022)Detic (ECCV'2022)CO-DETR (ICCV'2023)Mask R-CNN (ICCV'2017)Cascade Mask R-CNN (CVPR'2018)Mask Scoring R-CNN (CVPR'2019)Hybrid Task Cascade (CVPR'2019)YOLACT (ICCV'2019)InstaBoost (ICCV'2019)SOLO (ECCV'2020)PointRend (CVPR'2020)DetectoRS (ArXiv'2020)SOLOv2 (NeurIPS'2020)SCNet (AAAI'2021)QueryInst (ICCV'2021)Mask2Former (ArXiv'2021)CondInst (ECCV'2020)SparseInst (CVPR'2022)RTMDet (ArXiv'2022)BoxInst (CVPR'2021)ConvNeXt-V2 (Arxiv'2023)Panoptic FPN (CVPR'2019)MaskFormer (NeurIPS'2021)Mask2Former (ArXiv'2021)XDecoder (CVPR'2023)Contrastive LearningSwAV (NeurIPS'2020)MoCo (CVPR'2020)MoCov2 (ArXiv'2020)DistillationLocalization Distillation (CVPR'2022)Label Assignment Distillation (WACV'2022)Semi-Supervised Object DetectionSoft Teacher (ICCV'2021)ComponentsBackbonesNecksLossCommonVGG (ICLR'2015)ResNet (CVPR'2016)ResNeXt (CVPR'2017)MobileNetV2 (CVPR'2018)HRNet (CVPR'2019)Generalized Attention (ICCV'2019)GCNet (ICCVW'2019)Res2Net (TPAMI'2020)RegNet (CVPR'2020)ResNeSt (ArXiv'2020)PVT (ICCV'2021)Swin (CVPR'2021)PVTv2 (ArXiv'2021)ResNet strikes back (ArXiv'2021)EfficientNet (ArXiv'2021)ConvNeXt (CVPR'2022)ConvNeXtv2 (ArXiv'2023)PAFPN (CVPR'2018)NAS-FPN (CVPR'2019)CARAFE (ICCV'2019)FPG (ArXiv'2020)GRoIE (ICPR'2020)DyHead (CVPR'2021)GHM (AAAI'2019)Generalized Focal Loss (NeurIPS'2020)Seasaw Loss (CVPR'2021)OHEM (CVPR'2016)Group Normalization (ECCV'2018)DCN (ICCV'2017)DCNv2 (CVPR'2019)Weight Standardization (ArXiv'2019)Prime Sample Attention (CVPR'2020)Strong Baselines (CVPR'2021)Resnet strikes back (ArXiv'2021)Some other methods are also supported inprojects using MMDetection.FAQPlease refer toFAQfor frequently asked questions.ContributingWe appreciate all contributions to improve MMDetection. Ongoing projects can be found in outGitHub Projects. Welcome community users to participate in these projects. Please refer toCONTRIBUTING.mdfor the contributing guideline.AcknowledgementMMDetection 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 detectors.CitationIf you use this toolbox or benchmark in your research, please cite this project.@article{mmdetection,
  title   = {{MMDetection}: Open MMLab Detection Toolbox and Benchmark},
  author  = {Chen, Kai and Wang, Jiaqi and Pang, Jiangmiao and Cao, Yuhang and
             Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and
             Liu, Ziwei and Xu, Jiarui and Zhang, Zheng and Cheng, Dazhi and
             Zhu, Chenchen and Cheng, Tianheng and Zhao, Qijie and Li, Buyu and
             Lu, Xin and Zhu, Rui and Wu, Yue and Dai, Jifeng and Wang, Jingdong
             and Shi, Jianping and Ouyang, Wanli and Loy, Chen Change and Lin, Dahua},
  journal= {arXiv preprint arXiv:1906.07155},
  year={2019}
}LicenseThis project is released under theApache 2.0 license.Projects in OpenMMLabMMEngine: 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: A unified evaluation library for multiple machine learning libraries.Playground: A central hub for gathering and showcasing amazing projects built upon OpenMMLab.