Package: otxdeploy
OpenMMLab websiteHOTOpenMMLab platformTRY IT OUTEnglish |简体中文HighlightsThe MMDeploy 1.x has been released, which is adapted to upstream codebases from OpenMMLab 2.0. Pleasealign the versionwhen using it.
The default branch has been switched tomainfrommaster. MMDeploy 0.x (master) will be deprecated and new features will only be added to MMDeploy 1.x (main) in future.mmdeploymmenginemmcvmmdetothers0.x.y-<=1.x.y<=2.x.y0.x.y1.x.y0.x.y2.x.y3.x.y1.x.yIntroductionMMDeploy is an open-source deep learning model deployment toolset. It is a part of theOpenMMLabproject.Main featuresFully support OpenMMLab modelsThe currently supported codebases and models are as follows, and more will be included in the futuremmclsmmdetmmsegmmeditmmocrmmposemmdet3dmmrotatemmaction2Multiple inference backends are availableThe supported Device-Platform-InferenceBackend matrix is presented as following, and more will be compatible.The benchmark can be found fromhereDevice / PlatformLinuxWindowsmacOSAndroidx86_64 CPUONNXRuntimepplnnncnnLibTorchOpenVINOTVMONNXRuntimeOpenVINO--ARM CPUncnn--ncnnRISC-Vncnn---NVIDIA GPUONNXRuntimeTensorRTpplnnLibTorchTVMONNXRuntimeTensorRTpplnn--NVIDIA JetsonTensorRT---Huawei ascend310CANN---RockchipRKNN---Apple M1--CoreML-Adreno GPU---SNPEncnnHexagon DSP---SNPE|Efficient and scalable C/C++ SDK FrameworkAll kinds of modules in the SDK can be extended, such asTransformfor image processing,Netfor Neural Network inference,Modulefor postprocessing and so onDocumentationPlease readgetting_startedfor the basic usage of MMDeploy. We also provide tutoials about:BuildBuild from DockerBuild from ScriptBuild for LinuxBuild for WindowsBuild for macOSBuild for Win10Build for AndroidBuild for JetsonBuild for SNPEBuild for RockchipCross Build for aarch64User GuideHow to convert modelHow to write configHow to profile modelHow to quantize modelUseful toolsDeveloper GuideArchitectureHow to support new modelsHow to support new backendsHow to partition modelHow to test rewritten modelHow to test backend opsHow to do regression testCustom Backend OpsncnnonnxruntimetensorrtFAQContributingBenchmark and Model zooYou can find the supported models fromhereand their performance in thebenchmark.ContributingWe appreciate all contributions to MMDeploy. Please refer toCONTRIBUTING.mdfor the contributing guideline.AcknowledgementWe would like to sincerely thank the following teams for their contributions toMMDeploy:OpenPPLOpenVINOncnnCitationIf you find this project useful in your research, please consider citing:@misc{=mmdeploy,title={OpenMMLab's Model Deployment Toolbox.},author={MMDeploy Contributors},howpublished={\url{https://github.com/open-mmlab/mmdeploy}},year={2021}}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.MMYOLO: OpenMMLab YOLO series toolbox and benchmarkMMRotate: 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.