Package: mmflow
OpenMMLab websiteHOTOpenMMLab platformTRY IT OUT📘Documentation|🛠️Installation|👀Model Zoo|🤔Reporting IssuesEnglish |简体中文IntroductionMMFlow is an open source optical flow toolbox based on PyTorch. It is a part of theOpenMMLabproject.The master branch works withPyTorch 1.5+.https://user-images.githubusercontent.com/76149310/141947796-af4f1e67-60c9-48ed-9dd6-fcd809a7d991.mp4Major featuresThe First Unified Framework for Optical FlowMMFlow is the first toolbox that provides a framework for unified implementation and evaluation of optical flow algorithms.Flexible and Modular DesignWe decompose the flow estimation framework into different components,
which makes it much easy and flexible to build a new model by combining different modules.Plenty of Algorithms and Datasets Out of the BoxThe toolbox directly supports popular and contemporary optical flow models,e.g.FlowNet, PWC-Net, RAFT, etc,
and representative datasets, FlyingChairs, FlyingThings3D, Sintel, KITTI, etc.What's Newv0.5.2was released in 01/10/2023:Add flow1d attentionPlease refer tochangelog.mdfor details and release history.InstallationPlease refer toinstall.mdfor installation and
guidance indataset_preparefor dataset preparation.Get StartedIf you're new of optical flow, you can start withlearn the basics. If you’re familiar with it, check outgetting_startedto try out MMFlow.Refer to the below tutorials to dive deeper:configmodel inferencefine tuningdata pipelineadd new modulescustomized runtimeBenchmark and model zooResults and models are available in themodel zoo.Supported methods:FlowNet (ICCV'2015)FlowNet2 (CVPR'2017)PWC-Net (CVPR'2018)LiteFlowNet (CVPR'2018)LiteFlowNet2 (TPAMI'2020)IRR (CVPR'2019)MaskFlownet (CVPR'2020)RAFT (ECCV'2020)GMA (ICCV' 2021)ContributingWe appreciate all contributions improving MMFlow. Please refer toCONTRIBUTING.mdin MMCV for more details about the contributing guideline.AcknowledgementMMFlow 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 flow algorithm.CitationIf you use this toolbox or benchmark in your research, please cite this project.@misc{2021mmflow,title={{MMFlow}: OpenMMLab Optical Flow Toolbox and Benchmark},author={MMFlow Contributors},howpublished={\url{https://github.com/open-mmlab/mmflow}},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.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.