Awesome

📘Documentation | 🛠️Installation | 👀Model Zoo | 🤔Reporting Issues

English | 简体中文

Introduction

MMFlow is an open source optical flow toolbox based on PyTorch. It is a part of the OpenMMLab project.

The master branch works with PyTorch 1.5+.

https://user-images.githubusercontent.com/76149310/141947796-af4f1e67-60c9-48ed-9dd6-fcd809a7d991.mp4

Major features

• The First Unified Framework for Optical Flow

  MMFlow is the first toolbox that provides a framework for unified implementation and evaluation of optical flow algorithms.

• Flexible and Modular Design

  We 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 Box

  The 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 New

v0.5.2 was released in 01/10/2023:

• Add flow1d attention

Please refer to changelog.md for details and release history.

Installation

Please refer to install.md for installation and guidance in dataset_prepare for dataset preparation.

Get Started

If you're new of optical flow, you can start with learn the basics. If you’re familiar with it, check out getting_started to try out MMFlow.

Refer to the below tutorials to dive deeper:

• config

• model inference

• fine tuning

• data pipeline

• add new modules

• customized runtime

Benchmark and model zoo

Results and models are available in the model 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)

Contributing

We appreciate all contributions improving MMFlow. Please refer to CONTRIBUTING.md in MMCV for more details about the contributing guideline.

Acknowledgement

MMFlow 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.

Citation

If 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}
}

License

This project is released under the Apache 2.0 license.

Projects in OpenMMLab

• MMCV: 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.