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Documentation: https://mmflow.readthedocs.io/

Introduction

English | 简体中文

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+.

mmflow_readme.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 methods.

  • Flexible and Modular Design

    We decompose the flow estimation framework into different components and one can build a new model easily and flexibly by combining different modules.

  • Plenty of Algorithms and Datasets Out of the Box

    The toolbox directly supports popular and contemporary optical flow methods, e.g. FlowNet, PWC-Net, RAFT, etc,
    and representative datasets, FlyingChairs, FlyingThings3D, Sintel, KITTI, etc.

License

This project is released under the Apache 2.0 license.

Benchmark and model zoo

Results and models are available in the model zoo.

Supported methods:

Installation

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

Getting 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.md to try out MMFlow.

Refer to the below tutorials to dive deeper:

Contributing

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

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}
}

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.
  • MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
  • MMAction2: OpenMMLab’s next-generation action understanding toolbox and benchmark.
  • MMTracking: OpenMMLab video perception toolbox and benchmark.
  • MMPose: OpenMMLab pose estimation toolbox and benchmark.
  • MMEditing: OpenMMLab image and video editing toolbox.
  • MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding.
  • MMGeneration: OpenMMLab image and video generative models toolbox.
  • MMFlow: OpenMMLab optical flow toolbox and benchmark.

GitHub

View Github