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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
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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.
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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.
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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.
v0.4.1 was released in 04/29/2022:
- Support occlusion estimation methods including flow forward-backward consistency, range map of the backward flow, and flow forward-backward abstract difference
Please refer to changelog.md for details and release history.
Please refer to install.md for installation and guidance in dataset_prepare for dataset preparation.
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:
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)
We appreciate all contributions improving MMFlow. Please refer to CONTRIBUTING.md in MMCV for more details about the contributing guideline.
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.
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}
}This project is released under the Apache 2.0 license.
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