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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+.
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:
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
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- MIM: MIM installs OpenMMLab packages.
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- MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.
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- 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.