OpenMMLab Optical flow Toolbox and Benchmark
Project description
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+.
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.
License
This project is released under the Apache 2.0 license.
Changelog
0.2.0 was released in 07/01/2022:
- Support GMA: Learning to Estimate Hidden Motions with Global Motion Aggregation (ICCV 2021)
- Fix the bug of wrong refine iter in RAFT, and update RAFT model checkpoint after the bug fixing
- Support resuming from the latest checkpoint automatically
Please refer to changelog.md for details and release history.
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)
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 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}
}
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- MMDeploy: OpenMMLab Model Deployment Framework.
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