Metadata-Version: 2.1
Name: mmflow
Version: 0.3.0
Summary: OpenMMLab Optical flow Toolbox and Benchmark
Home-page: https://github.com/open-mmlab/mmflow
Author: MMFlow Contributors
Author-email: openmmlab@gmail.com
License: Apache License 2.0
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        Documentation: https://mmflow.readthedocs.io/
        
        ## Introduction
        
        English | [简体中文](README_zh-CN.md)
        
        MMFlow is an open source optical flow toolbox based on PyTorch. It is a part of the [OpenMMLab](https://openmmlab.com/) 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.
        
        ## License
        
        This project is released under the [Apache 2.0 license](LICENSE).
        
        ## Changelog
        
        **v0.3.0** was released in 03/04/2022:
        
        - Officially support CPU train/inference
        - Officially support model inference in windows platform
        - Add census loss, SSIM loss and smoothness loss
        - Update `nan` files in Flyingthings3d_subset dataset
        
        Please refer to [changelog.md](docs/en/changelog.md) for details and release history.
        
        ## Benchmark and model zoo
        
        Results and models are available in the [model zoo](docs/en/model_zoo.md).
        
        Supported methods:
        
        - [x] [FlowNet (ICCV'2015)](configs/flownet/README.md)
        - [x] [FlowNet2 (CVPR'2017)](configs/flownet2/README.md)
        - [x] [PWC-Net (CVPR'2018)](configs/pwcnet/README.md)
        - [x] [LiteFlowNet (CVPR'2018)](configs/liteflownet/README.md)
        - [x] [LiteFlowNet2 (TPAMI'2020)](configs/liteflownet2/README.md)
        - [x] [IRR (CVPR'2019)](configs/irr/README.md)
        - [x] [MaskFlownet (CVPR'2020)](configs/maskflownet/README.md)
        - [x] [RAFT (ECCV'2020)](configs/raft/README.md)
        - [x] [GMA (ICCV' 2021)](configs/gma/README.md)
        
        ## Installation
        
        Please refer to [install.md](docs/en/install.md) for installation and
        guidance in [dataset_prepare](docs/en/dataset_prepare.md) for dataset preparation.
        
        ## Getting Started
        
        If you're new of optical flow, you can start with [learn the basics](docs/en/intro.md). If you’re familiar with it, check out [getting_started](docs/en/getting_started.md) to try out MMFlow.
        
        Refer to the below tutorials to dive deeper:
        
        - [config](docs/en/tutorials/0_config.md)
        
        - [model inference](docs/en/tutorials/1_inference.md)
        
        - [fine tuning](docs/en/tutorials/2_finetune.md)
        
        - [data pipeline](docs/en/tutorials/3_data_pipeline.md)
        
        - [add new modules](docs/en/tutorials/4_new_modules.md)
        
        - [customized runtime](docs/en/tutorials/5_customize_runtime.md)
        
        ## Contributing
        
        We appreciate all contributions improving MMFlow. Please refer to [CONTRIBUTING.md](https://github.com/open-mmlab/mmcv/blob/master/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.
        
        ```BibTeX
        @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](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
        - [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
        - [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
        - [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
        - [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
        - [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
        - [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
        - [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
        - [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
        - [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
        - [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
        - [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
        - [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
        - [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
        - [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
        - [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
        - [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
        - [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
        - [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab Model Deployment Framework.
        
Keywords: computer vision,optical flow
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
Provides-Extra: all
Provides-Extra: tests
Provides-Extra: build
Provides-Extra: optional
