Metadata-Version: 2.1
Name: mmsegmentation
Version: 0.6.0
Summary: Open MMLab Semantic Segmentation Toolbox and Benchmark
Home-page: http://github.com/open-mmlab/mmsegmentation
Author: MMSegmentation Authors
Author-email: openmmlab@gmail.com
License: Apache License 2.0
Description: <div align="center">
          <img src="resources/mmseg-logo.png" width="600"/>
        </div>
        <br />
        
        [![PyPI](https://img.shields.io/pypi/v/mmsegmentation)](https://pypi.org/project/mmsegmentation)
        [![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmsegmentation.readthedocs.io/en/latest/)
        [![badge](https://github.com/open-mmlab/mmsegmentation/workflows/build/badge.svg)](https://github.com/open-mmlab/mmsegmentation/actions)
        [![codecov](https://codecov.io/gh/open-mmlab/mmsegmentation/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmsegmentation)
        [![license](https://img.shields.io/github/license/open-mmlab/mmsegmentation.svg)](https://github.com/open-mmlab/mmsegmentation/blob/master/LICENSE)
        
        Documentation: https://mmsegmentation.readthedocs.io/
        
        ## Introduction
        
        MMSegmentation is an open source semantic segmentation toolbox based on PyTorch.
        It is a part of the OpenMMLab project.
        
        The master branch works with **PyTorch 1.3 to 1.5**.
        
        ![demo image](resources/seg_demo.gif)
        
        ### Major features
        
        - **Unified Benchmark**
        
          We provide a unified benchmark toolbox for various semantic segmentation methods.
        
        - **Modular Design**
        
          We decompose the semantic segmentation framework into different components and one can easily construct a customized semantic segmentation framework by combining different modules.
        
        - **Support of multiple methods out of box**
        
          The toolbox directly supports popular and contemporary semantic segmentation frameworks, *e.g.* PSPNet, DeepLabV3, PSANet, DeepLabV3+, etc.
        
        - **High efficiency**
        
          The training speed is faster than or comparable to other codebases.
        
        ## License
        
        This project is released under the [Apache 2.0 license](LICENSE).
        
        ## Changelog
        
        v0.5.1 was released in 11/08/2020.
        Please refer to [changelog.md](docs/changelog.md) for details and release history.
        
        ## Benchmark and model zoo
        
        Results and models are available in the [model zoo](docs/model_zoo.md).
        
        Supported backbones:
        - [x] ResNet
        - [x] ResNeXt
        - [x] [HRNet](configs/hrnet/README.md)
        - [x] [ResNeSt](configs/resnest/README.md)
        
        Supported methods:
        - [x] [FCN](configs/fcn)
        - [x] [PSPNet](configs/pspnet)
        - [x] [DeepLabV3](configs/deeplabv3)
        - [x] [PSANet](configs/psanet)
        - [x] [DeepLabV3+](configs/deeplabv3plus)
        - [x] [UPerNet](configs/upernet)
        - [x] [NonLocal Net](configs/nonlocal_net)
        - [x] [EncNet](configs/encnet)
        - [x] [CCNet](configs/ccnet)
        - [x] [DANet](configs/danet)
        - [x] [GCNet](configs/gcnet)
        - [x] [ANN](configs/ann)
        - [x] [OCRNet](configs/ocrnet)
        - [x] [Fast-SCNN](configs/fastscnn)
        - [x] [Semantic FPN](configs/sem_fpn)
        - [x] [PointRend](configs/point_rend)
        - [x] [EMANet](configs/emanet)
        - [x] [DNLNet](configs/dnlnet)
        - [x] [Mixed Precision (FP16) Training](configs/fp16/README.md)
        
        ## Installation
        
        Please refer to [INSTALL.md](docs/install.md) for installation and dataset preparation.
        
        ## Get Started
        
        Please see [getting_started.md](docs/getting_started.md) for the basic usage of MMSegmentation.
        There are also tutorials for [adding new dataset](docs/tutorials/new_dataset.md), [designing data pipeline](docs/tutorials/data_pipeline.md), and [adding new modules](docs/tutorials/new_modules.md).
        
        A Colab tutorial is also provided. You may preview the notebook [here](demo/MMSegmentation_Tutorial.ipynb) or directly [run](https://colab.research.google.com/github/open-mmlab/mmsegmentation/blob/master/demo/MMSegmentation_Tutorial.ipynb) on Colab.
        
        ## Contributing
        
        We appreciate all contributions to improve MMSegmentation. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
        
        ## Acknowledgement
        
        MMSegmentation is an open source project that welcome any contribution and feedback.
        We wish that the toolbox and benchmark could serve the growing research
        community by providing a flexible as well as standardized toolkit to reimplement existing methods
        and develop their own new semantic segmentation methods.
        
Keywords: computer vision,semantic segmentation
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
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
