Metadata-Version: 2.1
Name: mmsegmentation
Version: 0.14.1
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 />
        
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        Documentation: https://mmsegmentation.readthedocs.io/
        
        English | [简体中文](README_zh-CN.md)
        
        ## 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+**.
        
        ![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
        
        v0.14.1 was released in 06/16/2021.
        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 (CVPR'2016)
        - [x] ResNeXt (CVPR'2017)
        - [x] [HRNet (CVPR'2019)](configs/hrnet/README.md)
        - [x] [ResNeSt (ArXiv'2020)](configs/resnest/README.md)
        - [x] [MobileNetV2 (CVPR'2018)](configs/mobilenet_v2/README.md)
        - [x] [MobileNetV3 (ICCV'2019)](configs/mobilenet_v3/README.md)
        
        Supported methods:
        
        - [x] [FCN (CVPR'2015/TPAMI'2017)](configs/fcn)
        - [x] [UNet (MICCAI'2016/Nat. Methods'2019)](configs/unet)
        - [x] [PSPNet (CVPR'2017)](configs/pspnet)
        - [x] [DeepLabV3 (ArXiv'2017)](configs/deeplabv3)
        - [x] [Mixed Precision (FP16) Training (ArXiv'2017)](configs/fp16/README.md)
        - [x] [PSANet (ECCV'2018)](configs/psanet)
        - [x] [DeepLabV3+ (CVPR'2018)](configs/deeplabv3plus)
        - [x] [UPerNet (ECCV'2018)](configs/upernet)
        - [x] [NonLocal Net (CVPR'2018)](configs/nonlocal_net)
        - [x] [EncNet (CVPR'2018)](configs/encnet)
        - [x] [Semantic FPN (CVPR'2019)](configs/sem_fpn)
        - [x] [DANet (CVPR'2019)](configs/danet)
        - [x] [APCNet (CVPR'2019)](configs/apcnet)
        - [x] [EMANet (ICCV'2019)](configs/emanet)
        - [x] [CCNet (ICCV'2019)](configs/ccnet)
        - [x] [DMNet (ICCV'2019)](configs/dmnet)
        - [x] [ANN (ICCV'2019)](configs/ann)
        - [x] [GCNet (ICCVW'2019/TPAMI'2020)](configs/gcnet)
        - [x] [Fast-SCNN (ArXiv'2019)](configs/fastscnn)
        - [x] [OCRNet (ECCV'2020)](configs/ocrnet)
        - [x] [DNLNet (ECCV'2020)](configs/dnlnet)
        - [x] [PointRend (CVPR'2020)](configs/point_rend)
        - [x] [CGNet (TIP'2020)](configs/cgnet)
        
        ## Installation
        
        Please refer to [get_started.md](docs/get_started.md#installation) for installation and dataset preparation.
        
        ## Get Started
        
        Please see [train.md](docs/train.md) and [inference.md](docs/inference.md) for the basic usage of MMSegmentation.
        There are also tutorials for [customizing dataset](docs/tutorials/customize_datasets.md), [designing data pipeline](docs/tutorials/data_pipeline.md), [customizing modules](docs/tutorials/customize_models.md), and [customizing runtime](docs/tutorials/customize_runtime.md).
        We also provide many [training tricks](docs/tutorials/training_tricks.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.
        
        ## Citation
        
        If you find this project useful in your research, please consider cite:
        
        ```latex
        @misc{mmseg2020,
            title={{MMSegmentation}: OpenMMLab Semantic Segmentation Toolbox and Benchmark},
            author={MMSegmentation Contributors},
            howpublished = {\url{https://github.com/open-mmlab/mmsegmentation}},
            year={2020}
        }
        ```
        
        ## 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.
        
        ## Projects in OpenMMLab
        
        - [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
        - [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.
        - [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation 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.
        - [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
        - [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
        - [MMOCR](https://github.com/open-mmlab/mmocr): A Comprehensive Toolbox for Text Detection, Recognition and Understanding.
        - [MMGeneration](https://github.com/open-mmlab/mmgeneration): A powerful toolkit for generative models.
        
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
