Metadata-Version: 2.1
Name: mmsegmentation
Version: 1.0.0rc3
Summary: Open MMLab Semantic Segmentation Toolbox and Benchmark
Home-page: http://github.com/open-mmlab/mmsegmentation
Author: MMSegmentation Contributors
Author-email: openmmlab@gmail.com
License: Apache License 2.0
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        Documentation: <https://mmsegmentation.readthedocs.io/en/1.x/>
        
        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 1.x branch works with **PyTorch 1.6+**.
        
        ![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.
        
        ## What's New
        
        v1.0.0rc3 was released in 31/12/2022.
        Please refer to [changelog.md](docs/en/notes/changelog.md) for details and release history.
        
        - Support test time augmentation ([#2184](https://github.com/open-mmlab/mmsegmentation/pull/2184))
        - Add 'Projects/' folder and the first example project ([#2412](https://github.com/open-mmlab/mmsegmentation/pull/2412))
        
        ## Installation
        
        Please refer to [get_started.md](docs/en/get_started.md#installation) for installation and [dataset_prepare.md](docs/en/user_guides/2_dataset_prepare.md#prepare-datasets) for dataset preparation.
        
        ## Get Started
        
        Please see [Overview](docs/en/overview.md) for the general introduction of MMSegmentation.
        
        Please see [user guides](https://mmsegmentation.readthedocs.io/en/1.x/user_guides/index.html#) for the basic usage of MMSegmentation.
        There are also [advanced tutorials](https://mmsegmentation.readthedocs.io/en/dev-1.x/advanced_guides/index.html) for in-depth understanding of mmseg design and implementation .
        
        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/1.x/demo/MMSegmentation_Tutorial.ipynb) on Colab.
        
        To migrate from MMSegmentation 1.x, please refer to [migration](docs/en/migration.md).
        
        ## Benchmark and model zoo
        
        Results and models are available in the [model zoo](docs/en/model_zoo.md).
        
        Supported backbones:
        
        - [x] ResNet (CVPR'2016)
        - [x] ResNeXt (CVPR'2017)
        - [x] [HRNet (CVPR'2019)](configs/hrnet)
        - [x] [ResNeSt (ArXiv'2020)](configs/resnest)
        - [x] [MobileNetV2 (CVPR'2018)](configs/mobilenet_v2)
        - [x] [MobileNetV3 (ICCV'2019)](configs/mobilenet_v3)
        - [x] [Vision Transformer (ICLR'2021)](configs/vit)
        - [x] [Swin Transformer (ICCV'2021)](configs/swin)
        - [x] [Twins (NeurIPS'2021)](configs/twins)
        - [x] [BEiT (ICLR'2022)](configs/beit)
        - [x] [ConvNeXt (CVPR'2022)](configs/convnext)
        - [x] [MAE (CVPR'2022)](configs/mae)
        - [x] [PoolFormer (CVPR'2022)](configs/poolformer)
        
        Supported methods:
        
        - [x] [FCN (CVPR'2015/TPAMI'2017)](configs/fcn)
        - [x] [ERFNet (T-ITS'2017)](configs/erfnet)
        - [x] [UNet (MICCAI'2016/Nat. Methods'2019)](configs/unet)
        - [x] [PSPNet (CVPR'2017)](configs/pspnet)
        - [x] [DeepLabV3 (ArXiv'2017)](configs/deeplabv3)
        - [x] [BiSeNetV1 (ECCV'2018)](configs/bisenetv1)
        - [x] [PSANet (ECCV'2018)](configs/psanet)
        - [x] [DeepLabV3+ (CVPR'2018)](configs/deeplabv3plus)
        - [x] [UPerNet (ECCV'2018)](configs/upernet)
        - [x] [ICNet (ECCV'2018)](configs/icnet)
        - [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] [FastFCN (ArXiv'2019)](configs/fastfcn)
        - [x] [Fast-SCNN (ArXiv'2019)](configs/fastscnn)
        - [x] [ISANet (ArXiv'2019/IJCV'2021)](configs/isanet)
        - [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)
        - [x] [BiSeNetV2 (IJCV'2021)](configs/bisenetv2)
        - [x] [STDC (CVPR'2021)](configs/stdc)
        - [x] [SETR (CVPR'2021)](configs/setr)
        - [x] [DPT (ArXiv'2021)](configs/dpt)
        - [x] [Segmenter (ICCV'2021)](configs/segmenter)
        - [x] [SegFormer (NeurIPS'2021)](configs/segformer)
        - [x] [K-Net (NeurIPS'2021)](configs/knet)
        - [x] [MaskFormer (NeurIPS'2021)](configs/maskformer)
        - [x] [Mask2Former (CVPR'2022)](configs/mask2former)
        
        Supported datasets:
        
        - [x] [Cityscapes](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/user_guides/2_dataset_prepare.md#cityscapes)
        - [x] [PASCAL VOC](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/user_guides/2_dataset_prepare.md#pascal-voc)
        - [x] [ADE20K](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/user_guides/2_dataset_prepare.md#ade20k)
        - [x] [Pascal Context](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/user_guides/2_dataset_prepare.md#pascal-context)
        - [x] [COCO-Stuff 10k](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/user_guides/2_dataset_prepare.md#coco-stuff-10k)
        - [x] [COCO-Stuff 164k](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/user_guides/2_dataset_prepare.md#coco-stuff-164k)
        - [x] [CHASE_DB1](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/user_guides/2_dataset_prepare.md#chase-db1)
        - [x] [DRIVE](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/user_guides/2_dataset_prepare.md#drive)
        - [x] [HRF](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/user_guides/2_dataset_prepare.md#hrf)
        - [x] [STARE](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/user_guides/2_dataset_prepare.md#stare)
        - [x] [Dark Zurich](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/user_guides/2_dataset_prepare.md#dark-zurich)
        - [x] [Nighttime Driving](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/user_guides/2_dataset_prepare.md#nighttime-driving)
        - [x] [LoveDA](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/user_guides/2_dataset_prepare.md#loveda)
        - [x] [Potsdam](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/user_guides/2_dataset_prepare.md#isprs-potsdam)
        - [x] [Vaihingen](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/user_guides/2_dataset_prepare.md#isprs-vaihingen)
        - [x] [iSAID](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/user_guides/2_dataset_prepare.md#isaid)
        
        Please refer to [FAQ](docs/en/notes/faq.md) for frequently asked questions.
        
        ## 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.
        
        ## Citation
        
        If you find this project useful in your research, please consider cite:
        
        ```bibtex
        @misc{mmseg2020,
            title={{MMSegmentation}: OpenMMLab Semantic Segmentation Toolbox and Benchmark},
            author={MMSegmentation Contributors},
            howpublished = {\url{https://github.com/open-mmlab/mmsegmentation}},
            year={2020}
        }
        ```
        
        ## License
        
        This project is released under the [Apache 2.0 license](LICENSE).
        
        ## Projects in OpenMMLab
        
        - [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab foundational library for training deep learning models
        - [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
        - [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
        - [MMEval](https://github.com/open-mmlab/mmeval): A unified evaluation library for multiple machine learning libraries.
        - [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.
        - [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series 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,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
Classifier: Programming Language :: Python :: 3.9
Description-Content-Type: text/markdown
Provides-Extra: all
Provides-Extra: tests
Provides-Extra: optional
Provides-Extra: mim
