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| <div align="center"> | ||
| <img src="resources/mmseg-logo.png" width="600"/> | ||
| <div> </div> | ||
| <div align="center"> | ||
| <b><font size="5">OpenMMLab website</font></b> | ||
| <sup> | ||
| <a href="https://openmmlab.com"> | ||
| <i><font size="4">HOT</font></i> | ||
| </a> | ||
| </sup> | ||
| | ||
| <b><font size="5">OpenMMLab platform</font></b> | ||
| <sup> | ||
| <a href="https://platform.openmmlab.com"> | ||
| <i><font size="4">TRY IT OUT</font></i> | ||
| </a> | ||
| </sup> | ||
| </div> | ||
| <div> </div> | ||
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| <br /> | ||
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| [](https://pypi.org/project/mmsegmentation/) | ||
| [](https://pypi.org/project/mmsegmentation) | ||
| [](https://mmsegmentation.readthedocs.io/en/latest/) | ||
| [](https://github.com/open-mmlab/mmsegmentation/actions) | ||
| [](https://codecov.io/gh/open-mmlab/mmsegmentation) | ||
| [](https://github.com/open-mmlab/mmsegmentation/blob/master/LICENSE) | ||
| [](https://github.com/open-mmlab/mmsegmentation/issues) | ||
| [](https://github.com/open-mmlab/mmsegmentation/issues) | ||
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| [📘Documentation](https://mmsegmentation.readthedocs.io/en/latest/) | | ||
| [🛠️Installation](https://mmsegmentation.readthedocs.io/en/latest/get_started.html) | | ||
| [👀Model Zoo](https://mmsegmentation.readthedocs.io/en/latest/model_zoo.html) | | ||
| [🆕Update News](https://mmsegmentation.readthedocs.io/en/latest/changelog.html) | | ||
| [🤔Reporting Issues](https://github.com/open-mmlab/mmsegmentation/issues/new/choose) | ||
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| </div> | ||
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| <div align="center"> | ||
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| English | [简体中文](README_zh-CN.md) | ||
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| </div> | ||
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| ## Introduction | ||
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| MMSegmentation is an open source semantic segmentation toolbox based on PyTorch. | ||
| It is a part of the [OpenMMLab](https://openmmlab.com/) project. | ||
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| The master branch works with **PyTorch 1.5+**. | ||
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|  | ||
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| <details open> | ||
| <summary>Major features</summary> | ||
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| - **Unified Benchmark** | ||
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| We provide a unified benchmark toolbox for various semantic segmentation methods. | ||
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| - **Modular Design** | ||
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| We decompose the semantic segmentation framework into different components and one can easily construct a customized semantic segmentation framework by combining different modules. | ||
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| - **Support of multiple methods out of box** | ||
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| The toolbox directly supports popular and contemporary semantic segmentation frameworks, *e.g.* PSPNet, DeepLabV3, PSANet, DeepLabV3+, etc. | ||
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| - **High efficiency** | ||
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| The training speed is faster than or comparable to other codebases. | ||
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| </details> | ||
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| ## What's New | ||
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| ### 💎 Stable version | ||
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| v0.30.0 was released on 01/11/2023: | ||
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| - Add 'Projects/' folder, and the first example project | ||
| - Support Delving into High-Quality Synthetic Face Occlusion Segmentation Datasets | ||
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| Please refer to [changelog.md](docs/en/changelog.md) for details and release history. | ||
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| ### 🌟 Preview of 1.x version | ||
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| A brand new version of **MMSegmentation v1.0.0rc3** was released in 12/31/2022: | ||
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| - Unifies interfaces of all components based on [MMEngine](https://github.com/open-mmlab/mmengine). | ||
| - Faster training and testing speed with complete support of mixed precision training. | ||
| - Refactored and more flexible [architecture](https://mmsegmentation.readthedocs.io/en/1.x/overview.html). | ||
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| Find more new features in [1.x branch](https://github.com/open-mmlab/mmsegmentation/tree/1.x). Issues and PRs are welcome! | ||
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| ## Installation | ||
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| Please refer to [get_started.md](docs/en/get_started.md#installation) for installation and [dataset_prepare.md](docs/en/dataset_prepare.md#prepare-datasets) for dataset preparation. | ||
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| ## Get Started | ||
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| Please see [train.md](docs/en/train.md) and [inference.md](docs/en/inference.md) for the basic usage of MMSegmentation. | ||
| There are also tutorials for: | ||
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| - [customizing dataset](docs/en/tutorials/customize_datasets.md) | ||
| - [designing data pipeline](docs/en/tutorials/data_pipeline.md) | ||
| - [customizing modules](docs/en/tutorials/customize_models.md) | ||
| - [customizing runtime](docs/en/tutorials/customize_runtime.md) | ||
| - [training tricks](docs/en/tutorials/training_tricks.md) | ||
| - [useful tools](docs/en/useful_tools.md) | ||
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| 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. | ||
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| ## Benchmark and model zoo | ||
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| Results and models are available in the [model zoo](docs/en/model_zoo.md). | ||
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| Supported backbones: | ||
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| - [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) | ||
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| Supported methods: | ||
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| - [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) | ||
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| Supported datasets: | ||
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| - [x] [Cityscapes](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#cityscapes) | ||
| - [x] [PASCAL VOC](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#pascal-voc) | ||
| - [x] [ADE20K](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#ade20k) | ||
| - [x] [Pascal Context](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#pascal-context) | ||
| - [x] [COCO-Stuff 10k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#coco-stuff-10k) | ||
| - [x] [COCO-Stuff 164k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#coco-stuff-164k) | ||
| - [x] [CHASE_DB1](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#chase-db1) | ||
| - [x] [DRIVE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#drive) | ||
| - [x] [HRF](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#hrf) | ||
| - [x] [STARE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#stare) | ||
| - [x] [Dark Zurich](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#dark-zurich) | ||
| - [x] [Nighttime Driving](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#nighttime-driving) | ||
| - [x] [LoveDA](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#loveda) | ||
| - [x] [Potsdam](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#isprs-potsdam) | ||
| - [x] [Vaihingen](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#isprs-vaihingen) | ||
| - [x] [iSAID](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#isaid) | ||
| - [x] [High quality synthetic face occlusion](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#delving-into-high-quality-synthetic-face-occlusion-segmentation-datasets) | ||
| - [x] [ImageNetS](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#imagenets) | ||
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| ## FAQ | ||
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| Please refer to [FAQ](docs/en/faq.md) for frequently asked questions. | ||
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| ## Contributing | ||
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| We appreciate all contributions to improve MMSegmentation. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline. | ||
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| ## Acknowledgement | ||
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| 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. | ||
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| ## Citation | ||
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| If you find this project useful in your research, please consider cite: | ||
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| ```bibtex | ||
| @misc{mmseg2020, | ||
| title={{MMSegmentation}: OpenMMLab Semantic Segmentation Toolbox and Benchmark}, | ||
| author={MMSegmentation Contributors}, | ||
| howpublished = {\url{https://github.com/open-mmlab/mmsegmentation}}, | ||
| year={2020} | ||
| } | ||
| ``` | ||
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| ## License | ||
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| MMSegmentation is released under the Apache 2.0 license, while some specific features in this library are with other licenses. Please refer to [LICENSES.md](LICENSES.md) for the careful check, if you are using our code for commercial matters. | ||
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| ## Projects in OpenMMLab | ||
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| - [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. | ||
| - [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series toolbox and benchmark. | ||
| - [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. |
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