114 lines
4.1 KiB
Markdown
114 lines
4.1 KiB
Markdown
<div align="center">
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<img src="resources/mmseg-logo.png" width="600"/>
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</div>
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<br />
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[](https://pypi.org/project/mmsegmentation)
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[](https://mmsegmentation.readthedocs.io/en/latest/)
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[](https://github.com/open-mmlab/mmsegmentation/actions)
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[](https://codecov.io/gh/open-mmlab/mmsegmentation)
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[](https://github.com/open-mmlab/mmsegmentation/blob/master/LICENSE)
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Documentation: https://mmsegmentation.readthedocs.io/
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## Introduction
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MMSegmentation is an open source semantic segmentation toolbox based on PyTorch.
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It is a part of the OpenMMLab project.
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The master branch works with **PyTorch 1.3 to 1.5**.
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### Major features
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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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## License
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This project is released under the [Apache 2.0 license](LICENSE).
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## Changelog
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v0.5.0 was released in 10/7/2020.
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## Benchmark and model zoo
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Results and models are available in the [model zoo](docs/model_zoo.md).
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Supported backbones:
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- [x] ResNet
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- [x] ResNeXt
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- [x] HRNet
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Supported methods:
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- [x] [FCN](configs/fcn)
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- [x] [PSPNet](configs/pspnet)
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- [x] [DeepLabV3](configs/deeplabv3)
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- [x] [PSANet](configs/psanet)
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- [x] [DeepLabV3+](configs/deeplabv3plus)
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- [x] [UPerNet](configs/upernet)
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- [x] [NonLocal Net](configs/nonlocal_net)
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- [x] [EncNet](configs/encnet)
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- [x] [CCNet](configs/ccnet)
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- [x] [DANet](configs/danet)
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- [x] [GCNet](configs/gcnet)
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- [x] [ANN](configs/ann)
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- [x] [OCRNet](configs/ocrnet)
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- [x] [Mixed Precision (FP16) Training](configs/fp16/README.md)
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## Installation
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Please refer to [INSTALL.md](docs/install.md) for installation and dataset preparation.
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## Get Started
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Please see [getting_started.md](docs/getting_started.md) for the basic usage of MMSegmentation.
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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).
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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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## 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.
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We wish that the toolbox and benchmark could serve the growing research
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community by providing a flexible as well as standardized toolkit to reimplement existing methods
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and develop their own new semantic segmentation methods.
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Many thanks to Ruobing Han ([@drcut](https://github.com/drcut)), Xiaoming Ma([@aishangmaxiaoming](https://github.com/aishangmaxiaoming)), Shiguang Wang ([@sunnyxiaohu](https://github.com/sunnyxiaohu)) for deployment support.
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## Citation
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If you use this toolbox or benchmark in your research, please cite this project.
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```
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@misc{mmseg2020,
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author={Xu, Jiarui and Chen, Kai and Lin, Dahua},
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title={{MMSegmenation}},
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howpublished={\url{https://github.com/open-mmlab/mmsegmentation}},
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year={2020}
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}
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```
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## Contact
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This repo is currently maintained by Jiarui Xu ([@xvjiarui](https://github.com/xvjiarui)), Kai Chen ([@hellock](http://github.com/hellock)).
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