142 lines
6.6 KiB
Markdown
142 lines
6.6 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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[](https://github.com/open-mmlab/mmsegmentation/issues)
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[](https://github.com/open-mmlab/mmsegmentation/issues)
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Documentation: https://mmsegmentation.readthedocs.io/
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English | [简体中文](README_zh-CN.md)
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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+**.
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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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v0.14.1 was released in 06/16/2021.
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Please refer to [changelog.md](docs/changelog.md) for details and release history.
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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 (CVPR'2016)
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- [x] ResNeXt (CVPR'2017)
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- [x] [HRNet (CVPR'2019)](configs/hrnet/README.md)
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- [x] [ResNeSt (ArXiv'2020)](configs/resnest/README.md)
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- [x] [MobileNetV2 (CVPR'2018)](configs/mobilenet_v2/README.md)
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- [x] [MobileNetV3 (ICCV'2019)](configs/mobilenet_v3/README.md)
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Supported methods:
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- [x] [FCN (CVPR'2015/TPAMI'2017)](configs/fcn)
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- [x] [UNet (MICCAI'2016/Nat. Methods'2019)](configs/unet)
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- [x] [PSPNet (CVPR'2017)](configs/pspnet)
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- [x] [DeepLabV3 (ArXiv'2017)](configs/deeplabv3)
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- [x] [Mixed Precision (FP16) Training (ArXiv'2017)](configs/fp16/README.md)
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- [x] [PSANet (ECCV'2018)](configs/psanet)
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- [x] [DeepLabV3+ (CVPR'2018)](configs/deeplabv3plus)
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- [x] [UPerNet (ECCV'2018)](configs/upernet)
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- [x] [NonLocal Net (CVPR'2018)](configs/nonlocal_net)
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- [x] [EncNet (CVPR'2018)](configs/encnet)
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- [x] [Semantic FPN (CVPR'2019)](configs/sem_fpn)
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- [x] [DANet (CVPR'2019)](configs/danet)
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- [x] [APCNet (CVPR'2019)](configs/apcnet)
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- [x] [EMANet (ICCV'2019)](configs/emanet)
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- [x] [CCNet (ICCV'2019)](configs/ccnet)
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- [x] [DMNet (ICCV'2019)](configs/dmnet)
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- [x] [ANN (ICCV'2019)](configs/ann)
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- [x] [GCNet (ICCVW'2019/TPAMI'2020)](configs/gcnet)
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- [x] [Fast-SCNN (ArXiv'2019)](configs/fastscnn)
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- [x] [OCRNet (ECCV'2020)](configs/ocrnet)
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- [x] [DNLNet (ECCV'2020)](configs/dnlnet)
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- [x] [PointRend (CVPR'2020)](configs/point_rend)
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- [x] [CGNet (TIP'2020)](configs/cgnet)
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## Installation
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Please refer to [get_started.md](docs/get_started.md#installation) for installation and [dataset_prepare.md](docs/dataset_prepare.md#prepare-datasets) for dataset preparation.
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## Get Started
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Please see [train.md](docs/train.md) and [inference.md](docs/inference.md) for the basic usage of MMSegmentation.
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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).
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We also provide many [training tricks](docs/tutorials/training_tricks.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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## Citation
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If you find this project useful in your research, please consider cite:
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```latex
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@misc{mmseg2020,
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title={{MMSegmentation}: OpenMMLab Semantic Segmentation Toolbox and Benchmark},
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author={MMSegmentation Contributors},
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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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## 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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## Projects in OpenMMLab
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- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
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- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
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- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
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- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
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- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
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- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
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- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
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- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
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- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
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- [MMOCR](https://github.com/open-mmlab/mmocr): A Comprehensive Toolbox for Text Detection, Recognition and Understanding.
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- [MMGeneration](https://github.com/open-mmlab/mmgeneration): A powerful toolkit for generative models.
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