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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

v0.11.0 was released in 02/02/2021.
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
- [x] ResNeXt
- [x] [HRNet](configs/hrnet/README.md)
- [x] [ResNeSt](configs/resnest/README.md)
- [x] [MobileNetV2](configs/mobilenet_v2/README.md)
- [x] [MobileNetV3](configs/mobilenet_v3/README.md)

Supported methods:

- [x] [FCN](configs/fcn)
- [x] [PSPNet](configs/pspnet)
- [x] [DeepLabV3](configs/deeplabv3)
- [x] [PSANet](configs/psanet)
- [x] [DeepLabV3+](configs/deeplabv3plus)
- [x] [UPerNet](configs/upernet)
- [x] [NonLocal Net](configs/nonlocal_net)
- [x] [EncNet](configs/encnet)
- [x] [CCNet](configs/ccnet)
- [x] [DANet](configs/danet)
- [x] [APCNet](configs/apcnet)
- [x] [GCNet](configs/gcnet)
- [x] [DMNet](configs/dmnet)
- [x] [ANN](configs/ann)
- [x] [OCRNet](configs/ocrnet)
- [x] [Fast-SCNN](configs/fastscnn)
- [x] [Semantic FPN](configs/sem_fpn)
- [x] [PointRend](configs/point_rend)
- [x] [EMANet](configs/emanet)
- [x] [DNLNet](configs/dnlnet)
- [x] [CGNet](configs/cgnet)
- [x] [Mixed Precision (FP16) Training](configs/fp16/README.md)

## 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.