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OpenMMLab Semantic Segmentation Toolbox and Benchmark.
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[Feature] Add segformer decode head and related train config (#599) 
* [Feature]Segformer re-implementation

* Using act_cfg and norm_cfg to control activation and normalization

* Split this PR into several little PRs

* Fix lint error

* Remove SegFormerHead

* [Feature] Add segformer decode head and related train config

* Add ade20K trainval support for segformer

1. Add related train and val configs;

2. Add AlignedResize;

* Set arg: find_unused_parameters = True

* parameters init refactor

* 1. Refactor segformer backbone parameters init;

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* 1. Remove rebundant codes;

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README.md


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Documentation: https://mmsegmentation.readthedocs.io/

English | 简体中文

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

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.

Changelog

v0.16.0 was released in 08/04/2021. Please refer to changelog.md for details and release history.

Benchmark and model zoo

Results and models are available in the model zoo.

Supported backbones:

Supported methods:

Installation

Please refer to get_started.md for installation and dataset_prepare.md for dataset preparation.

Get Started

Please see train.md and inference.md for the basic usage of MMSegmentation. There are also tutorials for customizing dataset, designing data pipeline, customizing modules, and customizing runtime. We also provide many training tricks.

A Colab tutorial is also provided. You may preview the notebook here or directly run on Colab.

Citation

If you find this project useful in your research, please consider cite:

@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 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: OpenMMLab foundational library for computer vision.
  • MMClassification: OpenMMLab image classification toolbox and benchmark.
  • MMDetection: OpenMMLab detection toolbox and benchmark.
  • MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
  • MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
  • MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
  • MMTracking: OpenMMLab video perception toolbox and benchmark.
  • MMPose: OpenMMLab pose estimation toolbox and benchmark.
  • MMEditing: OpenMMLab image and video editing toolbox.
  • MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding.
  • MMGeneration: A powerful toolkit for generative models.
  • MIM: MIM Installs OpenMMLab Packages.