You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 
tangyucong c956a5abb2 branch 4 months ago
.circleci first commit 4 months ago
.dev first commit 4 months ago
.github first commit 4 months ago
configs 注释说明 4 months ago
demo first commit 4 months ago
docker first commit 4 months ago
docs first commit 4 months ago
mmseg first commit 4 months ago
projects branch 4 months ago
requirements first commit 4 months ago
resources first commit 4 months ago
tests first commit 4 months ago
tools first commit 4 months ago
.gitignore first commit 4 months ago
.pre-commit-config.yaml first commit 4 months ago
.readthedocs.yml first commit 4 months ago
CITATION.cff first commit 4 months ago
LICENSE first commit 4 months ago
MANIFEST.in first commit 4 months ago
README.md first commit 4 months ago
README_zh-CN.md first commit 4 months ago
model-index.yml first commit 4 months ago
pytest.ini first commit 4 months ago
requirements.txt first commit 4 months ago
setup.cfg first commit 4 months ago
setup.py first commit 4 months ago

README.md

 
OpenMMLab website HOT      OpenMMLab platform TRY IT OUT
 

PyPI - Python Version PyPI docs badge codecov license issue resolution open issues

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 1.5+.

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

v0.21.0 was released in 2/9/2022. 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:

Supported datasets:

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 for better training and useful tools for deployment.

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.
  • MMFlow: OpenMMLab optical flow toolbox and benchmark.
  • MMFewShot: OpenMMLab few shot learning toolbox and benchmark.
  • MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.
  • MMSelfSup: OpenMMLab self-supervised learning toolbox and benchmark.
  • MMRazor: OpenMMLab Model Compression Toolbox and Benchmark.
  • MMDeploy: OpenMMLab Model Deployment Framework.
  • MMRotate: OpenMMLab rotated object detection toolbox and benchmark.