Jerry Jiarui XU 5d46314844 | 3 years ago | |
---|---|---|
.dev | 3 years ago | |
.github | 3 years ago | |
configs | 3 years ago | |
demo | 3 years ago | |
docker | 3 years ago | |
docs | 3 years ago | |
mmseg | 3 years ago | |
requirements | 3 years ago | |
resources | 3 years ago | |
tests | 3 years ago | |
tools | 3 years ago | |
.gitignore | 3 years ago | |
.pre-commit-config.yaml | 3 years ago | |
.readthedocs.yml | 3 years ago | |
LICENSE | 3 years ago | |
MANIFEST.in | 3 years ago | |
README.md | 3 years ago | |
README_zh-CN.md | 3 years ago | |
model_zoo.yml | 3 years ago | |
pytest.ini | 3 years ago | |
requirements.txt | 3 years ago | |
setup.cfg | 3 years ago | |
setup.py | 3 years ago |
Documentation: https://mmsegmentation.readthedocs.io/
English | 简体中文
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+.
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.
This project is released under the Apache 2.0 license.
v0.14.0 was released in 06/02/2021.
Please refer to changelog.md for details and release history.
Results and models are available in the model zoo.
Supported backbones:
Supported methods:
Please refer to get_started.md for installation and dataset preparation.
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.
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}
}
We appreciate all contributions to improve MMSegmentation. Please refer to CONTRIBUTING.md for the contributing guideline.
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.
No Description
Python Markdown Shell Dockerfile other
Dear OpenI User
Thank you for your continuous support to the Openl Qizhi Community AI Collaboration Platform. In order to protect your usage rights and ensure network security, we updated the Openl Qizhi Community AI Collaboration Platform Usage Agreement in January 2024. The updated agreement specifies that users are prohibited from using intranet penetration tools. After you click "Agree and continue", you can continue to use our services. Thank you for your cooperation and understanding.
For more agreement content, please refer to the《Openl Qizhi Community AI Collaboration Platform Usage Agreement》