Documentation: https://mmsegmentation.readthedocs.io/
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This is the implementation for Multi-scale Prototype Contrast Network.
The trained model for iSAID can be found at the BaiduYun (password: cbn0), corresponding config is configs/mpcnet/mpc_swin_isaid.py.
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Unified Benchmark
We provide a unified benchmark toolbox for various semantic segmentation methods. This code is developed based on mmsegemetnation.
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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.
Please refer to get_started.md for installation and dataset_prepare.md for 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 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.
Please refer to FAQ for frequently asked questions.
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.