* [CI] Fix CI * fix wrong command * remove mmcv * fix lint error * add pytorch install * fix pytorch installation * fix mmengine error * fix mmcv installation in pr_stage * fix docstring coverage in lint and delete cu102 in pr_stage windows * fix lint.yml and reset test.yml * ignore some ut in build_windows of pr_stage * test merge stage test * fix mmseg dependencies in pr_stage_test.yml * delete redundant lines in pr_stage and fix mmseg dependencies in mr_stage * fix error in merge_stage * delete python -m in merge_stage * fix error in merge_stage * let mmcv installation before mmengine * fix error of mmcv not found * fix ut error in merge)stage_test.yml * fix build_windows ut in metge_stage * fix error * fix windows error of merge_stag * Update .github/workflows/merge_stage_test.yml * Update .github/workflows/merge_stage_test.yml * Update .github/workflows/merge_stage_test.yml * fix error * delete skip timm ut * add requitements/optinal.txt in test.yml * Update .github/workflows/merge_stage_test.yml Co-authored-by: Miao Zheng <76149310+MeowZheng@users.noreply.github.com>
Documentation: https://mmsegmentation.readthedocs.io/en/1.x/
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Introduction
MMSegmentation is an open source semantic segmentation toolbox based on PyTorch. It is a part of the OpenMMLab project.
The 1.x branch works with PyTorch 1.6+.
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.
What's New
v1.0.0rc0 was released in 31/8/2022. Please refer to changelog.md for details and release history.
- Unifies interfaces of all components based on MMEngine.
- Faster training and testing speed with complete support of mixed precision training.
- Refactored and more flexible architecture.
Installation
Please refer to get_started.md for installation and dataset_prepare.md for dataset preparation.
Get Started
Please see Overview for the general introduction of MMSegmentation.
Please see user guides for the basic usage of MMSegmentation. There are also advanced tutorials for in-depth understanding of mmseg design and implementation .
A Colab tutorial is also provided. You may preview the notebook here or directly run on Colab.
To migrate from MMSegmentation 1.x, please refer to migration.
Benchmark and model zoo
Results and models are available in the model zoo.
Supported backbones:
- ResNet (CVPR'2016)
- ResNeXt (CVPR'2017)
- HRNet (CVPR'2019)
- ResNeSt (ArXiv'2020)
- MobileNetV2 (CVPR'2018)
- MobileNetV3 (ICCV'2019)
- Vision Transformer (ICLR'2021)
- Swin Transformer (ICCV'2021)
- Twins (NeurIPS'2021)
- BEiT (ICLR'2022)
- ConvNeXt (CVPR'2022)
- MAE (CVPR'2022)
Supported methods:
- FCN (CVPR'2015/TPAMI'2017)
- ERFNet (T-ITS'2017)
- UNet (MICCAI'2016/Nat. Methods'2019)
- PSPNet (CVPR'2017)
- DeepLabV3 (ArXiv'2017)
- BiSeNetV1 (ECCV'2018)
- PSANet (ECCV'2018)
- DeepLabV3+ (CVPR'2018)
- UPerNet (ECCV'2018)
- ICNet (ECCV'2018)
- NonLocal Net (CVPR'2018)
- EncNet (CVPR'2018)
- Semantic FPN (CVPR'2019)
- DANet (CVPR'2019)
- APCNet (CVPR'2019)
- EMANet (ICCV'2019)
- CCNet (ICCV'2019)
- DMNet (ICCV'2019)
- ANN (ICCV'2019)
- GCNet (ICCVW'2019/TPAMI'2020)
- FastFCN (ArXiv'2019)
- Fast-SCNN (ArXiv'2019)
- ISANet (ArXiv'2019/IJCV'2021)
- OCRNet (ECCV'2020)
- DNLNet (ECCV'2020)
- PointRend (CVPR'2020)
- CGNet (TIP'2020)
- BiSeNetV2 (IJCV'2021)
- STDC (CVPR'2021)
- SETR (CVPR'2021)
- DPT (ArXiv'2021)
- Segmenter (ICCV'2021)
- SegFormer (NeurIPS'2021)
- K-Net (NeurIPS'2021)
Supported datasets:
- Cityscapes
- PASCAL VOC
- ADE20K
- Pascal Context
- COCO-Stuff 10k
- COCO-Stuff 164k
- CHASE_DB1
- DRIVE
- HRF
- STARE
- Dark Zurich
- Nighttime Driving
- LoveDA
- Potsdam
- Vaihingen
- iSAID
Please refer to FAQ for frequently asked questions.
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.
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}
}
License
This project is released under the Apache 2.0 license.
Projects in OpenMMLab
- MMEngine: OpenMMLab foundational library for training deep learning models
- MMCV: OpenMMLab foundational library for computer vision.
- MIM: MIM installs OpenMMLab packages.
- MMClassification: OpenMMLab image classification toolbox and benchmark.
- MMDetection: OpenMMLab detection toolbox and benchmark.
- MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
- MMRotate: OpenMMLab rotated object detection toolbox and benchmark.
- MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
- MMOCR: OpenMMLab text detection, recognition, and understanding toolbox.
- MMPose: OpenMMLab pose estimation 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.
- MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.
- MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
- MMTracking: OpenMMLab video perception toolbox and benchmark.
- MMFlow: OpenMMLab optical flow toolbox and benchmark.
- MMEditing: OpenMMLab image and video editing toolbox.
- MMGeneration: OpenMMLab image and video generative models toolbox.
- MMDeploy: OpenMMLab Model Deployment Framework.