dazory / mmsegmentation

OpenMMLab Semantic Segmentation Toolbox and Benchmark.

 

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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 master branch works with PyTorch 1.5+.

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

v0.26.0 was released in 7/1/2022:

• Update New SegFormer models on ADE20K
• Dedicated MMSegWandbHook for MMSegmentation to use wandb visualization tool

Please refer to changelog.md for details and release history.

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
• customizing runtime
• training tricks
• useful tools

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

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

FAQ

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

MMSegmentation is released under the Apache 2.0 license, while some specific features in this library are with other licenses. Please refer to LICENSES.md for the careful check, if you are using our code for commercial matters.

Projects in OpenMMLab

• 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.