Package: mmsegmentation-zzb
OpenMMLab websiteHOTOpenMMLab platformTRY IT OUTDocumentation:https://mmsegmentation.readthedocs.io/English |简体中文IntroductionMMSegmentation is an open source semantic segmentation toolbox based on PyTorch.
It is a part of the OpenMMLab project.The master branch works withPyTorch 1.5+.Major featuresUnified BenchmarkWe provide a unified benchmark toolbox for various semantic segmentation methods.Modular DesignWe 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 boxThe toolbox directly supports popular and contemporary semantic segmentation frameworks,e.g.PSPNet, DeepLabV3, PSANet, DeepLabV3+, etc.High efficiencyThe training speed is faster than or comparable to other codebases.LicenseThis project is released under theApache 2.0 license.Changelogv0.23.0 was released in 4/1/2022.
Please refer tochangelog.mdfor details and release history.Benchmark and model zooResults and models are available in themodel 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)ConvNeXt (CVPR'2022)BEiT (ICLR'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:CityscapesPASCAL VOCADE20KPascal ContextCOCO-Stuff 10kCOCO-Stuff 164kCHASE_DB1DRIVEHRFSTAREDark ZurichNighttime DrivingLoveDAPotsdamVaihingeniSAIDInstallationPlease refer toget_started.mdfor installation anddataset_prepare.mdfor dataset preparation.Get StartedPlease seetrain.mdandinference.mdfor the basic usage of MMSegmentation.
There are also tutorials forcustomizing dataset,designing data pipeline,customizing modules, andcustomizing runtime.
We also provide manytraining tricksfor better training anduseful toolsfor deployment.A Colab tutorial is also provided. You may preview the notebookhereor directlyrunon Colab.Please refer toFAQfor frequently asked questions.CitationIf 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}}ContributingWe appreciate all contributions to improve MMSegmentation. Please refer toCONTRIBUTING.mdfor the contributing guideline.AcknowledgementMMSegmentation 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 OpenMMLabMMCV: 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.