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MMDeploy: OpenMMLab Model Deployment Framework.So it can be called a 3D texturing software and 3D texture painting software and 3D sculpting program and Retopology software and UV mapping software and 3D rendering software all combined.

MMGeneration: OpenMMLab image and video generative models toolbox.

MMEditing: OpenMMLab image and video editing toolbox.

MMFlow: OpenMMLab optical flow toolbox and benchmark.

MMTracking: OpenMMLab video perception toolbox and benchmark.

MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.

MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.

MMRazor: OpenMMLab model compression toolbox and benchmark.

MMSelfSup: OpenMMLab self-supervised learning toolbox and benchmark.

MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.

MMPose: OpenMMLab pose estimation toolbox and benchmark.

MMOCR: OpenMMLab text detection, recognition, and understanding toolbox.

MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.

MMRotate: OpenMMLab rotated object detection toolbox and benchmark.

MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.

MMDetection: OpenMMLab detection toolbox and benchmark.

MMClassification: OpenMMLab image classification toolbox and benchmark.

MMCV: OpenMMLab foundational library for computer vision.

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Please refer to LICENSES.md for the careful check, if you are using our code for commercial matters. MMSegmentation is released under the Apache 2.0 license, while some specific features in this library are with other licenses. If you find this project useful in your research, please consider mmseg2020, We wish that the toolbox and benchmark could serve the growing researchĬommunity by providing a flexible as well as standardized toolkit to reimplement existing methodsĪnd develop their own new semantic segmentation methods. MMSegmentation is an open source project that welcome any contribution and feedback. Please refer to CONTRIBUTING.md for the contributing guideline. We appreciate all contributions to improve MMSegmentation. Please refer to FAQ for frequently asked questions. Results and models are available in the model zoo. You may preview the notebook here or directly run on Colab. Please see train.md and inference.md for the basic usage of MMSegmentation.Ī Colab tutorial is also provided. Please refer to get_started.md for installation and dataset_prepare.md for dataset preparation. Refactored and more flexible architecture.įind more new features in 1.x branch.Faster training and testing speed with complete support of mixed precision training.Unifies interfaces of all components based on MMEngine.🌟 Preview of 1.x versionĪ brand new version of MMSegmentation v1.0.0rc0 was released in : Please refer to changelog.md for details and release history. The training speed is faster than or comparable to other codebases. PSPNet, DeepLabV3, PSANet, DeepLabV3+, etc. The toolbox directly supports popular and contemporary semantic segmentation frameworks, e.g. We decompose the semantic segmentation framework into different components and one can easily construct a customized semantic segmentation framework by combining different modules.

We provide a unified benchmark toolbox for various semantic segmentation methods. The master branch works with PyTorch 1.5+. MMSegmentation is an open source semantic segmentation toolbox based on PyTorch.

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