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## Introduction
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MMCV is a foundational library for computer vision research and supports many
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research projects as below:
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- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
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- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
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- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
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- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
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- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
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- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
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- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
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- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
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- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
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- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
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- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
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- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
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- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
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- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
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- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
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- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
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- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
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- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.
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It provides the following functionalities.
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- Universal IO APIs
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- Image/Video processing
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- Image and annotation visualization
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- Useful utilities (progress bar, timer, ...)
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- PyTorch runner with hooking mechanism
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- Various CNN architectures
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- High-quality implementation of common CUDA ops
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It supports the following systems.
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- Linux
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- Windows
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- macOS
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2021-08-22 09:44:51 +08:00
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```{note}
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MMCV requires Python 3.6+.
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```
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