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Introduction

MMCV is a foundational library for computer vision research and supports many research projects as below:

  • 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.

It provides the following functionalities.

  • Universal IO APIs

  • Image/Video processing

  • Image and annotation visualization

  • Useful utilities (progress bar, timer, …)

  • PyTorch runner with hooking mechanism

  • Various CNN architectures

  • High-quality implementation of common CUDA ops

It supports the following systems.

  • Linux

  • Windows

  • macOS

Note

MMCV requires Python 3.6+.

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