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The OpenMMLab team released a new generation of training engine MMEngine at the World Artificial Intelligence Conference on September 1, 2022. It is a foundational library for training deep learning models. Compared with MMCV, it provides a universal and powerful runner, an open architecture with a more unified interface, and a more customizable training process.

At the same time, MMCV released 2.x release candidate version and will release 2.x official version on January 1, 2023.

In version 2.x, it removed components related to the training process and added a data transformation module. Also, starting from 2.x, it renamed the package names mmcv to mmcv-lite and mmcv-full to mmcv. For details, see Compatibility Documentation.

MMCV will maintain both 1.x and 2.x versions. For details, see Branch Maintenance Plan.


MMCV is a foundational library for computer vision research and it provides the following functionalities:

It supports the following systems:

  • Linux
  • Windows
  • macOS

See the documentation for more features and usage.

Note: MMCV requires Python 3.6+.


There are two versions of MMCV:

  • mmcv-full: comprehensive, with full features and various CPU and CUDA ops out of the box. It takes longer time to build.
  • mmcv: lite, without CPU and CUDA ops but all other features, similar to mmcv<1.0.0. It is useful when you do not need those CUDA ops.

Note: Do not install both versions in the same environment, otherwise you may encounter errors like ModuleNotFound. You need to uninstall one before installing the other. Installing the full version is highly recommended if CUDA is available.

Install mmcv-full

Before installing mmcv-full, make sure that PyTorch has been successfully installed following the PyTorch official installation guide.

The command to install mmcv-full:

pip install -U openmim
mim install mmcv-full

If you need to specify the version of mmcv-full, you can use the following command:

mim install mmcv-full==1.7.0

If you find that the above installation command does not use a pre-built package ending with .whl but a source package ending with .tar.gz, you may not have a pre-build package corresponding to the PyTorch or CUDA or mmcv-full version, in which case you can build mmcv-full from source.

Installation log using pre-built packages

Looking in links: https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html
Collecting mmcv-full
Downloading https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/mmcv_full-1.6.1-cp38-cp38-manylinux1_x86_64.whl

Installation log using source packages

Looking in links: https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html
Collecting mmcv-full==1.6.0
Downloading mmcv-full-1.6.0.tar.gz

For more installation methods, please refer to the Installation documentation.

Install mmcv

If you need to use PyTorch-related modules, make sure PyTorch has been successfully installed in your environment by referring to the PyTorch official installation guide.

pip install -U openmim
mim install mmcv

Branch Maintenance Plan

MMCV currently has two branches, the master and 2.x branches, which go through the following three phases.

Phase Time Branch description
RC Period 2022/9/1 - 2022.12.31 Release candidate code (2.x version) will be released on 2.x branch. Default master branch is still 1.x version Master and 2.x branches iterate normally
Compatibility Period 2023/1/1 - 2023.12.31 Default master branch will be switched to 2.x branch, and 1.x branch will correspond to 1.x version We still maintain the old version 1.x, respond to user needs, but try not to introduce changes that break compatibility; master branch iterates normally
Maintenance Period From 2024/1/1 Default master branch corresponds to 2.x version and 1.x branch is 1.x version 1.x branch is in maintenance phase, no more new feature support; master branch is iterating normally

Supported projects

  • 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.


If you face installation problems or runtime issues, you may first refer to this Frequently Asked Questions to see if there is a solution. If the problem is still not solved, feel free to open an issue.


If you find this project useful in your research, please consider cite:

    title={{MMCV: OpenMMLab} Computer Vision Foundation},
    author={MMCV Contributors},
    howpublished = {\url{https://github.com/open-mmlab/mmcv}},


We appreciate all contributions to improve MMCV. Please refer to CONTRIBUTING.md for the contributing guideline.


MMCV is released under the Apache 2.0 license, while some specific operations 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.


面向计算机视觉的基础库,支持 Linux、Windows 以及 MacOS 平台。它提供了众多功能,包括基于 PyTorch 的通用训练框架、高质量实现的常见 CUDA 算子、通用的 IO 接口、图像和视频处理、图像和标注结果可视化、多种 CNN 网络结构等功能、常用小工具(进度条,计时器等)。 expand collapse
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