OpenMMLab website ^HOT      OpenMMLab platform ^{TRY IT OUT}

 

📘Documentation | 🛠️Installation | 🤔Reporting Issues

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.

MMCV v2.0.0 official version was released on April 6, 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 (corresponding to the original master branch) and 2.x (corresponding to the main branch, now the default branch) versions simultaneously. For details, see Branch Maintenance Plan.

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

• Image/Video processing
• Image and annotation visualization
• Image transformation
• Various CNN architectures
• High-quality implementation of common CPU and CUDA ops

It supports the following systems:

• Linux
• Windows
• macOS

See the documentation for more features and usage.

Note: MMCV requires Python 3.7+.

There are two versions of MMCV:

• mmcv: comprehensive, with full features and various CUDA ops out of the box. It takes longer time to build.
• mmcv-lite: lite, without 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.

Before installing mmcv, make sure that PyTorch has been successfully installed following the PyTorch official installation guide. For apple silicon users, please use PyTorch 1.13+.

The command to install mmcv:

pip install -U openmim
mim install mmcv

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

mim install mmcv==2.0.0

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 version, in which case you can build mmcv from source.

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

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-lite

If you face some installation issues, CUDA related issues or RuntimeErrors, you may first refer to this Frequently Asked Questions.

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:

@misc{mmcv,
    title={{MMCV: OpenMMLab} Computer Vision Foundation},
    author={MMCV Contributors},
    howpublished = {\url{https://github.com/open-mmlab/mmcv}},
    year={2018}
}

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.

MMCV currently has four branches, namely main, 1.x, master, and 2.x, where 2.x is an alias for the main branch, and master is an alias for the 1.x branch. The 2.x and master branches will be deleted in the future. MMCV's branches go through the following three stages:

• MMEngine: OpenMMLab foundational library for training deep learning models.
• MMCV: 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.
• MMYOLO: OpenMMLab YOLO series 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.