[](https://mmcv.readthedocs.io/en/latest/)
[](https://mmcv.readthedocs.io/en/latest/get_started/installation.html)
[](https://pypi.org/project/mmcv/)
[](https://pytorch.org/get-started/previous-versions/)
[](https://developer.nvidia.com/cuda-downloads)
[](https://pypi.org/project/mmcv)
[](https://github.com/open-mmlab/mmcv/actions)
[](https://codecov.io/gh/open-mmlab/mmcv)
[](https://github.com/open-mmlab/mmcv/blob/master/LICENSE)
English | [简体中文](README_zh-CN.md)
## Highlights
The OpenMMLab team released a new generation of training engine [MMEngine](https://github.com/open-mmlab/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](https://github.com/open-mmlab/mmcv/tree/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](docs/en/compatibility.md).
MMCV will maintain both `1.x` and `2.x` versions. For details, see [Branch Maintenance Plan](README.md#branch-maintenance-plan).
## Introduction
MMCV is a foundational library for computer vision research and it provides the following functionalities:
- [Universal IO APIs](https://mmcv.readthedocs.io/en/latest/understand_mmcv/io.html)
- [Image/Video processing](https://mmcv.readthedocs.io/en/latest/understand_mmcv/data_process.html)
- [Image and annotation visualization](https://mmcv.readthedocs.io/en/latest/understand_mmcv/visualization.html)
- [Useful utilities (progress bar, timer, ...)](https://mmcv.readthedocs.io/en/latest/understand_mmcv/utils.html)
- [PyTorch runner with hooking mechanism](https://mmcv.readthedocs.io/en/latest/understand_mmcv/runner.html)
- [Various CNN architectures](https://mmcv.readthedocs.io/en/latest/understand_mmcv/cnn.html)
- [High-quality implementation of common CPU and CUDA ops](https://mmcv.readthedocs.io/en/latest/understand_mmcv/ops.html)
It supports the following systems:
- Linux
- Windows
- macOS
See the [documentation](http://mmcv.readthedocs.io/en/latest) for more features and usage.
Note: MMCV requires Python 3.6+.
## Installation
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](https://github.com/pytorch/pytorch#installation).
The command to install mmcv-full:
```bash
pip install -U openmim
mim install mmcv-full
```
If you need to specify the version of mmcv-full, you can use the following command:
```bash
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](https://mmcv.readthedocs.io/en/latest/get_started/build.html).
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](https://mmcv.readthedocs.io/en/latest/get_started/installation.html).
### 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](https://github.com/pytorch/pytorch#installation).
```bash
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](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.
## FAQ
If you face installation problems or runtime issues, you may first refer to this [Frequently Asked Questions](https://mmcv.readthedocs.io/en/latest/faq.html) to see if there is a solution. If the problem is still not solved, feel free to open an [issue](https://github.com/open-mmlab/mmcv/issues).
## Citation
If you find this project useful in your research, please consider cite:
```latex
@misc{mmcv,
title={{MMCV: OpenMMLab} Computer Vision Foundation},
author={MMCV Contributors},
howpublished = {\url{https://github.com/open-mmlab/mmcv}},
year={2018}
}
```
## Contributing
We appreciate all contributions to improve MMCV. Please refer to [CONTRIBUTING.md](CONTRIBUTING.md) for the contributing guideline.
## License
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](LICENSES.md) for the careful check, if you are using our code for commercial matters.
