-[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.
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
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 CUDA ops out of box. It takes longer time to build.
-**mmcv**: 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`.
a. Install the full version.
Before installing mmcv-full, make sure that PyTorch has been successfully installed following the [official guide](https://pytorch.org/).
We provide pre-built mmcv packages (recommended) with different PyTorch and CUDA versions to simplify the building for **Linux and Windows systems**. In addition, you can run [check_installation.py](.dev_scripts/check_installation.py) to check the installation of mmcv-full after running the installation commands.
i. Install the latest version.
The rule for installing the latest `mmcv-full` is as follows:
**Note**: mmcv-full is only compiled on PyTorch 1.x.0 because the compatibility usually holds between 1.x.0 and 1.x.1. If your PyTorch version is 1.x.1, you can install mmcv-full compiled with PyTorch 1.x.0 and it usually works well. For example, if your PyTorch version is 1.8.1 and CUDA version is 11.1, you can use the following command to install mmcv-full.
**Note**: The pre-built packages provided above do not include all versions of mmcv-full, you can click on the corresponding links to see the supported versions. For example, you can click [cu102-torch1.8.0](https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html) and you can see that `cu102-torch1.8.0` only provides 1.3.0 and above versions of mmcv-full. In addition, We no longer provide `mmcv-full` pre-built packages compiled with `PyTorch 1.3 & 1.4` since v1.3.17. You can find previous versions that compiled with PyTorch 1.3 & 1.4 [here](./docs/en/get_started/previous_versions.md). The compatibility is still ensured in our CI, but we will discard the support of PyTorch 1.3 & 1.4 next year.
**Note**: mmcv-full does not provide pre-built packages for `cu102-torch1.11` and `cu92-torch*` on Windows.
Another way is to compile locally by running
```python
pipinstallmmcv-full
```
Note that the local compiling may take up to 10 mins.
b. Install the lite version.
```python
pipinstallmmcv
```
c. Install full version with custom operators for onnxruntime
- Check [here](docs/en/deployment/onnxruntime_op.md) for detailed instruction.
If you would like to build MMCV from source, please refer to the [guide](https://mmcv.readthedocs.io/en/latest/get_started/build.html).
## FAQ
If you face some installation issues, CUDA related issues or RuntimeErrors,
you may first refer to this [Frequently Asked Questions](https://mmcv.readthedocs.io/en/latest/faq.html).
## Citation
If you find this project useful in your research, please consider cite:
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.
-[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.
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
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 CUDA ops out of box. It takes longer time to build.
-**mmcv**: 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`.
a. Install the full version.
Before installing mmcv-full, make sure that PyTorch has been successfully installed following the [official guide](https://pytorch.org/).
We provide pre-built mmcv packages (recommended) with different PyTorch and CUDA versions to simplify the building for **Linux and Windows systems**. In addition, you can run [check_installation.py](.dev_scripts/check_installation.py) to check the installation of mmcv-full after running the installation commands.
i. Install the latest version.
The rule for installing the latest `mmcv-full` is as follows:
**Note**: mmcv-full is only compiled on PyTorch 1.x.0 because the compatibility usually holds between 1.x.0 and 1.x.1. If your PyTorch version is 1.x.1, you can install mmcv-full compiled with PyTorch 1.x.0 and it usually works well. For example, if your PyTorch version is 1.8.1 and CUDA version is 11.1, you can use the following command to install mmcv-full.
**Note**: The pre-built packages provided above do not include all versions of mmcv-full, you can click on the corresponding links to see the supported versions. For example, you can click [cu102-torch1.8.0](https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html) and you can see that `cu102-torch1.8.0` only provides 1.3.0 and above versions of mmcv-full. In addition, We no longer provide `mmcv-full` pre-built packages compiled with `PyTorch 1.3 & 1.4` since v1.3.17. You can find previous versions that compiled with PyTorch 1.3 & 1.4 [here](./docs/en/get_started/previous_versions.md). The compatibility is still ensured in our CI, but we will discard the support of PyTorch 1.3 & 1.4 next year.
**Note**: mmcv-full does not provide pre-built packages for `cu102-torch1.11` and `cu92-torch*` on Windows.
Another way is to compile locally by running
```python
pipinstallmmcv-full
```
Note that the local compiling may take up to 10 mins.
b. Install the lite version.
```python
pipinstallmmcv
```
c. Install full version with custom operators for onnxruntime
- Check [here](docs/en/deployment/onnxruntime_op.md) for detailed instruction.
If you would like to build MMCV from source, please refer to the [guide](https://mmcv.readthedocs.io/en/latest/get_started/build.html).
## FAQ
If you face some installation issues, CUDA related issues or RuntimeErrors,
you may first refer to this [Frequently Asked Questions](https://mmcv.readthedocs.io/en/latest/faq.html).
## Citation
If you find this project useful in your research, please consider cite:
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.
