MMYOLO unifies the modules of various YOLO algorithms and provides a unified benchmark process. Users can compare and analyze in a fair and convenient way.
- **Detailed introductory and advanced documentation**
MMYOLO provides a series of documents from getting started, to model deployment, advanced guidelines, and algorithm analysis, making it easy for different users to get started and make extensions quickly.
MMYOLO decompose the framework into different components and users can easily construct a customized model by combining different modules and training and testing strategies.
- Unified component interfaces based on [OpenMMLab 2.0](https://github.com/open-mmlab) and [MMDetection 3.0](https://github.com/open-mmlab/mmdetection/tree/3.x)
- Support for YOLOv5/YOLOX training and deployment, support for YOLOv6 inference and deployment
MMYOLO relies on PyTorch, MMCV, MMEngine, and MMDetection. Below are quick steps for installation. Please refer to the [Install Guide](docs/en/get_started.md) for more detailed instructions.
MMYOLO is based on the MMDetection and uses the same code organization and design approach. To get better use of this, please read [MMDetection Overview](https://mmdetection.readthedocs.io/en/latest/get_started.html) for the first understanding of MMDetection.
MMYOLO usage is almost identical to MMDetection and all tutorials are straightforward to use, you can also learn about [MMDetection User Guide and Advanced Guide](https://mmdetection.readthedocs.io/en/3.x/).
For different sections than MMDetection, we have also prepared user guides and advanced guides, please read our [documentation](https://mmyolo.readthedocs.io/zenh_CN/latest/).
- [Algorithm principles and implementation](https://mmyolo.readthedocs.io/zh_CN/latest/algorithm_descriptions/index.html#algorithm-principles-and-implementation)
- [Algorithm principles and implementation with YOLOv5](docs/en/algorithm_descriptions/yolov5_description.md)
Results and models are available in the [model zoo](docs/en/model_zoo.md).
<detailsopen>
<summary><b>Supported Algorithms</b></summary>
- [x] [YOLOv5](configs/yolov5)
- [x] [YOLOX](configs/yolox)
- [ ] [YOLOv6](configs/yolov6)(Inference only)
</details>
<detailsopen>
<divalign="center">
<b>Module Components</b>
</div>
<tablealign="center">
<tbody>
<tralign="center"valign="bottom">
<td>
<b>Backbones</b>
</td>
<td>
<b>Necks</b>
</td>
<td>
<b>Loss</b>
</td>
<td>
<b>Common</b>
</td>
</tr>
<trvalign="top">
<td>
<ul>
<li>YOLOv5CSPDarknet</li>
<li>YOLOXCSPDarknet</li>
<li>EfficientRep</li>
</ul>
</td>
<td>
<ul>
<li>YOLOv5PAFPN</li>
<li>YOLOv6RepPAFPN</li>
<li>YOLOXPAFPN</li>
</ul>
</td>
<td>
<ul>
<li>IoULoss</li>
</ul>
</td>
<td>
<ul>
</ul>
</td>
</tr>
</td>
</tr>
</tbody>
</table>
</details>
## FAQ
Please refer to the [FAQ](docs/en/notes/faq.md) for frequently asked questions.
## Contributing
We appreciate all contributions to improving MMYOLO. Ongoing projects can be found in our [GitHub Projects](https://github.com/open-mmlab/mmyolo/projects). Welcome community users to participate in these projects. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
## Acknowledgement
MMYOLO is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedback.
We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new detectors.
