https://github.com/ultralytics/yolo11
Ultralytics YOLO11 🚀
https://github.com/ultralytics/yolo11
cli computer-vision deep-learning image-classification instance-segmentation machine-learning object-detection pose-estimation python pytorch rotated-object-detection tracking ultralytics yolo yolo11
Last synced: 11 days ago
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Ultralytics YOLO11 🚀
- Host: GitHub
- URL: https://github.com/ultralytics/yolo11
- Owner: ultralytics
- License: agpl-3.0
- Created: 2026-06-12T19:53:14.000Z (26 days ago)
- Default Branch: main
- Last Pushed: 2026-06-12T20:31:21.000Z (26 days ago)
- Last Synced: 2026-06-12T22:14:03.527Z (26 days ago)
- Topics: cli, computer-vision, deep-learning, image-classification, instance-segmentation, machine-learning, object-detection, pose-estimation, python, pytorch, rotated-object-detection, tracking, ultralytics, yolo, yolo11
- Homepage: https://platform.ultralytics.com/ultralytics/yolo11
- Size: 15.6 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
README
[中文](README.zh-CN.md) | [한국어](https://docs.ultralytics.com/ko) | [日本語](https://docs.ultralytics.com/ja) | [Русский](https://docs.ultralytics.com/ru) | [Deutsch](https://docs.ultralytics.com/de) | [Français](https://docs.ultralytics.com/fr) | [Español](https://docs.ultralytics.com/es) | [Português](https://docs.ultralytics.com/pt) | [Türkçe](https://docs.ultralytics.com/tr) | [Tiếng Việt](https://docs.ultralytics.com/vi) | [العربية](https://docs.ultralytics.com/ar)
[Ultralytics](https://www.ultralytics.com/) [YOLO11](https://docs.ultralytics.com/models/yolo11) is available
through the official [Ultralytics YOLO](https://github.com/ultralytics/ultralytics) package. It supports object
detection, instance segmentation, image classification, pose estimation, oriented object detection, and tracking in a
fast, accurate, and easy to use Python and CLI workflow.
This repository is a lightweight discovery page for YOLO11. The canonical implementation, package releases, model
downloads, issues, and pull requests are maintained in [ultralytics/ultralytics](https://github.com/ultralytics/ultralytics).
## 📄 Documentation
See below for quickstart installation and YOLO11 usage examples. For comprehensive guidance on training, validation,
prediction, and deployment, refer to the full [Ultralytics Docs](https://docs.ultralytics.com).
Install
Install the `ultralytics` package in a [Python>=3.8](https://www.python.org/) environment with
[PyTorch](https://pytorch.org/get-started/locally/).
[](https://pypi.org/project/ultralytics/)
[](https://clickpy.clickhouse.com/dashboard/ultralytics)
[](https://pypi.org/project/ultralytics/)
```bash
pip install ultralytics
```
Usage
### CLI
```bash
yolo predict model=yolo11n.pt source="https://ultralytics.com/images/bus.jpg"
```
### Python
```python
from ultralytics import YOLO
# Load a pretrained YOLO11n model
model = YOLO("yolo11n.pt")
# Run inference on the sample image
results = model("https://ultralytics.com/images/bus.jpg")
# Display the annotated results
results[0].show()
```
## ✨ Models
YOLO11 models are available for detection, segmentation, classification, pose estimation, and oriented object detection.
All model weights download automatically from the latest Ultralytics assets release on first use.
| Model Family | Example Weights | Task | Train | Val | Predict | Export |
| ------------------------------------------------------------------ | ----------------------------------------------------------------------------------------- | ------------------------------------------------------------------- | ----- | --- | ------- | ------ |
| [YOLO11](https://platform.ultralytics.com/ultralytics/yolo11) | `yolo11n.pt` `yolo11s.pt` `yolo11m.pt` `yolo11l.pt` `yolo11x.pt` | [Detection](https://docs.ultralytics.com/tasks/detect) | ✅ | ✅ | ✅ | ✅ |
| [YOLO11-seg](https://platform.ultralytics.com/ultralytics/yolo11) | `yolo11n-seg.pt` `yolo11s-seg.pt` `yolo11m-seg.pt` `yolo11l-seg.pt` `yolo11x-seg.pt` | [Instance Segmentation](https://docs.ultralytics.com/tasks/segment) | ✅ | ✅ | ✅ | ✅ |
| [YOLO11-cls](https://platform.ultralytics.com/ultralytics/yolo11) | `yolo11n-cls.pt` `yolo11s-cls.pt` `yolo11m-cls.pt` `yolo11l-cls.pt` `yolo11x-cls.pt` | [Classification](https://docs.ultralytics.com/tasks/classify) | ✅ | ✅ | ✅ | ✅ |
| [YOLO11-pose](https://platform.ultralytics.com/ultralytics/yolo11) | `yolo11n-pose.pt` `yolo11s-pose.pt` `yolo11m-pose.pt` `yolo11l-pose.pt` `yolo11x-pose.pt` | [Pose Estimation](https://docs.ultralytics.com/tasks/pose) | ✅ | ✅ | ✅ | ✅ |
| [YOLO11-obb](https://platform.ultralytics.com/ultralytics/yolo11) | `yolo11n-obb.pt` `yolo11s-obb.pt` `yolo11m-obb.pt` `yolo11l-obb.pt` `yolo11x-obb.pt` | [Oriented Detection](https://docs.ultralytics.com/tasks/obb) | ✅ | ✅ | ✅ | ✅ |
## 🧩 Integrations
Ultralytics integrations extend dataset labeling, training, visualization, deployment, and model management workflows.
Explore [Ultralytics Platform](https://platform.ultralytics.com) and the
[Ultralytics Integrations docs](https://docs.ultralytics.com/integrations) to connect YOLO11 with your AI stack,
including popular export formats like [TensorRT](https://docs.ultralytics.com/integrations/tensorrt),
[ONNX](https://docs.ultralytics.com/integrations/onnx),
[CoreML](https://docs.ultralytics.com/integrations/coreml), and
[TFLite](https://docs.ultralytics.com/integrations/tflite).
## 🤝 Contribute
We thrive on community collaboration! Ultralytics YOLO would not be the SOTA framework it is without contributions from
developers like you. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing) to get started.
For source changes, documentation improvements, bug reports, and feature requests, use the canonical
[ultralytics/ultralytics](https://github.com/ultralytics/ultralytics) repository.
[](https://github.com/ultralytics/ultralytics/graphs/contributors)
## 📜 License
Ultralytics offers two licensing options to suit different needs:
- **AGPL-3.0 License**: This [OSI-approved](https://opensource.org/license/agpl-3.0) open-source license is perfect for students, researchers, and enthusiasts. It encourages open collaboration and knowledge sharing. See the [LICENSE](LICENSE) file for full details.
- **Ultralytics Enterprise License**: For development and production use, this license enables seamless integration of Ultralytics software and AI models into business products and services, including internal tools, automated workflows, and production deployments, bypassing the open-source requirements of AGPL-3.0. To get started, please contact us via [Ultralytics Licensing](https://www.ultralytics.com/license).
## 📞 Contact
For YOLO11 usage guidance, start with the [YOLO11 documentation](https://docs.ultralytics.com/models/yolo11). Install
or upgrade the [Ultralytics Python package](https://pypi.org/project/ultralytics/) with `pip`, and review the
[canonical source code](https://github.com/ultralytics/ultralytics) for implementation details.
> [!IMPORTANT]
> Please submit bug reports and feature requests in the
> [ultralytics/ultralytics issue tracker](https://github.com/ultralytics/ultralytics/issues/new/choose), where
> maintainers triage them alongside the source code.
For questions, discussions, and community support, join our active communities on
[Discord](https://discord.com/invite/ultralytics), [Reddit](https://www.reddit.com/r/ultralytics/), and the
[Ultralytics Community Forums](https://community.ultralytics.com/).