yolo11 — What's That AI

<br>

Ultralytics YOLO11 is available
through the official Ultralytics YOLO 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.

📄 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.

<details open>
<summary>Install</summary>

Install the ultralytics package in a Python>=3.8 environment with
PyTorch.

pip install ultralytics

</details>

<details open>
<summary>Usage</summary>

CLI

yolo predict model=yolo11n.pt source="https://ultralytics.com/images/bus.jpg"

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()

</details>

✨ 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	`yolo11n.pt` `yolo11s.pt` `yolo11m.pt` `yolo11l.pt` `yolo11x.pt`	Detection	✅	✅	✅	✅
YOLO11-seg	`yolo11n-seg.pt` `yolo11s-seg.pt` `yolo11m-seg.pt` `yolo11l-seg.pt` `yolo11x-seg.pt`	Instance Segmentation	✅	✅	✅	✅
YOLO11-cls	`yolo11n-cls.pt` `yolo11s-cls.pt` `yolo11m-cls.pt` `yolo11l-cls.pt` `yolo11x-cls.pt`	Classification	✅	✅	✅	✅
YOLO11-pose	`yolo11n-pose.pt` `yolo11s-pose.pt` `yolo11m-pose.pt` `yolo11l-pose.pt` `yolo11x-pose.pt`	Pose Estimation	✅	✅	✅	✅
YOLO11-obb	`yolo11n-obb.pt` `yolo11s-obb.pt` `yolo11m-obb.pt` `yolo11l-obb.pt` `yolo11x-obb.pt`	Oriented Detection	✅	✅	✅	✅

🧩 Integrations

Ultralytics integrations extend dataset labeling, training, visualization, deployment, and model management workflows.
Explore Ultralytics Platform and the
Ultralytics Integrations docs to connect YOLO11 with your AI stack,
including popular export formats like TensorRT,
ONNX,
CoreML, and
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 to get started.
For source changes, documentation improvements, bug reports, and feature requests, use the canonical
ultralytics/ultralytics repository.

📜 License

Ultralytics offers two licensing options to suit different needs:

AGPL-3.0 License: This OSI-approved open-source license is perfect for students, researchers, and enthusiasts. It encourages open collaboration and knowledge sharing. See the 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.

📞 Contact

For YOLO11 usage guidance, start with the YOLO11 documentation. Install
or upgrade the Ultralytics Python package with pip, and review the
canonical source code for implementation details.

[!IMPORTANT]
Please submit bug reports and feature requests in the
ultralytics/ultralytics issue tracker, where
maintainers triage them alongside the source code.

For questions, discussions, and community support, join our active communities on
Discord, Reddit, and the
Ultralytics Community Forums.