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Ultralytics YOLO11n Object Detection

This endpoint runs Ultralytics YOLO11n, the nano variant of the YOLO11 family, for real-time object detection on the 80 COCO classes. It loads the fixed yolo11n.pt weights (this endpoint is not size-selectable) and returns an annotated image plus optional JSON detections. Inputs let you tune confidence, IoU (NMS) threshold, and image size at inference time.

Find detailed documentation in the Ultralytics Docs. Get support via GitHub Issues. Join discussions on Discord, Reddit, and the Ultralytics Community Forums!

Request an Enterprise License for commercial use at Ultralytics Licensing.

📄 Documentation

For comprehensive guidance on training, validation, prediction, and deployment, refer to the full Ultralytics Docs.

Install

Install the ultralytics package, including all requirements, in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

For alternative installation methods, including Conda, Docker, and building from source via Git, please consult the Quickstart Guide.

CLI

You can run this model from the Command Line Interface (CLI) with the yolo command:

yolo predict model=yolo11n.pt source=path/to/bus.jpg

The yolo command supports various tasks and modes, accepting additional arguments like imgsz=640. See the CLI Docs.

Python

You can also run the model directly in Python:

from ultralytics import YOLO

# Load the COCO-pretrained YOLO11n model (the deployed weights)
model = YOLO("yolo11n.pt")

# Run inference on an image
results = model("path/to/bus.jpg", conf=0.25, iou=0.45, imgsz=640)

# Save the annotated image and print JSON detections
results[0].save("output.png")
print(results[0].to_json())

Discover more in the Python Docs.

✨ Model

Performance on the COCO val2017 dataset. All weights download automatically from the latest Ultralytics release on first use.

• mAP val values are for single-model single-scale on the COCO val2017 dataset.
• This Replicate endpoint serves only the deployed YOLO11n (nano) variant; the other rows are shown for reference.

🧩 Integrations

Our key integrations with leading AI platforms extend the functionality of Ultralytics’ offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with partners like Weights & Biases, Comet ML, Roboflow, and Intel OpenVINO, can optimize your AI workflow. Explore more at Ultralytics Integrations.

🤝 Contribute

We thrive on community collaboration! Ultralytics YOLO wouldn’t be the SOTA framework it is without contributions from developers like you. Please see our Contributing Guide to get started. We also welcome your feedback—share your experience by completing our Survey. A huge Thank You 🙏 to everyone who contributes!

📜 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, bypassing the open-source requirements of AGPL-3.0. To get started, please contact us via Ultralytics Licensing.

📞 Contact

For bug reports and feature requests related to Ultralytics software, please visit GitHub Issues. For questions, discussions, and community support, join our active communities on Discord, Reddit, and the Ultralytics Community Forums. We’re here to help with all things Ultralytics!