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Ultralytics YOLOv8s-WorldV2 Open-Vocabulary Detection

This endpoint deploys the YOLOv8s-WorldV2 open-vocabulary object detector (yolov8s-worldv2.pt, loaded via the Ultralytics YOLOWorld class) for real-time, zero-shot detection of arbitrary objects described by text — see YOLO-World. The Replicate endpoint takes a comma-separated class_names input (default person, bus, sign) and applies it via set_classes() on every request, so you choose exactly what to detect. It returns an annotated image plus optional JSON, with adjustable confidence, IoU, and image size.

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=yolov8s-worldv2.pt source=path/to/image.jpg imgsz=640

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 YOLOWorld

# Load the deployed YOLOv8s-worldv2 model
model = YOLOWorld("yolov8s-worldv2.pt")

# Set custom open-vocabulary classes before inference
model.set_classes(["person", "bus", "sign"])

# Run inference
results = model("path/to/image.jpg", conf=0.25, iou=0.45, imgsz=640)
results[0].show()

Discover more in the Python Docs.

✨ Model

Zero-shot transfer performance on the COCO dataset. All weights download automatically from the latest Ultralytics release on first use.

• Metrics are zero-shot transfer mAP / mAP50 / mAP75 on COCO, from the Ultralytics YOLO-World docs.
• This endpoint serves the deployed yolov8s-worldv2 variant (37.7 mAP); 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!