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Ultralytics YOLO26 Instance Segmentation

Ultralytics creates cutting-edge, state-of-the-art (SOTA) YOLO models built on years of foundational research in computer vision and AI. YOLO26 is the latest generation — fast, accurate, and easy to use. This Replicate deployment runs YOLO26 on CPU with a runtime-selectable model size (n/s/m/l/x); this endpoint performs instance segmentation on the 80 COCO-Seg classes, returning a per-object mask in addition to each bounding box.

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=yolo26n-seg.pt source='https://ultralytics.com/images/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 pretrained YOLO26-seg model
model = YOLO("yolo26n-seg.pt")

# Run inference on an image
results = model("path/to/image.jpg")
results[0].show()  # display the annotated result
results[0].save("output.png")  # save it

Discover more in the Python Docs.

✨ Model

Refer to the Segmentation Docs for usage examples. These models are trained on COCO-Seg, including 80 classes. All models download automatically from the latest Ultralytics release on first use.

• mAP box/mask values are for single-model single-scale on the COCO val2017 dataset. Reproduce with yolo val segment data=coco.yaml device=0
• Speed metrics are averaged over dataset val images; CPU speeds measured with ONNX export, GPU speeds with TensorRT. See YOLO Performance Metrics for details.

🧩 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!