ultralytics
Ultralytics YOLO27, YOLO26, YOLO11, YOLOv8 for various computer vision tasks
GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
Ultralytics offers a suite of YOLO models for a variety of computer vision tasks, including object detection, instance segmentation, and image classification, with support for deployment in ONNX format.
Good fit when
- When you need a comprehensive set of YOLO models for multiple computer vision tasks, including object detection, instance segmentation, and image classification.
- If you are working on projects that require the flexibility of deploying models in ONNX format for efficient inference.
Avoid when
- If your project strictly requires proprietary or closed-source software, as Ultralytics' AGPL-3.0 license may not be suitable without purchasing the Enterprise License.
- When you need a tool that focuses solely on a specific computer vision task, as Ultralytics provides a broad range of models which might be overkill for a single-task focus.
- Pricing:
- freemium - Free for open-source projects under AGPL-3.0, with paid options for commercial use through the Enterprise License.
- Requirements:
- Min 4 GB RAM; Requires Python and PyTorch for model training and inference.
Observed Sep 18, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of Sep 18, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 18, 2026
- Security (OSV)
- No lockfile
- As of Sep 18, 2026
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Install
pip install ultralyticsHow it fits your stack(9)
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Evidence and technical details
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Overview
Ultralytics provides YOLO models for object detection, instance segmentation, semantic segmentation, image classification, pose estimation, and object tracking.
Capability facts
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Sep 18, 2026
- Languages
- python
Source: github.language+pyproject.toml · Sep 18, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 18, 2026)
Discover more examples in the YOLO [Python Docs](https://docs.ultralytics.com/usage/python).Source link
Tags
README
Export the model to ONNX format for deployment path = model.export(format="onnx") Returns the path to the exported model ``` Discover more examples in the YOLO Python Docs. </details 📜 License Ultralytics offers two licensing options to suit different needs: AGPL 3.0 License : T...
For agents
This page has a .md twin and JSON over the API.