ultralytics/ultralytics
Ultralytics YOLO27, YOLO26, YOLO11, YOLOv8 — object detection, instance segmentation, semantic segmentation, image classification, pose estimation, object tracking
Build with Backblaze B2
SDKs, agent skills, IDE extensions, and reference pipelines from Backblaze Labs. All open source.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
Ranks #22 among CLI repositories tracked this week, and #22 over the last 30 days; #226 of 1,950 repositories GStars tracks this week (top 12%).
5 releases in the last 90 days, the latest today, with 467 contributors on GitHub.
Peaked at #6 in the weekly ranking the week of Jan 18, its best in the past year.
Use Cases & Benefits
- Provides state-of-the-art YOLO models for object detection, segmentation, classification, pose estimation, and tracking using Python and PyTorch.
- Delivers fast, accurate, and easy-to-use models with extensive task support and seamless integration via CLI and Python API.
- Use for developing real-time object detection and tracking applications in video surveillance or autonomous systems.
- Use for training and deploying instance and semantic segmentation models for medical imaging or industrial inspection.
- Use for pose estimation in sports analytics or human-computer interaction projects requiring keypoint detection.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
AGPL 3.0
Network copyleft: like the GPL, and also triggered by offering the software over a network.
Community files
- README: present
- Contributing guide: present
- Code of conduct: present
Watch-outs
- AGPL-style copyleft also applies when the software is offered over a network.
General guidance from repository metadata, not legal advice. How we score
From the README
Ultralytics creates cutting-edge, state-of-the-art (SOTA) YOLO models built on years of foundational research in computer vision and AI. Constantly updated for performance and flexibility, our models are fast, accurate, and easy to use. They excel at object detection, tracking, instance segmentation, image classification, and pose estimation tasks.
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.
See below for quickstart installation and usage examples. For comprehensive guidance on training, validation, prediction, and deployment, refer to our full Ultralytics Docs.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
+2,828 stars over 87 days across 8 snapshot days; best day +12 on Sep 11.
Forks
11.9K+450
Open issues
77-82
Compared with peers in CLI
Fastest-growing repositories GStars tracks in this cohort for the week of Oct 11, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| morluto/rea | 207.6K | +183.8K | 60 | 2 days ago |
| mattpocock/skills | 289.7K | +12.0K | 75 | 9 days ago |
| stablyai/orca | 91.6K | +4.6K | 60 | 1 day ago |
| Panniantong/Agent-Reach | 97.0K | +3.9K | 46 | 124 days ago |
| addyosmani/agent-skills | 106.2K | +3.6K | 75 | today |
| ultralytics/ultralytics | 62.5K | +0 | 75 | today |
| Category median | 106.2K | +4.6K | 60 | — |
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