ultralytics/ultralytics
Ultralytics YOLO26, YOLO11, YOLOv8 — object detection, instance segmentation, semantic segmentation, image classification, pose estimation, object tracking observed · 2026-08-28
Health v2 · maintenance only
95/100
- Activity 99
- Release rhythm 87
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 1
- age_days: 1452
- days_rel: 7
- days_push: 7
- n_releases_24m: 398
Adoption not part of the score
60991 stars · 11634 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
Ultralytics YOLO is a Python package and CLI providing a family of real-time computer vision models (YOLO26, YOLO11, YOLOv8) for object detection, segmentation, classification, pose estimation, and tracking. It supports training, evaluation, inference, and export to many deployment formats from edge devices to cloud.
Use cases
- detect objects in images and video in real time
- train a custom object detection model on my own dataset
- segment objects in images with instance segmentation
- estimate human pose from images or video
- track objects across video frames
- classify images with a pretrained model
- export a vision model to ONNX or TensorRT for edge deployment
- count and locate objects in drone or surveillance footage
When to choose
- you need state-of-the-art real-time detection or segmentation with a simple Python API
- you want pretrained models plus easy fine-tuning on custom datasets
- you need broad export support (ONNX, TensorRT, CoreML, TFLite, OpenVINO) for deployment
- you want an active, well-documented project with a large community
When to avoid
- your product is closed-source and you cannot comply with AGPL-3.0 or buy an enterprise license
- you need non-vision tasks like NLP or audio
- you require fully custom model architectures outside the YOLO family
- you need a lightweight inference-only runtime without the training stack
Facets
library · maturity active
machine-learning deep-learning computer-vision image-processing llm-training computer-vision deep-learning machine-learning artificial-intelligence python cross-platform yolo object-detection instance-segmentation pose-estimation object-tracking pytorch image-classification semantic-segmentation edge-deployment onnx-export gpu
6 sources
- readme: https://github.com/ultralytics/ultralytics · fetched 2026-08-28 · f715848861d5
- homepage: https://platform.ultralytics.com · fetched 2026-08-28 · 211cb9725bb8
- site_page: https://docs.ultralytics.com · fetched 2026-08-28 · fba5847c3417
- site_page: https://www.ultralytics.com/about · fetched 2026-08-28 · e54f843031a6
- site_page: https://docs.ultralytics.com/integrations · fetched 2026-08-28 · 1dec60b917d0
- site_page: https://www.ultralytics.com/pricing · fetched 2026-08-28 · 708ea90c759d
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| ultralytics/ultralytics | main | 95 |
For agents
markdown · JSON · MCP: product_card(name="ultralytics/ultralytics")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem