# ultralytics/ultralytics

Ultralytics YOLO26, YOLO11, YOLOv8 — object detection, instance segmentation, semantic segmentation, image classification, pose estimation, object tracking

Repository: https://github.com/ultralytics/ultralytics
Canonical: https://ross.abutalabs.com/products/ultralytics
Homepage: https://platform.ultralytics.com
Language: Python
License: AGPL-3.0
License Family: copyleft
Topics: ultralytics, yolov8, yolo, deep-learning, image-classification, machine-learning, object-detection, pytorch, tracking, instance-segmentation, yolo-world, yolo11, computer-vision, python, pose-estimation, rotated-object-detection, segment-anything, yolo26, object-tracking, semantic-segmentation
Last push: 2026-08-27T00:21:57+00:00

## Health v2 (maintenance only)
Score: 95/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 87, longevity 100
- inputs: {"age_days": 1452, "days_push": 7, "days_rel": 7, "gap_med": 1, "n_releases_24m": 398}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 60991, forks 11634 (observed 2026-08-28T04:12:18.887297+00:00)

## What it is
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
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, computer-vision, image-processing, llm-training
- domain: computer-vision, deep-learning, machine-learning, artificial-intelligence
- platform: python, cross-platform
- tags: yolo, object-detection, instance-segmentation, pose-estimation, object-tracking, pytorch, image-classification, semantic-segmentation, edge-deployment, onnx-export, gpu

## Member repositories
- ultralytics/ultralytics (main) score 95

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:12:18.887297+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:17:41.009572+00:00, confidence not recorded.
  - readme: https://github.com/ultralytics/ultralytics (fetched 2026-08-28T04:12:18.887297+00:00, sha f715848861d5)
  - homepage: https://platform.ultralytics.com (fetched 2026-08-28T17:58:55.130712+00:00, sha 211cb9725bb8)
  - site_page: https://docs.ultralytics.com (fetched 2026-08-28T17:58:55.139183+00:00, sha fba5847c3417)
  - site_page: https://www.ultralytics.com/about (fetched 2026-08-28T17:58:55.143984+00:00, sha e54f843031a6)
  - site_page: https://docs.ultralytics.com/integrations (fetched 2026-08-28T17:58:55.145877+00:00, sha 1dec60b917d0)
  - site_page: https://www.ultralytics.com/pricing (fetched 2026-08-28T17:58:55.141866+00:00, sha 708ea90c759d)
- Data as of 2026-08-30T08:39:29.467469+00:00.