Verified project record

ultralytics/ultralytics

Ultralytics provides SOTA YOLO models for real-time computer vision across object detection, segmentation, classification, pose estimation, and tracking. It is accessible via a unified CLI and Python API for model training, evaluation, and deployment.

Project overview

Project type: Model Runtime · Image & Vision · Video
Deployment: Refer to project documentation
License: AGPL-3.0

Best for

Developers and AI engineers needing real-time computer vision for images and video.
Researchers evaluating SOTA YOLO models for tasks like pose estimation or semantic segmentation.

Key capabilities

Detect and localize objects within images.
Perform instance segmentation to identify object boundaries at the pixel level.
Perform semantic segmentation to classify each pixel in an image.
Classify the primary subject of an entire image.
Estimate the pose or keypoints of objects within an image.
Track detected objects across sequential frames or video.
Train models on custom datasets using the CLI or Python API.
Export trained or pretrained models to various formats like ONNX for deployment.

Limitations and risks

The AGPL-3.0 license requires open-sourcing derivative works, posing compliance challenges for closed-source commercial products.

Getting started

Install easily via a simple pip command with minimal boilerplate CLI and Python usage examples.

Evidence and sources

README: 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, ou…
README: Install the `ultralytics` package, including all requirements, in a Python>=3.8 environment with PyTorch>=1.8.
README: You can use Ultralytics YOLO directly from the Command Line Interface (CLI) with the `yolo` command:
README: yolo predict model=yolo26n.pt source='https://ultralytics.com/images/bus.jpg'
README: Ultralytics YOLO can also be integrated directly into your Python projects.

Reviewed at: 2026-07-17T07:42:25Z