Description
Ultralytics YOLO is a comprehensive AI framework renowned for its capabilities in real-time object detection and image segmentation. The latest iteration, YOLO26, represents the next generation of vision AI, built upon significant deep learning and computer vision advancements. It features end-to-end NMS-free inference and optimized edge deployment, making it highly adaptable for various hardware platforms, from compact edge devices to robust cloud APIs.
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This framework is designed to streamline the entire computer vision workflow, from annotation and training to deployment. Users can annotate, train, and deploy YOLO models with ease. The Ultralytics Docs serve as a central hub for understanding and utilizing the framework's features, catering to both experienced machine learning practitioners and newcomers to the field. The goal is to maximize the potential of YOLO in diverse projects.
The YOLO family has a rich history, starting with Joseph Redmon and Ali Farhadi's initial release in 2015. Subsequent versions, including YOLOv2, YOLOv3, YOLOv4, YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOv9, YOLOv10, and the latest YOLO11, have consistently introduced architectural improvements and innovative techniques. YOLO26 continues this evolution, offering enhanced performance, flexibility, and efficiency for a full range of vision AI tasks.
Ultralytics provides flexible licensing options, including the AGPL-3.0 open-source license for students and enthusiasts, and an Enterprise License for commercial use. This dual approach ensures that while open collaboration is encouraged, businesses can seamlessly integrate Ultralytics software and AI models into their products and services. The framework supports a wide array of computer vision tasks, including detection, instance segmentation, semantic segmentation, pose estimation, tracking, and classification, making it a versatile tool for developers and researchers.
- CategoryComputer Vision Tools
- Pricing ModelOpen source
- DeploymentEdge / Cloud API
Ultralytics YOLO's Core Features
Real-time object detection
Image segmentation
YOLO26 model with NMS-free inference
Optimized edge deployment
Cloud API deployment
Support for detection, segmentation, pose estimation, tracking, and classification tasks
Streamlined annotation, training, and deployment workflow
AGPL-3.0 open-source license
Enterprise licensing for commercial use
Integration with custom datasets
Multi-object tracking capabilities
Getting Started with Ultralytics YOLO
Installation: Install the ultralytics package using pip.
Quickstart: Follow the quickstart guide for initial setup and running your first model.
Prediction: Utilize the predict mode for inference on images, videos, and streams.
Training: Train new YOLO models on custom datasets using the train mode.
Task Exploration: Discover and implement various computer vision tasks like detect, segment, and pose.
Tracking: Implement multi-object tracking using the track command.
Deployment: Deploy models to edge devices or cloud APIs.
Ultralytics YOLO's Use Cases
- Object Detection
- Image Segmentation
- Pose Estimation
- Object Tracking
- Autonomous Systems
- Surveillance and Security
- Medical Imaging Analysis
- Retail Analytics
