Ultralytics YOLO11 - AI Vision Models Tool

Overview

Ultralytics YOLO11 is a suite of computer vision models for object detection, segmentation, pose estimation, and classification. Models are integrated with Ultralytics HUB for visualization and training and the project is hosted on GitHub.

Key Features

• Object detection models for bounding box prediction
• Instance and semantic segmentation models
• Human and object pose estimation models
• Image classification models for labeling tasks
• Integration with Ultralytics HUB for visualization and training
• GitHub repository with code, examples, and model definitions

Ideal Use Cases

• Real-time object detection in video streams
• Instance segmentation for image editing or analysis
• Pose estimation for motion capture and analysis
• Image classification for dataset labeling and sorting
• Train and visualize models using Ultralytics HUB

Getting Started

• Visit the GitHub repository URL
• Clone the repository locally
• Install required dependencies
• Select a model or configuration file
• Prepare your dataset in expected format
• Train or run inference using provided scripts
• Use Ultralytics HUB to visualize and manage experiments

Pricing

Pricing not disclosed.

Key Information

• Category: Vision Models
• Type: AI Vision Models Tool