Ultralytics Yolo

YOLO integration for realvirtual.io AI Builder with licensing information

The realvirtual.io AI Builder includes built-in integration with Ultralytics YOLO (You Only Look Once) for state-of-the-art object detection capabilities. While YOLO provides fast and accurate real-time object detection for industrial automation scenarios, it represents just one option among many training frameworks that can use AI Builder's synthetic data.

Overview

YOLO is a popular real-time object detection system that can detect multiple objects in images with high speed and accuracy. While realvirtual.io AI Builder includes built-in YOLO integration for convenience, the synthetic training data generated by AI Builder can be used with various machine learning frameworks including TensorFlow, PyTorch, Detectron2, and other training pipelines.

The built-in YOLO integration allows you to:

  • Train custom YOLO models using synthetic training data

  • Deploy trained models for real-time object detection

  • Test models within digital twin environments

  • Export models in ONNX format for cross-platform deployment

For alternative training approaches, the synthetic data can be exported in standard formats for use with your preferred AI framework.

GitHub Repository

The YOLO integration is available as a separate repository that works with realvirtual.io AI Builder:

YOLO Integration for realvirtual.io AI Builder

Licensing Information

Key Features

YOLO Model Variants

  • YOLOv8 Nano: Fastest inference, smallest model size

  • YOLOv8 Small: Balanced speed and accuracy

  • YOLOv8 Medium: Higher accuracy with moderate speed

  • YOLOv8 Large: Best accuracy, larger model size

  • YOLOv8 Extra Large: Maximum accuracy for complex scenarios

Training Capabilities

  • Custom Dataset Training: Train on your specific industrial objects

  • Transfer Learning: Fine-tune pre-trained models

  • Synthetic Data Integration: Use AI Builder generated training data

  • Multi-class Detection: Detect multiple object types simultaneously

Export Options

  • ONNX Format: Cross-platform deployment

  • TensorRT: NVIDIA GPU acceleration

  • OpenVINO: Intel hardware optimization

  • CoreML: Apple device deployment

Integration Workflow

  1. Generate Training Data: Use realvirtual.io AI Builder to create synthetic training datasets

  2. Choose Training Approach:

    • Use built-in YOLO integration for immediate training

    • Export data for use with external training frameworks

  3. Configure YOLO (if using built-in integration): Set up YOLO training parameters and model selection

  4. Train Model: Execute training using the YOLO integration or your preferred framework

  5. Test in Digital Twin: Validate model performance in virtual environment

  6. Deploy: Export trained model for production use

Installation

The Ultralytics YOLO integration is automatically handled by AI Builder:

Automatic Installation

  1. Open AI Builder Scene: Load your AI Builder scene in Unity

  2. Navigate to Training Component: Select the AI Training component in the Inspector

  3. Install Pipeline: When you see "Ultralytics training pipeline not found. Please install the package.", click the "Install Ultralytics Pipeline" button

  4. Package Manager: The Ultralytics dependency will be automatically installed and will appear in Unity's Package Manager

  5. Ready to Use: Once installed, the YOLO training capabilities will be available

Manual Repository Access

For advanced users or custom implementations, the YOLO integration source is available at:

Getting Started

To use the YOLO integration:

  1. Install the pipeline using the automatic installation method above

  2. Ensure you comply with the AGPL-3.0 licensing requirements for your use case

  3. Configure your training parameters in the AI Training component

  4. Start training with your synthetic training data

See Also

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