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:
Licensing Information
Important Licensing Notice
This repository includes example pipelines that are licensed under AGPL-3.0.
Ultralytics YOLO is also licensed under AGPL-3.0. If you plan to use this integration for commercial purposes, please review the Ultralytics license conditions carefully.
For commercial licensing options, please contact Ultralytics directly or review their licensing terms at https://ultralytics.com/license.
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
Generate Training Data: Use realvirtual.io AI Builder to create synthetic training datasets
Choose Training Approach:
Use built-in YOLO integration for immediate training
Export data for use with external training frameworks
Configure YOLO (if using built-in integration): Set up YOLO training parameters and model selection
Train Model: Execute training using the YOLO integration or your preferred framework
Test in Digital Twin: Validate model performance in virtual environment
Deploy: Export trained model for production use
Installation
The Ultralytics YOLO integration is automatically handled by AI Builder:
Automatic Installation
Open AI Builder Scene: Load your AI Builder scene in Unity
Navigate to Training Component: Select the AI Training component in the Inspector
Install Pipeline: When you see "Ultralytics training pipeline not found. Please install the package.", click the "Install Ultralytics Pipeline" button
Package Manager: The Ultralytics dependency will be automatically installed and will appear in Unity's Package Manager
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:
Install the pipeline using the automatic installation method above
Ensure you comply with the AGPL-3.0 licensing requirements for your use case
Configure your training parameters in the AI Training component
Start training with your synthetic training data
See Also
Generate AI Training Data - Creating synthetic datasets
Testing AI in a Digital Twin - Model validation
Deploying the AI - Production deployment
AI Training - General training information
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