# Ultralytics Yolo

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:

{% embed url="<https://github.com/game4automation/io.realvirtual.aibuilder-ultralytics>" %}
YOLO Integration for realvirtual.io AI Builder
{% endembed %}

## Licensing Information

{% hint style="warning" %}
**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>.
{% endhint %}

## 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:

* Repository: <https://github.com/game4automation/io.realvirtual.aibuilder-ultralytics>

## 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

* [Generate AI Training Data](https://doc.realvirtual.io/extensions/realvirtual.io-aibuilder/generate-ai-training-data) - Creating synthetic datasets
* [Testing AI in a Digital Twin](https://doc.realvirtual.io/extensions/realvirtual.io-aibuilder/testing-ai-in-a-digital-twin) - Model validation
* [Deploying the AI](https://doc.realvirtual.io/extensions/realvirtual.io-aibuilder/deploying-the-ai) - Production deployment
* [AI Training](https://github.com/game4automation/doc/blob/doc/extensions/realvirtual.io-aibuilder/ai-training/README.md) - General training information