YOLO Computer Vision

🚀 YOLO Service Usage Guide

This guide details how to install, configure, and use the YOLO model for image analysis, covering the entire process from basic operations to advanced features.

🚀 Quick Start

✨ Features

• Comprehensive guidance on YOLO model installation, configuration, and usage.
• Support for multiple YOLO pre - trained models.
• Core functions including image analysis, object detection, and comprehensive image analysis.
• Advanced features such as image segmentation and classification.
• Automatic CPU/GPU switching for inference.

📦 Installation

1. Environment Setup

Before you start, make sure you have Python3 and the following dependencies installed:

pip install -r requirements.txt

2. Model Download

Download the YOLO pre - trained models to the models/ directory. Supported models include but are not limited to:

• yolov8n.pt
• yolov8s.pt
• yolov8m.pt
• yolov8l.pt

💻 Usage Examples

Basic Usage

Check Service Status

<function_calls>
<invoke name="test_connection">
</invoke>

List Supported Model Directories

<function_calls>
<invoke name="get_model_directories">
</invoke>

Object Detection with Default Model

<function_calls>
<invoke name="analyze_image_from_path">
<parameter name="image_path">/path/to/your/image.jpg</parameter>
<parameter name="confidence">0.3</parameter>
</invoke>

Object Detection with Specific Model

<function_calls>
<invoke name="analyze_image_from_path">
<parameter name="image_path">/path/to/your/image.jpg</parameter>
<parameter name="model_name">yolov8n.pt</parameter>
<parameter name="confidence">0.4</parameter>
</invoke>

Comprehensive Image Analysis

<function_calls>
<invoke name="comprehensive_image_analysis">
<parameter name="image_path">/path/to/your/image.jpg</parameter>
<parameter name="confidence">0.3</parameter>
</invoke>

Advanced Usage

Image Segmentation

<function_calls>
<invoke name="segment_objects">
<parameter name="image_data">/path/to/your/image.jpg</parameter>
<parameter name="is_path">true</parameter>
<parameter name="model_name">yolov8n-seg.pt</parameter>
</invoke>

Image Classification

<function_calls>
<invoke name="classify_image">
<parameter name="image_data">/path/to/your/image.jpg</parameter>
<parameter name="is_path">true</parameter>
<parameter name="model_name">yolov8n-cls.pt</parameter>
<parameter name="top_k">5</parameter>
</invoke>

🔧 Technical Details

Device Support

The service will automatically detect and use available hardware (CPU or GPU) for inference.

📚 Documentation

Troubleshooting

Model Loading Failure

• Ensure that the model file exists and the path is correct.
• Check if the Python environment meets the minimum version requirements.

Performance Issues

• It is recommended to use higher - performance hardware (e.g., NVIDIA GPU).
• Adjust the model size (from n to l) to match the computing resources.

> ⚠️ Important Note

• All image paths must be absolute paths.
• Refer to the specific documentation for the parameter value ranges.
• Keep the service port unblocked to avoid firewall interception.

By following the above steps, you can successfully deploy and use the YOLO model. For further assistance, please refer to the full documentation or contact the technical support team.