License Plate Object Detection

🚀 YOLOv8 License Plate Detection

This project leverages the YOLOv8 object detection model to detect license plates, using Keremberke's License Plate Object Detection dataset and trained with the Ultralytics YOLOv8 framework.

🚀 Quick Start

This project uses the YOLOv8 object detection model to detect license plates. The dataset used is Keremberke's License Plate Object Detection, and the model is trained using the Ultralytics YOLOv8 framework.

📦 Installation

Ensure you have the required dependencies installed:

pip install datasets ultralytics opencv-python numpy pandas matplotlib

💻 Usage Examples

Basic Usage

Dataset Loading

The dataset is loaded from Hugging Face's datasets library:

from datasets import load_dataset
ds = load_dataset("keremberke/license-plate-object-detection", "full")

The dataset is split into:

• Training Set
• Validation Set
• Test Set

Data Preprocessing

• Images are extracted from the dataset and saved locally.
• Bounding box annotations are converted into YOLO format (normalized coordinates).
• The dataset is structured into:

dataset/
├── images/
│   ├── train/
│   ├── val/
├── labels/
│   ├── train/
│   ├── val/

Model Training

A pre-trained YOLOv8 model (yolov8n.pt) is fine-tuned on the dataset:

from ultralytics import YOLO

model = YOLO('yolov8n.pt')  # Load a small YOLOv8 model
results = model.train(data="dataset.yaml", epochs=75, imgsz=640, batch=16)

Advanced Usage

Training Configuration

• Epochs: 75
• Image Size: 640x640
• Batch Size: 16

📄 License

This project is licensed under the Apache-2.0 license.