README.md

metadata

license: apache-2.0
datasets:
  - keremberke/license-plate-object-detection
language:
  - en
metrics:
  - accuracy
base_model:
  - Ultralytics/YOLOv8
pipeline_tag: object-detection
tags:
  - yolov8
  - fine-tuned
  - self-driving
new_version: yasirfaizahmed/license-plate-object-detection
library_name: ultralytics

---

YOLOv8 License Plate Detection

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

Dataset

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)

Training Configuration

• Epochs : 75

• Image Size : 640x640

• Batch Size : 16

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