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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This model is not currently available via any of the supported third-party Inference Providers, and
the HF Inference API does not support ultralytics models with pipeline type object-detection
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