---
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:


```bash
pip install datasets ultralytics opencv-python numpy pandas matplotlib
```

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

```python
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:

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

## Model Training 
A pre-trained **YOLOv8**  model (`yolov8n.pt`) is fine-tuned on the dataset:

```python
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



---