File size: 1,754 Bytes
9922cd6 76a8043 d7fa289 a85f9f0 d7fa289 9d42447 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 |
---
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
---
|