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---
license: mit
pipeline_tag: object-detection
tags:
- License-Plate-Recognizer
- Yolov8m
- Object detection
---
**License Plate Detection Model using YOLOv8**
=============================================
**Model Description**
--------------------
This is a deep learning model for detecting and cropping license plates in images, trained using the YOLOv8 object detection architecture. The model takes an image of a vehicle as input and returns a cropped image of the detected license plate.
**Dataset**
----------
The model was trained on a dataset of 500 images of vehicles with annotated license plates. The dataset was curated to include a variety of license plate types, angles, and lighting conditions.
**Model Training**
-----------------
The model was trained using the YOLOv8 architecture with the following hyperparameters:
* Batch size: 32
* Epochs: 50
* Learning rate: 0.001
* Optimizer: Adam
* Loss function: Mean Average Precision (MAP)
**Model Performance**
---------------------
The model achieves the following performance metrics on the validation set:
* mAP (mean Average Precision): 0.92
* AP (Average Precision) for license plates: 0.95
* Recall: 0.93
* Precision: 0.94
**Usage**
-----
To use this model, you can follow these steps:
1. Install the required libraries: `pip install ultralytics`
2. Load the model: `model = torch.hub.load('ultralytics/yolov8', 'custom', path='path/to/model.pt')`
3. Load the input image: `img = cv2.imread('path/to/image.jpg')`
4. Preprocess the input image: `img = cv2.resize(img, (640, 480))`
5. Run the model: `results = model(img)`
6. Extract the cropped license plate image: `license_plate_img = results.crop[0].cpu().numpy()`
**Example Code**
--------------
Here is an example code snippet to get you started:
```python
import cv2
import torch
# Load the model
model = torch.hub.load('ultralytics/yolov8', 'custom', path='path/to/model.pt')
# Load the input image
img = cv2.imread('path/to/image.jpg')
# Preprocess the input image
img = cv2.resize(img, (640, 480))
# Run the model
results = model(img)
# Extract the cropped license plate image
license_plate_img = results.crop[0].cpu().numpy()
cv2.imwrite('license_plate.jpg', license_plate_img) |