https://github.com/alihassanml/license-plate-detection-using-yolov10
License Plate Detection using YOLOv10
https://github.com/alihassanml/license-plate-detection-using-yolov10
deep-learning number-plate-recognition object-detection yolov10
Last synced: about 1 month ago
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License Plate Detection using YOLOv10
- Host: GitHub
- URL: https://github.com/alihassanml/license-plate-detection-using-yolov10
- Owner: alihassanml
- License: mit
- Created: 2024-09-08T18:35:12.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-09-08T21:10:48.000Z (about 1 year ago)
- Last Synced: 2025-03-21T05:03:26.885Z (7 months ago)
- Topics: deep-learning, number-plate-recognition, object-detection, yolov10
- Language: Jupyter Notebook
- Homepage:
- Size: 23.8 MB
- Stars: 2
- Watchers: 0
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# License Plate Detection using YOLOv10
This project demonstrates real-time license plate detection using the YOLOv10 model. It leverages a custom dataset to detect number plates in live video streams.
## Table of Contents
- [Overview](#overview)
- [Installation](#installation)
- [Dataset](#dataset)
- [Model](#model)
- [Usage](#usage)
- [Results](#results)
- [License](#license)
## Overview
This project aims to detect and localize license plates in real-time using the YOLOv10 architecture. The model is trained on a custom dataset to accurately identify number plates in various conditions.
## Installation
1. Clone the repository:
```bash
git clone https://github.com/alihassanml/License-Plate-Detection-using-YOLOv10.git
cd License-Plate-Detection-using-YOLOv10
```
2. Install the required dependencies:
```bash
pip install -r requirements.txt
```
3. Download the dataset from Kaggle:
```bash
!kaggle datasets download -d alihassanml/yolo-number-plates
```
4. Extract the dataset:
```bash
unzip yolo-number-plates.zip -d dataset/
```
## Dataset
The dataset used for this project is a custom set containing labeled images of number plates. You can download it from [Kaggle](https://www.kaggle.com/datasets/alihassanml/yolo-number-plates).
## Model
YOLOv10 is used for real-time object detection, known for its speed and accuracy. The model is fine-tuned to detect number plates in various lighting and environmental conditions.
## Usage
1. Train the model using the dataset:
```bash
python train.py --data dataset/data.yaml --cfg cfg/yolov10.yaml --weights yolov10.pt
```
2. Run the live number plate detection:
```bash
python detect.py --source 0 --weights yolov10.pt --conf 0.5
```
Replace `0` with a video file path if needed.
## Results
The model is capable of detecting number plates in real-time with high accuracy. Sample results are shown below:
- Detected license plates in various live video streams
- Accurate bounding boxes even in low-light conditions
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.