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---
title: LPlateVision – YOLO-based License Plate Detection
emoji: 🚗
colorFrom: yellow
colorTo: blue
sdk: streamlit
sdk_version: "1.40.0" # latest stable streamlit
app_file: app.py
pinned: false
license: mit
---
# license_plate_predicition — YOLOv8 License Plate Detection 📸🚀
A lightweight Streamlit app that loads a trained YOLOv8 model and detects license plates in images with an intuitive chat-style UI.
---
## Badges
[](./LICENSE)
---
## Table of Contents
- [Demo](#demo)
- [Features](#features)
- [Installation / Setup](#installation--setup)
- [Usage](#usage)
- [Configuration / Options](#configuration--options)
- [Contributing](#contributing)
- [License](#license)
- [Acknowledgements / Credits](#acknowledgements--credits)
---
## Demo
The repository includes real demo assets under `./demo/`.
- Screenshot: `./demo/demo.png`
<img src="./demo/demo.png" alt="demo" width="640" />
- Video: `./demo/demo.mp4`
<video src="./demo/demo.mp4" width="640" controls></video>
---
## Features
- **YOLOv8 inference** via `ultralytics` for license plate detection.
- **Chat-style Streamlit UI** to upload an image or paste an image URL.
- **On-image annotations** with bounding boxes.
- **Single-command launch** with Streamlit.
---
## Installation / Setup
Use a virtual environment for isolation.
```bash
# Create a virtual environment
python -m venv .venv
# Activate it
# On Linux/Mac:
source .venv/bin/activate
# On Windows:
.venv\Scripts\activate
# Upgrade pip (recommended)
pip install --upgrade pip
# Install dependencies
pip install -r requirements.txt
```
---
## Usage
- Make sure you have a YOLOv8 model file at `./models/model.pt` (see [Configuration](#configuration--options)).
- Launch the app:
```bash
streamlit run app.py
```
- In the app UI (`app.py` calling `render_chat_ui()` in `ui.py`):
- Choose "Upload Image" and provide a `.jpg/.jpeg/.png`, or
- Choose "Image URL" and paste a direct image URL.
- Click "Detect License Plate" to run inference. The result image with bounding boxes will be displayed.
Programmatic highlights:
- Model loading: `utils.load_model()` uses `ultralytics.YOLO` and Streamlit caching.
- Inference: `utils.detect_license_plate()` runs `model.predict()` and returns an annotated image.
---
## Configuration / Options
- **Model path**: `app.py` loads `./models/model.pt` once at startup.
- Replace this file with your trained YOLOv8 weights.
- You can retrain with Ultralytics and export to `.pt`.
- **Confidence threshold**: Set in `utils.detect_license_plate()` (default `conf=0.25`). Adjust as needed.
- **Image inputs**:
- Upload via Streamlit file uploader.
- Provide a URL; the app fetches it using `requests` and decodes with OpenCV.
---
## Contributing
Contributions are welcome! Please:
1. Fork the repo.
2. Create a feature branch.
3. Commit changes with clear messages.
4. Open a pull request describing the motivation and changes.
For larger changes, consider opening an issue first to discuss the proposal.
---
## License
This project is licensed under the MIT License. See [`LICENSE`](./LICENSE) for details.
---
## Acknowledgements / Credits
- [`ultralytics`](https://github.com/ultralytics/ultralytics) — YOLOv8 model and inference utilities.
- [`streamlit`](https://streamlit.io) — simple web app framework for ML demos.
- [`opencv-python`](https://pypi.org/project/opencv-python/), [`numpy`](https://numpy.org/), and [`Pillow`](https://python-pillow.org/) for image handling and visualization.
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