Spaces:
Sleeping
Sleeping
File size: 2,404 Bytes
2d033ca d246850 2d033ca 3d3ace7 2d033ca d246850 |
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 |
---
title: ImageProof – Deep learning AI-generated Image Detection
emoji: 🚗
colorFrom: yellow
colorTo: blue
sdk: streamlit
sdk_version: "1.40.0" # latest stable streamlit
app_file: app.py
pinned: false
license: mit
---
# ImageProof - AI Image Authenticity Detector 🧠
A Streamlit-based web application that uses a fine-tuned EfficientNet-B3 model to detect whether images are AI-generated or real.
## Table of Contents
- [Demo](#demo)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)
## Demo
Check out the application in action with these demo files:
<video controls>
<source src="demo/demo.mp4" type="video/mp4">
Your browser does not support the video tag.
</video>
------------------------
## Features
- **Image Upload**: Support for JPG, JPEG, and PNG files.
- **URL Input**: Analyze images directly from web URLs.
- **Real-time Prediction**: Instant classification with confidence scores.
- **Interactive UI**: Built with Streamlit for easy use.
- **Model Integration**: Leverages EfficientNet-B3 for accurate detection.
## Installation
To get started, clone the repository and set up a virtual environment.
```bash
# Create a virtual environment
python -m venv .venv
# Activate it
# On Linux/Mac:
source .venv/bin/activate
# On Windows:
.venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
```
## Usage
Run the application using Streamlit:
```bash
streamlit run app.py
```
1. Open the app in your browser.
2. Choose to upload an image or enter an image URL.
3. View the prediction results, including the label (AI-generated or Real) and confidence score.
Example prediction output:
- Label: 🧠 AI-generated
- Confidence: 0.95
## Contributing
Contributions are welcome! Please fork the repository and submit a pull request. Ensure code follows best practices and includes tests.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Acknowledgements
- Built with [Streamlit](https://streamlit.io/) for the web interface.
- Model based on [EfficientNet](https://github.com/lukemelas/EfficientNet-PyTorch) and [timm](https://github.com/rwightman/pytorch-image-models).
- Thanks to the open-source community for PyTorch and related libraries.
|