https://github.com/alihassanml/neural-style-transfer
Welcome to the Neural Style Transfer app! This application allows you to apply artistic styles to your images using a deep learning model.
https://github.com/alihassanml/neural-style-transfer
deep deeplearning machine-learning neuralstyletransfer style
Last synced: 8 months ago
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Welcome to the Neural Style Transfer app! This application allows you to apply artistic styles to your images using a deep learning model.
- Host: GitHub
- URL: https://github.com/alihassanml/neural-style-transfer
- Owner: alihassanml
- License: apache-2.0
- Created: 2024-08-10T12:49:51.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-10T12:53:42.000Z (about 1 year ago)
- Last Synced: 2025-01-01T15:11:43.731Z (9 months ago)
- Topics: deep, deeplearning, machine-learning, neuralstyletransfer, style
- Language: Jupyter Notebook
- Homepage:
- Size: 1.6 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Neural Style Transfer
Welcome to the **Neural Style Transfer** app! This application allows you to apply artistic styles to your images using a deep learning model.
## Features
- **Upload Images:** Choose a content image and a style image.
- **Style Transfer:** Apply the style of the uploaded style image to the content image.
- **Visualize Results:** View the styled output alongside the original images.
## How to Use
1. **Upload Content Image:**
- Select an image that you want to apply the style to.
2. **Upload Style Image:**
- Select an image that provides the artistic style.
3. **See the Result:**
- The application will process the images and display the styled result.
## Requirements
- Python 3.x
- Streamlit
- TensorFlow
- TensorFlow Hub
- PIL (Pillow)
- NumPy
## Installation
To set up the application, follow these steps:
1. **Clone the Repository:**
```bash
git clone https://github.com/alihassanml/Neural-Style-Transfer.git
```
2. **Navigate to the Directory:**
```bash
cd Neural-Style-Transfer
```
3. **Install Dependencies:**
```bash
pip install streamlit tensorflow tensorflow_hub pillow numpy
```
4. **Run the Application:**
```bash
streamlit run app.py
```
5. **Open Your Browser:**
- Visit `http://localhost:8501` to use the application.
## Notes
- Ensure that your images are in JPG, JPEG, or PNG format.
- The images will be displayed at a reduced size to fit the layout.
## Author
- **Ali Hassan** - [alihassanml](https://github.com/alihassanml)
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.