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Neural Style Transfer

This repository contains an implementation of the neural style transfer technique as described in the paper "A Neural Algorithm of Artistic Style" by Leon A. Gatys et al. The method utilizes convolutional neural networks (CNNs) to blend the content of one image with the style of another, creating visually striking results.

Project Overview

The goal of this project is to perform Neural Style Transfer (NST) using a pre-trained VGG19 network. The implementation is modular, allowing for easy customization and experimentation.

Project Structure

Neural_Style_Transfert/
├── data/
│   ├── content/                    # Folder for content images
│   ├── style/                      # Folder for style images
│   └── generated/                  # Folder for generated output
├── src/
│   ├── NST_Model.py                # Neural Style Transfer implementation
│   ├── VGG19.py                    # Pre-trained VGG19 model utility
├── neural_style_transfer.ipynb     # Jupyter Notebook for experimentation
├── README.md                       # Project documentation
└── requirements.txt                # Python dependencies

Results

Here are some examples of the generated results:

Content Image Style Image Generated Result
Content Style Result

Getting Started

1. Clone the Repository

git clone https://github.com/safina57/Neural-Style-Transfer.git

2. Install Dependencies

Make sure you have Python 3.8 or later installed. Then, install the required dependencies:

pip install -r requirements.txt

3. Prepare Your Images

  • Place your content images in the data/content/ folder.
  • Place your style images in the data/style/ folder.

Using the Jupyter Notebook

For experimentation or detailed control, you can use the included Jupyter Notebook:

jupyter notebook neural_style_transfer.ipynb

This provides a step-by-step breakdown of the style transfer process.

Key Features

  • Pre-trained VGG19 Model: Leverages the pre-trained VGG19 network for feature extraction.
  • Customizability: Adjust content/style weights, learning rate, and optimizer to experiment with different results.

Dependencies

This project requires the following Python packages:

  • torch, torchvision
  • Pillow
  • matplotlib
  • numpy
  • jupyter

You can find the full list in the requirements.txt file.

References

  1. Gatys, L. A., Ecker, A. S., & Bethge, M. (2015). A Neural Algorithm of Artistic Style. arXiv preprint arXiv:1508.06576.
  2. PyTorch Documentation: https://pytorch.org