Style Transfer with Neural Networks

examples/Style Transfer with Neural Networks/README.md

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+# 🎨 Style Transfer with Neural Networks 🖼️
+
+Welcome to the **Style Transfer with Neural Networks** project! In this project, we explore the application of neural networks to perform artistic style transfer, allowing us to blend one image's artistic style with another's content. 
+
+## 📖 Table of Contents
+
+1. [Project Overview](#-project-overview)
+2. [Setup & Installation](#-setup--installation)
+3. [File Structure](#-file-structure)
+4. [How to Run](#-how-to-run)
+5. [Sample Run](#-sample-run)
+6. [Concepts Behind the Project](#-concepts-behind-the-project)
+7. [Technologies Used](#-technologies-used)
+8. [Parameters & Tuning](#-parameters--tuning)
+9. [License](#-license)
+10. [Contact](#-contact)
+
+## 📚 Project Overview
+
+Style transfer is a deep learning technique that involves using Convolutional Neural Networks (CNNs) to separate and combine the style of one image with the content of another image. This project implements a neural style transfer model using PyTorch and pre-trained VGG19.
+
+### Key Features:
+- Use of **pre-trained neural networks** for feature extraction (VGG19)
+- Content loss and style loss based on **Gram matrices**
+- Supports custom content and style images
+- Optimization with **L-BFGS** optimizer for faster convergence
+
+## 🛠️ Setup & Installation
+
+To get started with this project, you'll need to install the following dependencies:
+
+```bash
+pip install torch torchvision matplotlib Pillow
+```
+## 📁 File Structure
+| File/Folder                              | Description                                                         |
+|------------------------------------------|---------------------------------------------------------------------|
+| `Style Transfer with Neural Networks.ipynb` | The main notebook implementing style transfer                       |
+| `images/`                                | Directory containing sample content and style images               |
+| `output/`                                | Folder to store the generated images                               |
+| `requirements.txt`                       | List of dependencies                                               |
+
+## 🚀 How to Run
+1. Clone the repository and navigate to the project folder:
+    ```bash
+    git clone https://github.com/yourusername/style-transfer-project.git
+    cd style-transfer-project
+    ```
+
+2. Run the Jupyter Notebook to perform style transfer on your images. You can upload your custom content and style images in the notebook.
+
+3. Adjust parameters like `content_weight`, `style_weight`, and `num_steps` for different results.
+
+4. View and save the generated images in the `output/` folder.
+
+## 📸 Sample Run
+
+- **Content Image**: Defines the structure and objects in the final image.
+- **Style Image**: The textures, colors, and patterns are provided.
+
+| Content Image                                                                                  | Style Image                                                                                   |
+|------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------|
+| ![Content Image](https://raw.githubusercontent.com/alo7lika/explainableai/refs/heads/main/examples/Style%20Transfer%20with%20Neural%20Networks/content%20image.jpg) | ![Style Image](https://raw.githubusercontent.com/alo7lika/explainableai/refs/heads/main/examples/Style%20Transfer%20with%20Neural%20Networks/style%20image.jpg)|
+
+## 🔬 Concepts Behind the Project
+- **Content Representation**: Extracted from deeper layers of the neural network to capture the high-level structures in the image.
+- **Style Representation**: Captured using the Gram matrix of feature maps, representing the correlations between different feature maps.
+- **Optimization**: The neural network optimizes a random noise image to minimize content and style loss, blending the content and style.
+
+## 🧠 Technologies Used
+- Python 🐍
+- PyTorch for deep learning
+- Jupyter Notebook for interactive coding
+- Matplotlib for visualizations
+
+## 📊 Parameters & Tuning
+You can adjust the following parameters to control the output:
+
+| Parameter       | Default Value | Description                      |
+|------------------|---------------|----------------------------------|
+| `content_weight` | 1e5          | Weight for the content loss      |
+| `style_weight`   | 1e10         | Weight for the style loss        |
+| `num_steps`      | 300          | Number of optimization steps      |
+| `learning_rate`  | 0.01         | Learning rate for the optimizer   |
+
+
+## 📄 License
+This project is licensed under the MIT License - see the LICENSE file for details.
+
+## 💬 Contact
+If you have any questions, feel free to reach out to me at [alolikabhowmik72@gmail.com]
+