Style Transfer Project
- 0 Collaborators
This project implements Neural Style Transfer (NST) using Deep Convolutional Neural Networks (DCNNs) to blend the artistic style of one image with the content of another. Built using Python and TensorFlow, it leverages a pre-trained VGG-16 model to extract and optimize style and content features. Th ...learn more
Project status: Published/In Market
Intel Technologies
Intel Integrated Graphics,
Intel CPU,
oneAPI
Overview / Usage
This project implements Neural Style Transfer (NST) using Deep Convolutional Neural Networks (DCNNs) to blend the artistic style of one image with the content of another. The model extracts features from a pre-trained VGG-16 network and optimizes the content-styled image through iterative refinement.
Traditional digital art creation requires manual effort and artistic skills. This project automates artistic transformations using AI, making high-quality style transfer accessible to non-artists.
It can be used in ways such as -
- Digital Art & Content Creation: AI-powered filters for social media, graphic design, and game assets.
- Real-time Style Transfer: Could be optimized for video processing and live applications.
- AI Research: Helps in understanding feature extraction and optimization techniques in deep learning.
Methodology / Approach
In this project, I use Neural Style Transfer (NST) to blend the artistic style of one image with the content of another using deep learning. I rely on VGG-16, a pre-trained model, to extract important features from both images—preserving the structure of the original while applying the textures and patterns of the chosen style. The process works by optimizing an initial image until it matches the content image but with the desired artistic style applied. This is achieved using a specialized loss function that balances content and style features. To improve efficiency and quality, I use optimization techniques like L-BFGS or Adam optimizer. The project is developed in Python, using TensorFlow or PyTorch for AI processing and OpenCV & Matplotlib for handling and displaying images. Moving forward, I plan to implement real-time style transfer for videos, optimize performance with Intel oneAPI and OpenVINO, and explore advanced AI models like StyleGAN for even better artistic results.
Technologies Used
- Programming Language: Python
- Deep Learning Frameworks: TensorFlow, PyTorch
- Pre-trained Model: VGG-16
- Intel Technologies: oneAPI