This project is an implementation of Neural Style Transfer using PyTorch. Neural Style Transfer is a deep learning technique that merges the style of one image with the content of another, creating visually striking results by blending artistic features with photographic details.

The VGG19 pre-trained model is a convolutional neural network with 19 layers, including 16 convolutional layers and 3 fully connected layers, featuring 3x3 convolutional filters and max pooling, totaling approximately 143.67 million parameters for image classification tasks.

we start by extracting the characteristics of content and style

• content
• style
• random noise

$$ L_{\text{total}} = \alpha L_{\text{content}} + \beta L_{\text{style}} + \gamma L_{\text{TV}} $$

• L_Content is the content loss.
• L_Style is the style loss.
• L_TV is the total variation loss.
• alpha, beta, and gamma are hyperparamters for each loss.

Paper:Neural Style Transfer: A Review