Neural Style Transfert(NST)

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.

What is NST ?

Technical specifications

Property	Value
Framework	PyTorch
Device	MAC M2
Optimizer	LBFGS
Time for 1 image	~20 min

How does it work ?

Extract characteristics with VGG19

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

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Optimization Loop

The ititial image can be:

• content
• style
• random noise

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Loss Function

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

• 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.

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Style Loss

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Content Loss

Paper:Neural Style Transfer: A Review