The process of transferring an image's aesthetic style to
another is known as neural style transfer. The algorithm takes three images: an
input image, a content picture, and a style picture, and modifies the input to
match the content of the content image and the artistic style of the style
image.
The Basic Principle behind Neural Style Transfer
The basic idea behind neural style transfer is to establish
two distance functions: one to describe how different the content of two images
is, Lcontent, and another to characterize the difference in style between the
two images, Lstyle.Then, given three images: the desired style image, a desired
content picture, and the input picture (initialized with the content image), we
strive to alter the input image so that its content distant from the content
picture and its style distance from the style image are as small as possible.m takes three images: an
input image, a content picture, and a style picture, and modifies the input to
match the content of the content image and the artistic style of the style
image.
 |
| Content Image |
 |
| Style Image |
Importing Packages and Selecting a Device
Below is a list of the packages needed to implement the
neural transfer
- torch, torch.nn, numpy (indispensables packages for neural networks with PyTorch)
- torch.optim (efficient gradient descents)
- PIL, PIL.Image, matplotlib.pyplot (load and display images)
- torchvision.transforms (transform PIL images into tensors)
- torchvision.models (train or load pre-trained models)
- copy (to deep copy the models; system package)
General steps to perform style transfer:
- Visualize data
- Basic Preprocessing/preparing our data
- Set up loss functions
- Create model
- Optimize for loss function
Code:
You can find the complete code for this article in the given URL below.
https://www.kaggle.com/smitasahoo/neural-style-transfer-using-pytorch
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