/demo_artistic_neural_networks

This tutorial is one of the demos used in the course “Introduction to Machine Learning” at MINES ParisTech - PSL Research University, lectured by Simon Tamayo. Acknowledgement: Most of this demo was adapted from the PyTorch tutorial on Style Transfer (https://pytorch.org/tutorials/advanced/neural_style_tutorial.html), which explains how to implement the Neural-Style algorithm developed by L. A. Gatys, A. S. Ecker and M. Bethge.

Primary LanguageJupyter Notebook

Style transfer using deep artificial neural networks

This tutorial is one of the demos used in the course “Introduction to Machine Learning” at MINES ParisTech - PSL Research University, lectured by Simon Tamayo.

Here is what Style transfer looks like:

  1. You have an input_image:

input_image_ex

  1. You have an input_style (that you want to apply to your image):

input_style_ex

  1. The output a new image with keeps the features of your input_image while changing its style:

output_ex

Acknowledgement

Most of this demo was adapted from the PyTorch tutorial on Style Transfer (https://pytorch.org/tutorials/advanced/neural_style_tutorial.html), which explains how to implement the Neural-Style algorithm developed by L. A. Gatys, A. S. Ecker and M. Bethge.

Getting Started

Before running this code make sure you have Python3 and the packages: PyTorch, PIL, TorchVision and Matplotlib.

VGG19

This tutorial uses the convolutional neural network VGG-19, which was trained on more than a million images from the ImageNet database. The network is 19 layers deep and can classify images into 1000 object categories

Built With

  • PyTorch - Deep learning platform
  • TorchVision - package withpopular datasets and model architectures for computer vision.

This project is licensed under the MIT License - see the LICENSE.md file for details.