Image style transfer using Torch
![license](https://img.shields.io/github/license/Radonirinaunimi/Style-Transfer?style=flat-square) ![repo size](https://img.shields.io/github/repo-size/Radonirinaunimi/Style-Transfer?style=flat-square) #### Description —————-
Timst is a python package based on [pyTorch]() that extracts the features of an image and tranfers them into another; such a technique is known as image style transfer. The following implementation is a re-implementation of this [code]() that is based on the following [scientific paper](https://arxiv.org/pdf/1508.06576.pdf). The architecture is based on [Convolutional Neural Network](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf) (CNN) which is one of the applications of [Deep Learning](https://en.wikipedia.org/wiki/Deep_learning).
#### How to install
There are different ways the package can be installed: * By clonning this repository and running the following command in the terminal (you might require <kbd>sudo</kbd> privilege) `bash git clone https://github.com/Radonirinaunimi/Style-Transfer cd Style-Transfer/ python setup.py install --user ` * By installing it through the Python Package Index (PyPI) `bash pip install timst --upgrade `
#### How to use
To use timst, just run the following: `bash timst -i [IMAGE_TO_BE_STYLED] -s [STYLE_TO_BE_APPLIED] [-n NUMBER_OF_ITERATIONS] `
#### For bugs and feature request
Open an [issue](https://github.com/Radonirinaunimi/Style-Transfer/issues/new/choose) or a [pull request](https://github.com/Radonirinaunimi/Style-Transfer/compare).
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