Artistic style transfer

A module to apply artistic style transfer using pytorch.


Model | Trainable | Inference | Pre-trained

Published by DEEP-Hybrid-DataCloud Consortium
Created: - Updated:

Model Description

Build Status

This is neural_transfer application using DEEPaaS API.

The module allows you to take the content of an image and reproduce it with a new artistic style using the style of a different image. The code is based on the Faster Neural Style Pytorch example that implements the method described in "Perceptual Losses for Real-Time Style Transfer and Super-Resolution" developed by Justin Johnson, Alexandre Alahia and Li Fei-Fei [1].

References

[1] Justin Johnson, Alexandre Alahi, Li Fei-Fei, Perceptual Losses for Real-Time Style Transfer and Super-Resolution, arXiv:1603.08155 [cs.CV]

Test this module

You can test and execute this module in various ways.

Excecute locally on your computer using Docker

You can run this module directly on your computer, assuming that you have Docker installed, by following these steps:

$ docker pull deephdc/deep-oc-neural_transfer
$ docker run -ti -p 5000:5000 deephdc/deep-oc-neural_transfer

Execute on your computer using udocker

If you do not have Docker available or you do not want to install it, you can use udocker within a Python virtualenv:

$ virtualenv udocker
$ source udocker/bin/activate
$ git clone https://github.com/indigo-dc/udocker
$ cd udocker
$ pip install .
$ udocker pull deephdc/deep-oc-neural_transfer
$ udocker create deephdc/deep-oc-neural_transfer
$ udocker run -p 5000:5000  deephdc/deep-oc-neural_transfer

In either case, once the module is running, point your browser to http://127.0.0.1:5000/ and you will see the API documentation, where you can test the module functionality, as well as perform other actions (such as training).

For more information, refer to the user documentation.

Train this module

You can train this model using the DEEP framework. In order to execute this module in our pilot e-Infrastructure you would need to be registered in the DEEP IAM.

Once you are registedered, you can go to our training dashboard to configure and train it.

For more information, refer to the user documentation.