26 May 2019, Samuel Hinton

Neural Style Transfer

A small python project I forked to learn neural styling. That is, to learn an artisitic style and apply it to arbitrary images. Used not for work, but to create a birthday gift for my sister.

I normally suck at giving presents. Horrifically. Probably because I’m a nightmare to buy for - I hate useless gifts. Ornaments, decorations… “things” which aren’t functional.

It’s all clutter.

And because I don’t like receiving it, I don’t give it. Which means the struggle is always for a functional gift. But not this time! My sister has a cat called Ducky. Now, Ducky may not like me very much (breaks my heart), but Ali sure likes Ducky. So heck, why not print out a nice portrait of Ducky onto something. But a plain portrait just looks gaudy to me. Got to make it art. And then I remembered a whole bunch of amazing images I saw using deep style learning. And I thought - how hard could it be to fork an existing project and adapt it to my needs. Let other people do the hard work, and just reap the benefits. For a test, I took this picture of Ducky below as the original source and trained it on van Goph’s Stary Night (also shown below the image doesn’t immediately spring to mind).

Three thousand steps of learning the style and transferring it onto the original image later, we have this masterpiece. It took a while figuring out learning algorithm, rates, resolutions, etc to make something that looked good, but I like to think I succeeded!

With the proof of concept done, I then wrote some scripts to submit bulk jobs to the GPU’s and generated images for dozens of styles. You can see all the styles in the github repo if you’re curious. Here are a few nice samples from the styles you can flick through.

In the end, I had two printed - the first the cosmic cat style and the second the low-poly orange cat. I had them printed into cushions for the living room couch, and what can I say, I think they’re fantastic!