The next trend: An ML model that turns you into an illustration.

Vijish Madhavan — Tue, 09 Feb 2021 13:11:36 +0000

Deep Learning and Computer Vision has evolved and done wonders time and again. Today I would like to introduce my new project ‘Toon-Me’ after the phenomenal success of ArtLine(Over 2.7k GitHub Stars), ‘Toon-Me’ uses deep learning algorithms to achieve fine quality line art portraits. It is currently the most trending
topic in both GitHub and paperswithcode.

https://github.com/vijishmadhavan/Toon-Me

The model has been built using an custom dataset using many different algorithms, derived from some research papers self-attention, progressive resizing and generator loss. It shows how stacking all the methods can generate high-quality results. Primarily PyTorch and Fastai libraries are used. It generates fine lines/edges in the sketch image, which is better than most existing methods. Try out the demo from the Colab Notebook provided in the Github repo with any portrait picture which is expected in an URL and then converted to image formats. You can clone the repository or tweak the code to use your local image file and within less than 2 minutes (executing with GPU).

ArtLine, Create Amazing Line Art Portraits

Vijish Madhavan — Wed, 09 Dec 2020 11:00:31 +0000

I am Vijish Madhavan, My experience is into marketing. Coding ka kida caught me last year.

This is just a fun project!!

Introducing Artline, A Deep Learning based project for creating line art portraits.

As I said it was just a fun project, but the response I got surprised me. The results seems to be state of the art comparing it to previous models like APDrawingGan and U-2-Net. You are free to judge as its opensource.

GitHub Link

Check out for pretty images, scroll through the repo link.

https://github.com/vijishmadhavan/ArtLine

Gist of the project

Note: Being a newbie my code and documentation would be bad, I will improve with time.

Technical Details

Self-Attention (https://arxiv.org/abs/1805.08318). Generator is pretrained UNET with spectral normalization and self-attention. Something that I got from Jason Antic's DeOldify(https://github.com/jantic/DeOldify), this made a huge difference, all of a sudden I started getting proper details around the facial features.

Progressive Resizing (https://arxiv.org/abs/1710.10196),(https://arxiv.org/pdf/1707.02921.pdf). Progressive resizing takes this idea of gradually increasing the image size, In this project the image size were gradually increased and learning rates were adjusted. Thanks to fast.ai for intrdoucing me to Progressive resizing, this helps the model to generalise better as it sees many more different images.

Generator Loss : Perceptual Loss/Feature Loss based on VGG16. (https://arxiv.org/pdf/1603.08155.pdf).

Surprise!! No critic,No GAN. GAN did not make much of a difference so I was happy with No GAN.

You can follow me on twitter @Vijish68859437

DEV Community: Vijish Madhavan

The next trend: An ML model that turns you into an illustration.

ArtLine, Create Amazing Line Art Portraits

Gist of the project