Inspiration
One of our team members read about Generative Adversarial Networks (GAN) in his research seminar, so we looked for a new domain to apply GANs to. We decided to pursue creating logos because the training files were easy to obtain, and the website would be useful, as thousands of start-ups are created everyday that want to find a logo design.
What it does
From a bunch of existing company logos, a model is built that can assemble pieces of the existing logos together. The output of the model is loaded onto a website when a user requests to see more logo designs.
How I built it
The GAN network was taken from https://github.com/carpedm20/DCGAN-tensorflow. This originally ran on face data, and we modified the code and data parameters so the algorithm worked for logos.
Challenges I ran into
Fixing some bugs of the pre-existing GAN software.
Accomplishments that I'm proud of
Getting the GAN to run. Polishing GAN outputs with Paint.net for clearer visualization and as a demonstration of moving from logo prototype to possible actual logo.
What I learned
Git branching and GAN implementation in Python.
What's next for Logo GANerator
Generate logos based on user keywords.

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