Inspiration

Seomething to make us laugh

What it does

It tells you which celebrity you look most alike (limited by those currently in the ML database)

How I built it

Messenger api was used with a node backend was used to interact with messenger users on Facebook. Flask was used to provide a backend for the pytorch model to provide predicts on user uploaded images. Communication between Node and Flask using POST. Machine learning architecture used was a pretrained ResNext model with the final layer retrained using transfer learning.

Challenges I ran into

Challenge of collecting data for reliable machine learning outcomes Time needed for training a deep learning model (with limited GPU access). Commuincation between Node and Flask using POST .

Accomplishments that I'm proud of

Getting a messenger app up and running that interfaced with deep learning tech.

What I learned

Many things, but am glad I learned how to communicate between two servers using POST.

What's next for CelebCheck

A better pytorch model. Increase the number of Celebs in the database. Show image of the matching celeb.

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