For 2022, I decided to build a system that helps me take a picture of myself every day. The goal is to make a time lapse of how I change over a single orbit of the earth. I use facial feature detection to find my eyes and then instruct myself how to move to get into the same position for ever picture.

This Instagram post shows how the facial tracing works:

https://www.instagram.com/tv/CYNXVkzKs4X/?utm_medium=copy_link

But then I had the idea:

What if I train a variational auto encoder on the pictures too. So using TensorFlow, I built a VAE using a number of 2D convolutional layers as well as a few others to compress the data into a 16-length latent vector.

Once it was trained l, I built a webapp that allows you to explore the latent space.

Try it out yourself

I’ll retrain the VAE at the end of every month of pictures.

Participated a live Twitch stream with the folks at sparkdai

How to Train a Computer Vision Model

https://youtu.be/urCY_2-n2r0

In this episode we will use the labels collected last week and we will experiment with an object detector. In particular we will move our first steps towards training a Computer Vision model using Colab and Pytorch.


If you want to learn more about Sparkd check out their website at https://sparkd.ai

Had a great time with a bunch of awesome students talking about Jupyter Notebooks, Machine Learning, Computer Vision and how to put all that together at "mAIówka z WRSS" organized by the Faculty Students' Council of the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at AGH. Thanks to everyone who attended and participated!

Notebook source for the lab is here: https://github.com/unjello/workshop-virtual-camera

Turned a Case of Soda into a Boombox for SXSW. Lead iOS engineer and technical project manager. Responsible for computer vision prototyping with OpenCV, hiring and managing a worldwide team of 3-4 audio and computer vision contractors, hands-on iOS UI construction with Objective-C, social media integration, and more.

Building out a custom mobile computer vision application to aid with retail product compliance! Scaling deep learning and trying to smush it into handheld devices with a UI is wild.