For context, I am a cofounder of Cord, a company in the current winter 2021 batch. We are focused on building software to improve data labelling for computer vision.
Before writing this I reviewed as many posts I could find(without having to go past the first page of Google) from people writing about their YC experience. There were fewer than I expected. Most people talk about interviewing and how to get in, which is probably the wider point of interest. I didn’t want this post to be redundant, so I decided to avoid talking about anything that collided with the…
I started academically in physics, but dropped out(sorry, took a leave of absence) of my PhD in the first year and did a long stint in quantitative finance. So out of all the possible topics for my first peer-reviewed published paper: portfolio optimisation, dark matter signatures, density functional theory, I ended up with the topic of…drawing rectangles on a colonoscopy video. I didn’t think it would come to this, but here we are. In reality though, drawing boxes on a colonoscopy video is one of the most interesting problems I have worked on.
The purpose of this tutorial is to demonstrate the power of algorithmic labelling through a real world example that we had to solve ourselves.* If you want to see the resulting full labelled dataset from this process, sign up here.
In a later post we will go over a more thorough description of what algorithmic labelling is, but, in short, algorithmic labelling is about harvesting all existing information in a problem space and converting it into the solution in the form of a program.
Here is an example of a algorithmic labelling that labels a short video of cars: