A complete guide to Facebook’s Detection Transformer (DETR) for Object Detection.


The DEtection TRansformer (DETR) is an object detection model developed by the Facebook Research team which cleverly utilizes the Transformer architecture. In this post, I’ll go through the inner-workings of DETR’s architecture to help provide some intuition on it’s moving parts. The colab notebook accompanying this tutorial can be found here:

Below, I’ll explain some of the architecture, but if you just want to see how to use the model, feel free to skip to the coding section.

The Architecture

The DETR model consists of a pretrained CNN backbone (like ResNet)…

The Shape of Data


Topological Data Science(TDA) has been bursting with new applications in machine learning and data science this year. The central dogma of TDA is that data (even complex, and high dimensional) has an underlying shape, and by understanding this shape we can reveal some kind of important information about the process which generate it. For a great survey on TDA for data scientists, check out [1]. One of the ways which TDA helps understand this shape is through persistence homology, which will be briefly explained in this article. For actual computations, giotto-tda is a great toolkit for calculations and examples. …

Mathematics for Selling Drugs and Changing Lightbulbs


Renewal processes are stochastic processes which model events which occur randomly in time. These events are referred to as renewals or arrivals. In this article, I’ll explain some of the basic theory behind renewal processes, and give a very interesting application of a renewal process.

Renewal Processes are generalizations of Poisson Processes, which I won’t talk about here. To learn more about Poisson Processes, the following article does a pretty good job of explaining it.

The Renewal Process

As a simple metaphor, let’s imagine a single lightbulb maintained by a very diligent janitor. The janitor replaces the lightbulb immediately after it burns out…

A formal Introduction


How do we define distance? In the cartesian plane, we know that the distance between two points (x₁, y₁) and (x₂, y₂) is given by the distance formula:

This geometric definition is usually how we think of distance. But in many real world situations, we can’t think of our data points in a way that would make sense geometrically.

For example, let’s say that we have a sequence of stock prices for two different companies. Lets say that f(t) is a function representing the price of a stock at time t. days and g(t) represents the price of or some…

A Brief Intuitive Explanation


In the 19th century, a Scottish botanist named Robert Brown noticed that pollen grains which were suspended in water displayed random movements. His work revealed this random movement is in fact a general property of matter in that state, and this phenomena was termed Brownian Motion.

Brownian Motion has numerous applications like Physics, Engineerging, Finance, Economics, etc. Most notably, Albert Einstein used Brownian Motion in his work to prove the existence of atoms. To build an intuition for what exactly Brownian motion is, think of it as some kind of random movement in space.

Random Movements in Space

We want to generate some kind…

Jacob Briones

Specializing in explainable AI, mathematics, and physical sciences through the use of visualization, computer science, and creative writing.

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