How to Build a Deep Learning Classification System for Less than $1000 USD

Aling

Introduction
Deep learning is set to alter the machine vision landscape in a big way. It is enabling new applications and disrupting established markets. As a product manager with FLIR, I have the privilege of visiting companies across a diverse range of industries; every company I visited this year is working on deep learning. It’s never been easier to get started, but where do you begin? This article will provide an easy-to-follow guide to building a deep learning inference system for less than $1000.

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annejones101

Collect images of each person you want to classify as well as of strangers to create your training set and preprocess it. You should use data augmentation methods that automatically generate related, but new, images from the collected data to increase the size of the data set. Your data set will likely be unbalanced meaning that it doesn’t contain the same number of observations for each class. You will need to compensate for this otherwise your model will overemphasize classes with more observations and its accuracy will suffer. Lastly, your data set needs to be converted into a data format suitable for the training process. You should plan for a training set of at least a 1000 observations for each class. Note that the terms observations and samples are used interchangeably in this article.