You've heard about Machine Learning and AI - and you want to see what all the fuss is about. But you don't want to spend all your time installing bazel and Jupyter? Or maybe you're running an old computer or your only computer is a phone! What now, give up? Never! Thanks to Google Colab, you can run TensorFlow in a browser window, and all the computation is handled on Google's cloud service for free. It's a great way to dabble, without all the setup

We've hacked together a Colab notebook that will use your computer/laptop/phone camera or webcam to get images which are then categorized with the Mobilenet v2 model to detect one of ~1000 different objects it recognizes. This way you can see what Mobilenet v2 can do, instantly!

For this tutorial you will need a free Google account, a computer, phone or tablet with a camera or webcam, and a recent browser

What is Object Detection?

Object Detection algorithms look at pictures and list out the objects they see. Take look at the example below:

The algorithm produces two outputs here:

  • The identified object, given both by name (water bottle) and an id number
  • Confidence Level, a measure of the algorithm's certainty

Early object detection algorithms used basic heuristics about the geometry of an object (for example, a tennis ball is usually round and green). While these had some successes, they were difficult to create and were prone to some hilarous false-positives.

Mobilenet v2

In recent years, a technology called neural nets has made it possible to let computers develop the heuristics on their own, by showing them a large number of examples. Mobilenet v2 is one of the well-known models beacuse it's optimized to run on devices like your cell phone or a raspberry pi.

The authors of Mobilenet v2 claim it runs in 143ms on a Pixel 1. It can recognize 1000 different objects, including:

  • animals, like fish, birds, and turtles
  • household items, like brooms, coffee mugs, and pens
  • airplanes, golf carts, mopeds

These objects are taken from a popular set of images used to develop object detection algorithms.

This guide was first published on Sep 19, 2019. It was last updated on Mar 08, 2024.

This page (Welcome) was last updated on Mar 08, 2024.

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