“Use a custom object detection model to automatically track your inventory with Microsoft Azure IoT Central integration!
In today’s tutorial, I will show you how to create a smart inventory tracker using object detection, powered by deep learning, with just a Raspberry Pi 4 and a camera. We will apply transfer learning on the YOLOv4 tiny model to identify custom objects, then use a simple python script to parse the model’s output to produce a count of each object. Finally, we will also integrate the application with Azure IoT Central so that we can monitor our inventory remotely and conveniently.
Have you ever passed by a grocery store but found yourself unsure of whether you needed to get that extra carton of milk? Well, what if there was some way to have eyes on the inside of our fridge to update us with that information? Today, with machine learning and IoT infrastructures, we are going to turn that convenience into reality.
To summarize, we want to create two key features in our project. First, to automatically count the items in our fridge. Then, to be able to access the data remotely when we need it. Before we begin, let’s take a look at a short video demonstration below.”