In this post, we will learn how the non-max suppression algorithm allows us to overcome multiple detections of the same object in an image. Let's go through an example! Let's say we want to detect pedestrians, cars, and motorcycles in this image. If we look at the picture above we can see that there are two cars. Each of these two cars has one midpoint so it should be assigned to just one grid cell which then actually predicts that there is a car in the picture.

In this post, we will finish with the theory behind object detection. We will combine last few posts together to complete the \(Yolo \) object detection algorithm. As the first step, let's see how we can construct our training set. Let's suppose that we are trying to train an algorithm to detect three objects: pedestrians, cars and motorcycles. In addition, we will need to explicitly define the background class, so we just have \(3 \) class labels.