Anchor Boxes As we can see from our previous posts, object detection is quite challenging. This is the final challenge that we are going to explain. Then, we will develop a holistic YOLO algorithm. One scenario that we may encounter in practice is that several objects of interest are present in the same grid cell. This is shown in the figure below. In this case, we can use the idea of \(Anchor\enspace boxes \) to…
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Non-Max Suppression 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…
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Intersection over Union In this post, we will learn about a function called Intersection over union. We will use this to make our object detection algorithm to work even better. How do we tell if our object detection algorithm is working well? When doing the object detection our task is to localize the object in the best possible way. Take a look at the picture above we can see that there are two bounding boxes…
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Introduction to Deep Learning as taught by ANDREW NG, DEEP LEARNING course LECTURE NOTES Deep learning is a sub-field of machine learning that is rapidly rising and is driving a lot of developments that has already transformed traditional internet businesses like web search and advertising. In the past couple of years, deep learning has gotten good from reading X-ray images, to delivering personalized education, precision agriculture, and even to self-driving cars. Over the next decades,…
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