In the last post, we talked about Siamese Network, but we didn’t talk how to actually define an objective function to make our neural network learn. So, in order to do that, here we will define Triplet Loss. Triplet Loss One way to learn the parameters of the neural network, which gives us a good encoding for our pictures of faces, is to define and apply gradient descent on the Triplet loss function. Let’s see…
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Siamese Network The job of the function \(d\), which we presented in the previous post, is to use two faces and to tell us how similar or how different they are. A good way to accomplish this is to use a Siamese network. We get used to see pictures of \(convnets \), like these two networks in the picture below. We have an input image, denoted with \(x^{(1)}\), and through a sequence of \(Convolutional \),…
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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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Convolutional Neural Network – An Example In previous posts (CNN 004, CNN 005 and CNN 009) we have defined all building blocks for building a full convolutional neural network. Let’s now look at an example of a convolutional neural network (CNN). Let’s say that we have a \(32 \times 32 \times 3 \) dimensional image as an input to the CNN. So it’s an RGB image and supoose we want to try to do a handwritten…
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