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Category: Deep Learning

#030 CNN One-Shot Learning

One – Shot Learning Solving the one-shot problem represents a challenge of face recognition task. This means that for most face recognition applications we need to recognize a person having only a single image or given just one example of that person’s face. Typically, deep learning algorithms don’t work well if there is only one training example. However, we will show how this problem can be tackled.  Let’s say that we have a database of…

#029 CNN Yolo Algorithm

YOLO ALGORITHM 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…

#028 CNN Anchor Boxes

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…

#027 CNN Non-Max Suppression algorithm

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…

#026 CNN Intersection over Union

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…