Landmark Detection In the previous post we saw how we can get a neural network to output \(4 \) numbers: \(b_{x} \), \(b_{y} \) ,\(b_{h} \), and \(b_{w} \) to specify the bounding box of an object we want neural network to localize. In more general cases we can have a neural network which outputs just \(x \) and \(y \) coordinates of important points in the image, sometimes called landmarks.  Let’s see a few…
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Object Localization Object detection is one of the areas of computer vision that’s exploding and it’s working so much better than just a couple years ago. In order to build up object detection we first learn about object localization. Let’s start by defining what that means. We have already said that the image classification task is to look at a picture and say is there a car or not. Classification with localization means not only do…
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Data Augmentation Most computer vision tasks could use more data and data augmentation is one of the techniques that is often used to improve the performance of computer vision systems. The computer vision is a pretty complicated task. For an input image we have to figure out what is in that picture and we need to learn a decently complicated function to do that. In practice, having more data will help  for almost all computer…
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Transfer Learning Last time we talked about Inception Network and some other Neural Network architectures. All of these Neural Network architectures are really big and hard to train and we have a problem. So if we are building a computer vision application rather than training a neural network from scratch we often make much faster progress if we download the network’s weights. In other words someone else has already trained the network architecture and we…
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Inception Network In the previous post we’ve already seen all the basic building blocks of the Inception network. Here, we will see how to put these building blocks together and build it. An example of an Inception module To explain how Inception Network works we will consider a few steps: The third step may be (look at a red rectangle) just using a \(1\times 1 \) convolution, maybe with \(64\) filters so we get a $latex  28\times…
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