In this post we will see how to implement Logistic Regression in TensorFlow. Let’s generate a dataset using random values. It’s very crucial to check the size of your array. Let’s split our dataset into a train and test set. For the training set 80% of the original dataset would be used, while the remaining 20% would be used for testing. Let’s set the parameters which would be used in training our model. Now, let’s…
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Object Detection We have learned about object localization as well as landmark detection, now let’s build an object detection algorithm. In this post we’ll learn how to use a \(convnet \) to perform object detection using a Sliding windows detection algorithm. Car detection – an example Let’s say we want to build a car detection algorithm. Some examples of a training set We can first create a labeled training set \((x,y) \) with closely cropped…
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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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