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Category: Deep Learning

025 CNN Bounding Box Predictions

#025 CNN Bounding Box Predictions

Bounding box predictions In the last post, we learned how to use a convolutional implementation of sliding windows. That’s more computationally efficient, but it still has a problem of not outputting the most accurate bounding boxes.  In this post, we will see how we can obtain more accurate predictions of bounding boxes.  Output accurate bounding boxes With sliding windows, we take the sets of windows that we move throughout the image and we obtain a set of sliding…
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017 CNN Inception Network

#017 CNN Inception Network

\(Inception\enspace network \)  Motivation for the \(Inception\enspace network \): In the last post we talked about why \(1\times 1 \) convolutional layer can be useful and now we will use it for building the \(Inception\enspace network \). When designing a layer for a \(convnet \) we might have to choose between a \(1 \times 3 \) filter, a \(3 \times 3\) or \(5\times 5\) or maybe a pooling layer. An \(Inception\enspace network \) solves this…
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#010 CNN An Example of a Neural Network

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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001-CNN-Convolutional-Neural-Networks

#001 CNN Convolutional Neural Networks

Source: Stanford CS 231n Convolutional Neural Networks What is Computer Vision?      Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do.                               Computer Vision is one of the fields of artificial intelligence that is rapidly progressing thanks to Deep…
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#001B Deep Learning, wait but why now?

Deep Learning, wait but why now? If the basic technical idea behind deep learning neural networks has been around for decades, why are they only now taking off? To answer this question, we plot a figure where on the x-axis we plot the amount of labelled data we have for a task, and on the y-axis, we plot the performance of our learning algorithm (accuracy). For example, we want to measure the accuracy of our…
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