Convolutional neural networks (CNNs) are commonly used for image classification and recognition tasks. CNNs use convolutional layers that apply learnable filters to detect patterns in input images. The filters are convolved across the width and height of the input to produce an activation map. Padding is added to images processed by CNNs to allow filters to fully cover edge pixels and produce accurate analyses. The dimensions of convolved outputs depend on the input and filter sizes, as well as any padding used.