February 2018
Intermediate to advanced
218 pages
5h 31m
English
Until now, in this book, we have been mostly using convolutional neural networks (CNNs) for classification. Classification classifies the whole image into one of the classes with respect to the entity having the maximum probability of detection in the image. But what if there is not one, but multiple entities of interest and we want to have the image associated with all of them? One way to do this is to use tags instead of classes, where these tags are all classes of the penultimate Softmax classification layer with probability above a given threshold. However, the probability of detection here varies widely by size and placement of entity, and from the following image, we can actually say, ...
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