![]() This vector represents the content of the image as a point in C-dimensional category space, where C is the number of categories (several thousand). We can interpret the output of the image classifier as a vector j c of the per-category scores. ![]() We could collate a large dictionary of synonyms and near-synonyms and hierarchical relationships between words, but this quickly becomes unwieldy, especially if we support multiple languages. ![]() Sure, if a user searches for beach we could return the images with the highest scores for that category, but what if they instead search for shore? What if instead of apple they search for fruit or granny smith? Image classification lets us automatically understand what’s in an image, but by itself this isn’t enough to enable search. Take a look at how well image classification works today: Since then, with model architecture improvements, better training methods, large datasets like Open Images or ImageNet, and easy-to-use libraries like TensorFlow and PyTorch, researchers have built image classifiers that can recognize thousands of categories. The past decade has seen tremendous progress in image classification using convolutional neural networks, beginning with Krizhevsky et al ’s breakthrough result on the ImageNet challenge in 2012.
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