CIFAR-100

Description

The CIFAR-100 Dataset is an image classification dataset for the field of machine vision. It has 20 large classes with a total of 100 sub-classes, each of which contains 600 images (500 training images and 100 test images) and each image has a small label and a large label.
The dataset was published in 2009 by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton of the Department of Computer Science at the University of Toronto, and is the subject of a paper called Learning Multiple Layers of Features from Tiny Images.

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