Introduced by Krizhevsky et al. in Learning multiple layers of features from tiny images

The CIFAR-100 dataset (Canadian Institute for Advanced Research, 100 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. There are 600 images per class. Each image comes with a "fine" label (the class to which it belongs) and a "coarse" label (the superclass to which it belongs). There are 500 training images and 100 testing images per class.

The criteria for deciding whether an image belongs to a class were as follows:

  • The class name should be high on the list of likely answers to the question “What is in this picture?”
  • The image should be photo-realistic. Labelers were instructed to reject line drawings.
  • The image should contain only one prominent instance of the object to which the class refers.
  • The object may be partially occluded or seen from an unusual viewpoint as long as its identity is still clear to the labeler.


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