CIFAR-100 is a widely used image classification dataset of 60,000 color images (32x32) in 100 classes, with 600 images per class. There are 50,000 training images and 10,000 test images; each class has 500 training and 100 test images. The 100 "fine" classes are grouped into 20 "coarse" superclasses, and each image has both a fine label (one of 100 classes) and a coarse label (one of 20 superclasses). CIFAR-100 was created by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton as labeled subsets of the 80 Million Tiny Images dataset and is commonly used for benchmarking image-classification models and transfer learning. Canonical dataset page / download and documentation: https://www.cs.toronto.edu/~kriz/cifar.html. A common Hugging Face hosted variant is uoft-cs/cifar100 (https://huggingface.co/datasets/uoft-cs/cifar100). Citation (original dataset page / tech report): Alex Krizhevsky, "Learning multiple layers of features from tiny images" (2009, dataset homepage: https://www.cs.toronto.edu/~kriz/cifar.html).
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