CIFAR-10 is a widely used image classification benchmark introduced as a labeled subset of the Tiny Images collection. It contains 60,000 32x32 color images in 10 classes (airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck), with 6,000 images per class. The standard split has 50,000 training images and 10,000 test images; the original release is organized into five training batches and one test batch (each batch contains 10,000 images). CIFAR-10 was created by Alex Krizhevsky (University of Toronto) and described in the technical report “Learning Multiple Layers of Features from Tiny Images” (2009). The images are drawn from the 80 Million Tiny Images dataset; note that the Tiny Images collection has since been the subject of dataset-level concerns and partial retraction, but CIFAR-10 remains a standard benchmark for small-image classification and transfer experiments. Common usage: training and evaluating image classification models (standard metric: classification accuracy on the 10k test images). Source / dataset homepage: https://www.cs.toronto.edu/~kriz/cifar.html. Canonical Hugging Face dataset page: https://huggingface.co/datasets/uoft-cs/cifar10.
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