The CIFAR-10 dataset is a popular benchmark in computer vision, consisting of 60,000 32x32 color images across 10 classes, with 6,000 images per class. It is widely used for evaluating image classification models, particularly convolutional neural networks (CNNs), due to its manageable size and broad applicability. The CIFAR-100 dataset expands on CIFAR-10 by featuring 60,000 images across 100 classes, providing a more complex challenge with 600 images per class. It is often used to test models on multi-class classification tasks.