CIFAR-10 stands for Canadian Institute For Advanced Research 10. It’s a widely recognized benchmark dataset consisting of 60,000 32x32 pixel color images divided into 10 classes (airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck). Each class contains 6,000 images. The dataset is commonly used to train and test image classification models, especially convolutional neural networks (CNNs). Its relatively small size compared to other datasets like ImageNet makes it suitable for quick experimentation and educational purposes while still presenting a reasonable challenge for model development. The data is split into 50,000 training images and 10,000 testing images. The simplicity of the images (32x32 resolution) and the limited number of classes make CIFAR-10 an excellent starting point for understanding fundamental concepts in computer vision and deep learning.