CIFAR-10 is a widely used dataset in the field of machine learning and computer vision. It consists of 60,000 color images divided into 10 different classes, such as airplane, automobile, bird, and cat. Each class contains 6,000 images, and the dataset is split into 50,000 training images and 10,000 test images, making it ideal for evaluating image classification algorithms. The images in CIFAR-10 are 32x32 pixels in size, which allows for efficient processing and training of models. Researchers and developers often use this dataset to benchmark their algorithms and improve the performance of deep learning models in recognizing and classifying objects in images.