CIFAR-10

Consists of 60,000 32x32 color images in 10 different classes, with 6,000 images per class, commonly used for training machine learning and computer vision algorithms.

About Dataset

CIFAR-10 and CIFAR-100 are small-scale image datasets consisting of 60,000 low-resolution color images labeled into 10 and 100 classes respectively. Developed by researchers at the University of Toronto, the datasets were designed as standardized benchmarks for image classification research. Images depict everyday objects such as animals, vehicles, and household items, with balanced class distributions.

Purpose of Dataset

Cifar Datasets Are Extensively Used For Benchmarking Image Classification Algorithms And Studying Model Behavior In Controlled Settings. They Are Particularly Useful For Rapid Experimentation, Architecture Comparison, And Educational Purposes. Researchers Use Cifar To Test Optimization Methods, Regularization Techniques, And Robustness Of Vision Models Before Scaling To Larger Datasets.