About the CIFAR100 dataset
The CIFAR-100 dataset is a large image classification dataset that consists of 60,000 32x32 color training images and 10,000 test images, each labeled with one of 100 fine-grained classes that are grouped into 20 coarse-grained classes. The classes include objects such as animals, vehicles, and scenes. The dataset was created by researchers at the Canadian Institute for Advanced Research (CIFAR) and is widely used in computer vision research as a benchmark for image classification tasks. The CIFAR-100 dataset is often used as a baseline for evaluating the performance of new algorithms and architectures in computer vision. The data is preprocessed, with the images being reshaped from their original form and normalized to have zero mean and unit variance. This makes the data suitable for use with machine learning algorithms.