CIFAR-10
is an established computer-vision dataset used for object recognition. It is a
subset of the 80 million tiny images dataset and consists of 60,000 32x32 color
images containing one of 10 object classes, with 6000 images per class. There
are 50,000 training images and 10,000 test images. It was collected by Alex
Krizhevsky, Vinod Nair and Geoffrey Hinton.
The
dataset is divided into five training batches and one test batch, each with
10,000 images. The test batch contains exactly 1000 randomly selected images
from each class. The training batches contain the remaining images in random
order, but some training batches may contain more images from one class than
another. Between them, the training batches contain exactly 5000 images from
each class.
Here are
the classes in the dataset, as well as 10 random images from each: Airplane,
automobile, bird, cat, deer, dog, frog, horse, ship, truck.
The
classes are completely mutually exclusive. There is no overlap between
automobiles and trucks. “Automobiles” includes sedans, SUVs things of that
sort. “Truck” includes only big trucks Neither includes pickup trucks.
No comments:
Post a Comment