CIFAR-10 & CIFAR-100

CIFAR-10 & CIFAR-100:

These two datasets are labeled images from a parent dataset called Tiny Images Dataset (which is available on http://horatio.cs.nyu.edu/mit/tiny/data/index.html).

CIFAR-10:

  • Number of images in the dataset: 60,000 (50,000 images for training divided into 5 batches and 10,000 images for test in one batch)

  • Image size: 32×32

  • Number of classes: 10 (airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck)

3 different versions are available for this dataset each suitable for either Python, Matlab or C programming, and they can be downloaded from https://www.cs.toronto.edu/~kriz/cifar.html.

If you use this dataset, make sure to cite the following tech report.

Alex Krizhevsky, Learning multiple layers of features from tiny images, 2009.

CIFAR-100:

  • Number of images in the dataset: 60,000 (50,000 images for training while 500 images belong to each class and 10,000 images for test while 100 images belong to each class for test)

  • Image size: 32×32

  • Number of classes: 100 , provided in detail in bellow (copied from http://www.cs.toronto.edu/~kriz/cifar.html)

The 100 classes belong to 20 Superclasses that determines the “coarse” label and the “fine” label refers to the class that image belongs to.

Superclass and Classes:

aquatic mammals: beaver, dolphin, otter, seal, whale

fish: aquarium fish, flatfish, ray, shark, trout

flowers: orchids, poppies, roses, sunflowers, tulips

food containers: bottles, bowls, cans, cups, plates

fruit and vegetables: apples, mushrooms, oranges, pears, sweet peppers

household electrical devices: clock, computer keyboard, lamp, telephone, television

household furniture: bed, chair, couch, table, wardrobe

insects: bee, beetle, butterfly, caterpillar, cockroach

large carnivores: bear, leopard, lion, tiger, wolf

large man-made outdoor things: bridge, castle, house, road, skyscraper

large natural outdoor scenes: cloud, forest, mountain, plain, sea

large omnivores and herbivores: camel, cattle, chimpanzee, elephant, kangaroo

medium-sized mammals: fox, porcupine, possum, raccoon, skunk

non-insect invertebrates: crab, lobster, snail, spider, worm

people: baby, boy, girl, man, woman

reptiles: crocodile, dinosaur, lizard, snake, turtle

small mammals: hamster, mouse, rabbit, shrew, squirrel

trees: maple, oak, palm, pine, willow

vehicles 1: bicycle, bus, motorcycle, pickup truck, train

vehicles 2: lawn-mower, rocket, streetcar, tank, tractor

3 different versions are available for this dataset each suitable for either Python, Matlab or C programming, and they can be downloaded from https://www.cs.toronto.edu/~kriz/cifar.html.

If you use this dataset, make sure to cite the following tech report.

Alex Krizhevsky, Learning multiple layers of features from tiny images, 2009.

Keywords: Vision, Image, Classification, Natural Images