Dataset Card for CIFAR-100

Dataset Summary

The CIFAR-100 dataset consists of 60000 32x32 colour images in 100 classes, with 600 images per class. There are 500 training images and 100 testing images per class. There are 50000 training images and 10000 test images. The 100 classes are grouped into 20 superclasses. There are two labels per image - fine label (actual class) and coarse label (superclass).

Supported Tasks and Leaderboards

• image-classification: The goal of this task is to classify a given image into one of 100 classes. The leaderboard is available here.

Languages

English

Dataset Structure

Data Instances

A sample from the training set is provided below:

{
  'img': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32 at 0x2767F58E080>, 'fine_label': 19,
  'coarse_label': 11
}

Data Fields

• img: A PIL.Image.Image object containing the 32x32 image. Note that when accessing the image column: dataset[0]["image"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"] should always be preferred over dataset["image"][0]

• fine_label: an int classification label with the following mapping:

  0: apple

  1: aquarium_fish

  2: baby

  3: bear

  4: beaver

  5: bed

  6: bee

  7: beetle

  8: bicycle

  9: bottle

  10: bowl

  11: boy

  12: bridge

  13: bus

  14: butterfly

  15: camel

  16: can

  17: castle

  18: caterpillar

  19: cattle

  20: chair

  21: chimpanzee

  22: clock

  23: cloud

  24: cockroach

  25: couch

  26: cra

  27: crocodile

  28: cup

  29: dinosaur

  30: dolphin

  31: elephant

  32: flatfish

  33: forest

  34: fox

  35: girl

  36: hamster

  37: house

  38: kangaroo

  39: keyboard

  40: lamp

  41: lawn_mower

  42: leopard

  43: lion

  44: lizard

  45: lobster

  46: man

  47: maple_tree

  48: motorcycle

  49: mountain

  50: mouse

  51: mushroom

  52: oak_tree

  53: orange

  54: orchid

  55: otter

  56: palm_tree

  57: pear

  58: pickup_truck

  59: pine_tree

  60: plain

  61: plate

  62: poppy

  63: porcupine

  64: possum

  65: rabbit

  66: raccoon

  67: ray

  68: road

  69: rocket

  70: rose

  71: sea

  72: seal

  73: shark

  74: shrew

  75: skunk

  76: skyscraper

  77: snail

  78: snake

  79: spider

  80: squirrel

  81: streetcar

  82: sunflower

  83: sweet_pepper

  84: table

  85: tank

  86: telephone

  87: television

  88: tiger

  89: tractor

  90: train

  91: trout

  92: tulip

  93: turtle

  94: wardrobe

  95: whale

  96: willow_tree

  97: wolf

  98: woman

  99: worm

• coarse_label: an int coarse classification label with following mapping:

  0: aquatic_mammals

  1: fish

  2: flowers

  3: food_containers

  4: fruit_and_vegetables

  5: household_electrical_devices

  6: household_furniture

  7: insects

  8: large_carnivores

  9: large_man-made_outdoor_things

  10: large_natural_outdoor_scenes

  11: large_omnivores_and_herbivores

  12: medium_mammals

  13: non-insect_invertebrates

  14: people

  15: reptiles

  16: small_mammals

  17: trees

  18: vehicles_1

  19: vehicles_2

Data Splits

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

@TECHREPORT{Krizhevsky09learningmultiple,
    author = {Alex Krizhevsky},
    title = {Learning multiple layers of features from tiny images},
    institution = {},
    year = {2009}
}

Contributions

Thanks to @gchhablani for adding this dataset.