Datasets:
cifar100

Task Categories: image-classification
Languages: en
Multilinguality: monolingual
Size Categories: 10K<n<100K
Licenses: unknown
Language Creators: found
Annotations Creators: crowdsourced
Dataset Preview Go to dataset viewer
img (image)fine_label (class label)coarse_label (class label)
19 (cattle)
11 (large_omnivores_and_herbivores)
29 (dinosaur)
15 (reptiles)
0 (apple)
4 (fruit_and_vegetables)
11 (boy)
14 (people)
1 (aquarium_fish)
1 (fish)
86 (telephone)
5 (household_electrical_devices)
90 (train)
18 (vehicles_1)
28 (cup)
3 (food_containers)
23 (cloud)
10 (large_natural_outdoor_scenes)
31 (elephant)
11 (large_omnivores_and_herbivores)
39 (keyboard)
5 (household_electrical_devices)
96 (willow_tree)
17 (trees)
82 (sunflower)
2 (flowers)
17 (castle)
9 (large_man-made_outdoor_things)
71 (sea)
10 (large_natural_outdoor_scenes)
39 (keyboard)
5 (household_electrical_devices)
8 (bicycle)
18 (vehicles_1)
97 (wolf)
8 (large_carnivores)
80 (squirrel)
16 (small_mammals)
71 (sea)
10 (large_natural_outdoor_scenes)
74 (shrew)
16 (small_mammals)
59 (pine_tree)
17 (trees)
70 (rose)
2 (flowers)
87 (television)
5 (household_electrical_devices)
59 (pine_tree)
17 (trees)
84 (table)
6 (household_furniture)
64 (possum)
12 (medium_mammals)
52 (oak_tree)
17 (trees)
42 (leopard)
8 (large_carnivores)
64 (possum)
12 (medium_mammals)
8 (bicycle)
18 (vehicles_1)
17 (castle)
9 (large_man-made_outdoor_things)
47 (maple_tree)
17 (trees)
65 (rabbit)
16 (small_mammals)
21 (chimpanzee)
11 (large_omnivores_and_herbivores)
22 (clock)
5 (household_electrical_devices)
81 (streetcar)
19 (vehicles_2)
11 (boy)
14 (people)
24 (cockroach)
7 (insects)
84 (table)
6 (household_furniture)
78 (snake)
15 (reptiles)
45 (lobster)
13 (non-insect_invertebrates)
49 (mountain)
10 (large_natural_outdoor_scenes)
97 (wolf)
8 (large_carnivores)
56 (palm_tree)
17 (trees)
76 (skyscraper)
9 (large_man-made_outdoor_things)
11 (boy)
14 (people)
90 (train)
18 (vehicles_1)
89 (tractor)
19 (vehicles_2)
78 (snake)
15 (reptiles)
73 (shark)
1 (fish)
14 (butterfly)
7 (insects)
87 (television)
5 (household_electrical_devices)
9 (bottle)
3 (food_containers)
71 (sea)
10 (large_natural_outdoor_scenes)
6 (bee)
7 (insects)
47 (maple_tree)
17 (trees)
20 (chair)
6 (household_furniture)
98 (woman)
14 (people)
47 (maple_tree)
17 (trees)
36 (hamster)
16 (small_mammals)
55 (otter)
0 (aquatic_mammals)
72 (seal)
0 (aquatic_mammals)
43 (lion)
8 (large_carnivores)
51 (mushroom)
4 (fruit_and_vegetables)
35 (girl)
14 (people)
83 (sweet_pepper)
4 (fruit_and_vegetables)
33 (forest)
10 (large_natural_outdoor_scenes)
27 (crocodile)
15 (reptiles)
53 (orange)
4 (fruit_and_vegetables)
92 (tulip)
2 (flowers)
50 (mouse)
16 (small_mammals)
15 (camel)
11 (large_omnivores_and_herbivores)
89 (tractor)
19 (vehicles_2)
36 (hamster)
16 (small_mammals)
18 (caterpillar)
7 (insects)
89 (tractor)
19 (vehicles_2)
46 (man)
14 (people)
33 (forest)
10 (large_natural_outdoor_scenes)
42 (leopard)
8 (large_carnivores)
39 (keyboard)
5 (household_electrical_devices)
64 (possum)
12 (medium_mammals)
75 (skunk)
12 (medium_mammals)
38 (kangaroo)
11 (large_omnivores_and_herbivores)
23 (cloud)
10 (large_natural_outdoor_scenes)
42 (leopard)
8 (large_carnivores)
66 (raccoon)
12 (medium_mammals)
77 (snail)
13 (non-insect_invertebrates)
49 (mountain)
10 (large_natural_outdoor_scenes)
18 (caterpillar)
7 (insects)
46 (man)
14 (people)
15 (camel)
11 (large_omnivores_and_herbivores)
35 (girl)
14 (people)
69 (rocket)
19 (vehicles_2)
95 (whale)
0 (aquatic_mammals)
83 (sweet_pepper)
4 (fruit_and_vegetables)
75 (skunk)
12 (medium_mammals)
99 (worm)
13 (non-insect_invertebrates)
73 (shark)
1 (fish)
93 (turtle)
15 (reptiles)
End of preview (truncated to 100 rows)

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

name train test
cifar100 50000 10000

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.

Update on GitHub
Papers with Code

Models trained or fine-tuned on cifar100