Amanmeena004/cifar100-lt

Dataset Card for CIFAR-100-LT (Long Tail)

Table of Contents

• —Dataset Description
• —Dataset Summary
• —Supported Tasks and Leaderboards
• —Languages
• —Dataset Structure
• —Data Instances
• —Data Fields
• —Data Splits
• —Additional Information
• —Licensing Information
• —Citation Information
• —Contributions

Dataset Description

• —Homepage: CIFAR Datasets
• —Paper: Paper imbalanced example
• —Leaderboard: r-10 r-100

Dataset Summary

Note (March 2026): This dataset has been migrated from a Python loading script to parquet format for compatibility with datasets v4.4+. No trust_remote_code=True is needed. Available configs: r-10, r-20, r-50, r-100.

The CIFAR-100-LT imbalanced dataset is comprised of under 60,000 color images, each measuring 32x32 pixels, distributed across 100 distinct classes. The number of samples within each class decreases exponentially with factors of 10 and 100. The dataset includes 10,000 test images, with 100 images per class, and fewer than 50,000 training images. These 100 classes are further organized into 20 overarching superclasses. Each image is assigned two labels: a fine label denoting the specific class, and a coarse label representing the associated 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: fruitandvegetables

5: householdelectricaldevices

6: household_furniture

7: insects

8: large_carnivores

9: largeman-madeoutdoor_things

10: largenaturaloutdoor_scenes

11: largeomnivoresand_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

Licensing Information

Apache License 2.0

Citation Information

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

Contributions

Thanks to @gchhablani and all contributors for adding the original balanced cifar100 dataset.