通常用于训练计算机视觉算法的图像集数据

通常用于训练计算机视觉算法的图像集数据

数据说明:

CIFAR-100数据集包含100个类别的60000张32×32彩色图像，每个类别包含600张图像。CIFAR-100中的100个类被划分为20个超类。每个图像都有一个“精细”标签(它所属的类)和一个“粗”标签(它属于的超类)。有50000个训练图像和10000个测试图像。元文件包含每个类和超类的标签名称.
1-5) beaver, dolphin, otter, seal, whale
6-10) aquarium fish, flatfish, ray, shark, trout
11-15) orchids, poppies, roses, sunflowers, tulips
16-20) bottles, bowls, cans, cups, plates
21-25) apples, mushrooms, oranges, pears, sweet peppers
26-30) clock, computer keyboard, lamp, telephone, television
31-35) bed, chair, couch, table, wardrobe
36-40) bee, beetle, butterfly, caterpillar, cockroach
41-45) bear, leopard, lion, tiger, wolf
46-50) bridge, castle, house, road, skyscraper
51-55) cloud, forest, mountain, plain, sea
56-60) camel, cattle, chimpanzee, elephant, kangaroo
61-65) fox, porcupine, possum, raccoon, skunk
66-70) crab, lobster, snail, spider, worm
71-75) baby, boy, girl, man, woman
76-80) crocodile, dinosaur, lizard, snake, turtle
81-85) hamster, mouse, rabbit, shrew, squirrel
86-90) maple, oak, palm, pine, willow
91-95) bicycle, bus, motorcycle, pickup truck, train
96-100) lawn-mower, rocket, streetcar, tank, tractor

and the list of the 20 superclasses:
1) aquatic mammals (classes 1-5)
2) fish (classes 6-10)
3) flowers (classes 11-15)
4) food containers (classes 16-20)
5) fruit and vegetables (classes 21-25)
6) household electrical devices (classes 26-30)
7) household furniture (classes 31-35)
8) insects (classes 36-40)
9) large carnivores (classes 41-45)
10) large man-made outdoor things (classes 46-50)
11) large natural outdoor scenes (classes 51-55)
12) large omnivores and herbivores (classes 56-60)
13) medium-sized mammals (classes 61-65)
14) non-insect invertebrates (classes 66-70)
15) people (classes 71-75)
16) reptiles (classes 76-80)
17) small mammals (classes 81-85)
18) trees (classes 86-90)
19) vehicles 1 (classes 91-95)
20) vehicles 2 (classes 96-100)