metadata
dataset_info:
features:
- name: img
dtype: image
- name: label
dtype:
class_label:
names:
'0': airplane
'1': automobile
'2': bird
'3': cat
'4': deer
'5': dog
'6': frog
'7': horse
'8': ship
'9': truck
splits:
- name: train
num_bytes: 1560708615
num_examples: 190000
- name: test
num_bytes: 82238790
num_examples: 10000
download_size: 1642628895
dataset_size: 1642947405
CIFARNet contains 200K images sampled from ImageNet-21K (Winter 2019 release), resized to 64x64, using coarse-grained labels that roughly match those of CIFAR-10. The exact ImageNet synsets used were:
{
"n02691156": 0, # airplane
"n02958343": 1, # automobile
"n01503061": 2, # bird
"n02121620": 3, # cat
"n02430045": 4, # deer
"n02083346": 5, # dog
"n01639765": 6, # frog
"n02374451": 7, # horse
"n04194289": 8, # ship
"n04490091": 9, # truck
}
The classes are balanced, and the dataset is pre-split into a training set of 190K images and a validation set of 10K images.