|
|
--- |
|
|
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: 1560708615.0 |
|
|
num_examples: 190000 |
|
|
- name: test |
|
|
num_bytes: 82238790.0 |
|
|
num_examples: 10000 |
|
|
download_size: 1642628895 |
|
|
dataset_size: 1642947405.0 |
|
|
--- |
|
|
|
|
|
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. |
