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imagewidth (px) 64
64
| label
class label 10
classes |
|---|---|
8ship
|
|
1automobile
|
|
2bird
|
|
1automobile
|
|
3cat
|
|
0airplane
|
|
9truck
|
|
6frog
|
|
9truck
|
|
2bird
|
|
7horse
|
|
5dog
|
|
9truck
|
|
6frog
|
|
5dog
|
|
3cat
|
|
6frog
|
|
9truck
|
|
6frog
|
|
2bird
|
|
0airplane
|
|
7horse
|
|
2bird
|
|
9truck
|
|
3cat
|
|
4deer
|
|
2bird
|
|
1automobile
|
|
6frog
|
|
0airplane
|
|
7horse
|
|
1automobile
|
|
2bird
|
|
1automobile
|
|
1automobile
|
|
9truck
|
|
0airplane
|
|
6frog
|
|
1automobile
|
|
7horse
|
|
7horse
|
|
7horse
|
|
1automobile
|
|
2bird
|
|
2bird
|
|
2bird
|
|
3cat
|
|
0airplane
|
|
2bird
|
|
3cat
|
|
9truck
|
|
0airplane
|
|
3cat
|
|
4deer
|
|
0airplane
|
|
4deer
|
|
7horse
|
|
0airplane
|
|
7horse
|
|
3cat
|
|
7horse
|
|
6frog
|
|
4deer
|
|
9truck
|
|
3cat
|
|
6frog
|
|
6frog
|
|
3cat
|
|
5dog
|
|
7horse
|
|
3cat
|
|
2bird
|
|
1automobile
|
|
9truck
|
|
8ship
|
|
1automobile
|
|
4deer
|
|
5dog
|
|
3cat
|
|
7horse
|
|
0airplane
|
|
2bird
|
|
1automobile
|
|
5dog
|
|
8ship
|
|
3cat
|
|
1automobile
|
|
4deer
|
|
9truck
|
|
2bird
|
|
3cat
|
|
9truck
|
|
9truck
|
|
1automobile
|
|
5dog
|
|
6frog
|
|
3cat
|
|
4deer
|
|
9truck
|
|
9truck
|
End of preview. Expand
in Data Studio
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.
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