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image
imagewidth (px)
32
32
label
class label
10 classes
9truck
0airplane
8ship
6frog
3cat
6frog
7horse
5dog
4deer
9truck
2bird
3cat
1automobile
2bird
0airplane
9truck
6frog
1automobile
7horse
3cat
5dog
2bird
3cat
6frog
5dog
9truck
0airplane
6frog
2bird
0airplane
0airplane
6frog
2bird
3cat
0airplane
2bird
3cat
5dog
1automobile
5dog
2bird
4deer
3cat
9truck
7horse
3cat
3cat
5dog
0airplane
9truck
5dog
3cat
8ship
2bird
7horse
0airplane
7horse
1automobile
8ship
4deer
7horse
6frog
5dog
6frog
6frog
2bird
1automobile
4deer
6frog
8ship
7horse
1automobile
5dog
4deer
8ship
5dog
2bird
4deer
6frog
8ship
2bird
5dog
6frog
0airplane
8ship
8ship
6frog
0airplane
8ship
4deer
9truck
6frog
4deer
8ship
1automobile
8ship
3cat
4deer
3cat
4deer
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CIFAR-10 — Subset

Stratified random subset of CIFAR-10.

Split Rows Per class
train 5,000 500
test 1,000 100
validation 500 50

Classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck Images: 32 × 32 RGB | Seed: 42

Label Map

ID Class ID Class
0 airplane 5 dog
1 automobile 6 frog
2 bird 7 horse
3 cat 8 ship
4 deer 9 truck

Usage

from datasets import load_dataset

ds = load_dataset("Chiranjeev007/CIFAR-10_Subset")
print(ds)
# DatasetDict({
#   train:      Dataset(num_rows: 5000),
#   validation: Dataset(num_rows: 500),
#   test:       Dataset(num_rows: 1000)
# })

sample = ds["train"][0]
sample["image"]   # PIL Image 32×32 RGB
sample["label"]   # int 0–9
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