priyadip/cifar10-resnet18-m25csa023
Image Classification • Updated
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 |
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
| ID | Class | ID | Class |
|---|---|---|---|
| 0 | airplane | 5 | dog |
| 1 | automobile | 6 | frog |
| 2 | bird | 7 | horse |
| 3 | cat | 8 | ship |
| 4 | deer | 9 | truck |
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