Visualization: Explore in Know Your Data
Description:
The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
Additional Documentation: Explore on Papers With Code
Source code:
tfds.image_classification.Cifar10Versions:
3.0.2(default): No release notes.
Download size:
162.17 MiBDataset size:
132.40 MiBAuto-cached (documentation): Yes
Splits:
| Split | Examples |
|---|---|
'test' |
10,000 |
'train' |
50,000 |
- Feature structure:
FeaturesDict({
'id': Text(shape=(), dtype=string),
'image': Image(shape=(32, 32, 3), dtype=uint8),
'label': ClassLabel(shape=(), dtype=int64, num_classes=10),
})
- Feature documentation:
| Feature | Class | Shape | Dtype | Description |
|---|---|---|---|---|
| FeaturesDict | ||||
| id | Text | string | ||
| image | Image | (32, 32, 3) | uint8 | |
| label | ClassLabel | int64 |
Supervised keys (See
as_superviseddoc):('image', 'label')Figure (tfds.show_examples):
- Examples (tfds.as_dataframe):
- Citation:
@TECHREPORT{Krizhevsky09learningmultiple,
author = {Alex Krizhevsky},
title = {Learning multiple layers of features from tiny images},
institution = {},
year = {2009}
}