- Description:
Omniglot data set for one-shot learning. This dataset contains 1623 different handwritten characters from 50 different alphabets.
Additional Documentation: Explore on Papers With Code
Homepage: https://github.com/brendenlake/omniglot/
Source code:
tfds.image_classification.OmniglotVersions:
3.0.0(default): New split API (https://tensorflow.org/datasets/splits)
Download size:
17.95 MiBDataset size:
12.29 MiBAuto-cached (documentation): Yes
Splits:
| Split | Examples |
|---|---|
'small1' |
2,720 |
'small2' |
3,120 |
'test' |
13,180 |
'train' |
19,280 |
- Feature structure:
FeaturesDict({
'alphabet': ClassLabel(shape=(), dtype=int64, num_classes=50),
'alphabet_char_id': int64,
'image': Image(shape=(105, 105, 3), dtype=uint8),
'label': ClassLabel(shape=(), dtype=int64, num_classes=1623),
})
- Feature documentation:
| Feature | Class | Shape | Dtype | Description |
|---|---|---|---|---|
| FeaturesDict | ||||
| alphabet | ClassLabel | int64 | ||
| alphabet_char_id | Tensor | int64 | ||
| image | Image | (105, 105, 3) | uint8 | |
| label | ClassLabel | int64 |
Supervised keys (See
as_superviseddoc):('image', 'label')Figure (tfds.show_examples):
- Examples (tfds.as_dataframe):
- Citation:
@article{lake2015human,
title={Human-level concept learning through probabilistic program induction},
author={Lake, Brenden M and Salakhutdinov, Ruslan and Tenenbaum, Joshua B},
journal={Science},
volume={350},
number={6266},
pages={1332--1338},
year={2015},
publisher={American Association for the Advancement of Science}
}