- Description:
Pre-trained embeddings for approximate nearest neighbor search using the Euclidean distance. This dataset consists of two splits:
- 'database': consists of 1,000,000 data points, each has features: 'embedding' (128 floats), 'index' (int64), 'neighbors' (empty list).
- 'test': consists of 10,000 data points, each has features: 'embedding' (128 floats), 'index' (int64), 'neighbors' (list of 'index' and 'distance' of the nearest neighbors in the database.)
Homepage: http://corpus-texmex.irisa.fr/
Source code:
tfds.datasets.sift1m.Builder
Versions:
1.0.0
(default): Initial release.
Download size:
500.80 MiB
Dataset size:
589.49 MiB
Auto-cached (documentation): No
Splits:
Split | Examples |
---|---|
'database' |
1,000,000 |
'test' |
10,000 |
- Feature structure:
FeaturesDict({
'embedding': Tensor(shape=(128,), dtype=float32),
'index': Scalar(shape=(), dtype=int64, description=Index within the split.),
'neighbors': Sequence({
'distance': Scalar(shape=(), dtype=float32, description=Neighbor distance.),
'index': Scalar(shape=(), dtype=int64, description=Neighbor index.),
}),
})
- Feature documentation:
Feature | Class | Shape | Dtype | Description |
---|---|---|---|---|
FeaturesDict | ||||
embedding | Tensor | (128,) | float32 | |
index | Scalar | int64 | Index within the split. | |
neighbors | Sequence | The computed neighbors, which is only available for the test split. | ||
neighbors/distance | Scalar | float32 | Neighbor distance. | |
neighbors/index | Scalar | int64 | Neighbor index. |
Supervised keys (See
as_supervised
doc):None
Figure (tfds.show_examples): Not supported.
Examples (tfds.as_dataframe):
- Citation:
@article{jegou2010product,
title={Product quantization for nearest neighbor search},
author={Jegou, Herve and Douze, Matthijs and Schmid, Cordelia},
journal={IEEE transactions on pattern analysis and machine intelligence},
volume={33},
number={1},
pages={117--128},
year={2010},
publisher={IEEE}
}