- Description:
Pre-trained embeddings for approximate nearest neighbor search using the cosine distance. This dataset consists of two splits:
- 'database': consists of 9,990,000 data points, each has features: 'embedding' (96 floats), 'index' (int64), 'neighbors' (empty list).
- 'test': consists of 10,000 data points, each has features: 'embedding' (96 floats), 'index' (int64), 'neighbors' (list of 'index' and 'distance' of the nearest neighbors in the database.)
Homepage: http://sites.skoltech.ru/compvision/noimi/
Source code:
tfds.nearest_neighbors.deep1b.Deep1b
Versions:
1.0.0
(default): Initial release.
Download size:
3.58 GiB
Dataset size:
4.46 GiB
Auto-cached (documentation): No
Splits:
Split | Examples |
---|---|
'database' |
9,990,000 |
'test' |
10,000 |
- Feature structure:
FeaturesDict({
'embedding': Tensor(shape=(96,), 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 | (96,) | 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:
@inproceedings{babenko2016efficient,
title={Efficient indexing of billion-scale datasets of deep descriptors},
author={Babenko, Artem and Lempitsky, Victor},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={2055--2063},
year={2016}
}