- Description:
Stanford Online Products Dataset
Additional Documentation: Explore on Papers With Code
Source code:
tfds.datasets.stanford_online_products.BuilderVersions:
1.0.0(default): No release notes.
Download size:
2.87 GiBDataset size:
2.89 GiBAuto-cached (documentation): No
Splits:
| Split | Examples |
|---|---|
'test' |
60,502 |
'train' |
59,551 |
- Feature structure:
FeaturesDict({
'class_id': ClassLabel(shape=(), dtype=int64, num_classes=22634),
'image': Image(shape=(None, None, 3), dtype=uint8),
'super_class_id': ClassLabel(shape=(), dtype=int64, num_classes=12),
'super_class_id/num': ClassLabel(shape=(), dtype=int64, num_classes=12),
})
- Feature documentation:
| Feature | Class | Shape | Dtype | Description |
|---|---|---|---|---|
| FeaturesDict | ||||
| class_id | ClassLabel | int64 | ||
| image | Image | (None, None, 3) | uint8 | |
| super_class_id | ClassLabel | int64 | ||
| super_class_id/num | ClassLabel | int64 |
Supervised keys (See
as_superviseddoc):NoneFigure (tfds.show_examples):
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{song2016deep,
author = {Song, Hyun Oh and Xiang, Yu and Jegelka, Stefanie and Savarese, Silvio},
title = {Deep Metric Learning via Lifted Structured Feature Embedding},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2016}
}