- Description:
This database is intended for experiments in 3D object recognition from shape.
It contains images of 50 toys belonging to 5 generic categories: four-legged
animals, human figures, airplanes, trucks, and cars. The objects were imaged by
two cameras under 6 lighting conditions, 9 elevations (30 to 70 degrees every 5
degrees), and 18 azimuths (0 to 340 every 20 degrees).
The training set is composed of 5 instances of each category (instances 4, 6, 7, 8 and 9), and the test set of the remaining 5 instances (instances 0, 1, 2, 3, and 5).
Source code:
tfds.image_classification.Smallnorb
Versions:
2.0.0
(default): New split API (https://tensorflow.org/datasets/splits)2.1.0
: No release notes.
Download size:
250.60 MiB
Dataset size:
Unknown size
Auto-cached (documentation): Unknown
Splits:
Split | Examples |
---|---|
'test' |
24,300 |
'train' |
24,300 |
- Features:
FeaturesDict({
'image': Image(shape=(96, 96, 1), dtype=tf.uint8),
'image2': Image(shape=(96, 96, 1), dtype=tf.uint8),
'instance': ClassLabel(shape=(), dtype=tf.int64, num_classes=10),
'label_azimuth': ClassLabel(shape=(), dtype=tf.int64, num_classes=18),
'label_category': ClassLabel(shape=(), dtype=tf.int64, num_classes=5),
'label_elevation': ClassLabel(shape=(), dtype=tf.int64, num_classes=9),
'label_lighting': ClassLabel(shape=(), dtype=tf.int64, num_classes=6),
})
Supervised keys (See
as_supervised
doc):('image', 'label_category')
Figure (tfds.show_examples): Not supported.
Examples (tfds.as_dataframe):
- Citation:
\
@article{LeCun2004LearningMF,
title={Learning methods for generic object recognition with invariance to pose and lighting},
author={Yann LeCun and Fu Jie Huang and L{\'e}on Bottou},
journal={Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
year={2004},
volume={2},
pages={II-104 Vol.2}
}