smallnorb

• 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).

• Additional Documentation: Explore on Papers With Code

• Homepage: https://cs.nyu.edu/~ylclab/data/norb-v1.0-small/

• Source code: tfds.datasets.smallnorb.Builder

• 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

• Feature structure:

FeaturesDict({
    'image': Image(shape=(96, 96, 1), dtype=uint8),
    'image2': Image(shape=(96, 96, 1), dtype=uint8),
    'instance': ClassLabel(shape=(), dtype=int64, num_classes=10),
    'label_azimuth': ClassLabel(shape=(), dtype=int64, num_classes=18),
    'label_category': ClassLabel(shape=(), dtype=int64, num_classes=5),
    'label_elevation': ClassLabel(shape=(), dtype=int64, num_classes=9),
    'label_lighting': ClassLabel(shape=(), dtype=int64, num_classes=6),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
image	Image	(96, 96, 1)	uint8
image2	Image	(96, 96, 1)	uint8
instance	ClassLabel		int64
label_azimuth	ClassLabel		int64
label_category	ClassLabel		int64
label_elevation	ClassLabel		int64
label_lighting	ClassLabel		int64

• 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}
}