caltech101

• Description:

Caltech-101 consists of pictures of objects belonging to 101 classes, plus one background clutter class. Each image is labelled with a single object. Each class contains roughly 40 to 800 images, totalling around 9k images. Images are of variable sizes, with typical edge lengths of 200-300 pixels. This version contains image-level labels only. The original dataset also contains bounding boxes.

• Homepage: http://www.vision.caltech.edu/Image_Datasets/Caltech101/

• Source code: tfds.image_classification.Caltech101

• Versions:

  • 3.0.0: New split API (https://tensorflow.org/datasets/splits)
  • 3.0.1 (default): Website URL update

• Download size: 125.64 MiB

• Dataset size: 132.86 MiB

• Auto-cached (documentation): Yes

• Splits:

Split	Examples
'test'	6,084
'train'	3,060

• Feature structure:

FeaturesDict({
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'image/file_name': Text(shape=(), dtype=tf.string),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=102),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
image	Image	(None, None, 3)	tf.uint8
image/file_name	Text		tf.string
label	ClassLabel		tf.int64

• Supervised keys (See as_supervised doc): ('image', 'label')

• Figure (tfds.show_examples):

• Examples (tfds.as_dataframe):

• Citation:

@article{FeiFei2004LearningGV,
  title={Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories},
  author={Li Fei-Fei and Rob Fergus and Pietro Perona},
  journal={Computer Vision and Pattern Recognition Workshop},
  year={2004},
}