caltech101

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.

• URL: http://www.vision.caltech.edu/Image_Datasets/Caltech101/
• DatasetBuilder: tfds.image.caltech.Caltech101
• Version: v1.1.0

• Versions:

  • 1.1.0 (default):
  • 3.0.0: New split API (https://tensorflow.org/datasets/splits)

• Size: 125.64 MiB

Features

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),
})

Statistics

Urls

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

Supervised keys (for as_supervised=True)

(u'image', u'label')

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},
}