caltech101

• Tanım :

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.

• Additional Documentation : Explore on Papers With Code north_east

• Homepage : https://doi.org/10.22002/D1.20086

• Source code : tfds.datasets.caltech101.Builder

• Versions :

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

• Download size : 131.05 MiB

• Dataset size : 132.86 MiB

• Auto-cached ( documentation ): Yes

• Splits :

• Feature structure :

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

• Feature documentation :

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

• Figure ( tfds.show_examples ):

• Examples ( tfds.as_dataframe ):

• Alıntı :

@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},
}