Caltech 101 is a data set of digital images created in September 2003 and compiled by Fei-Fei Li, Marco Andreetto, Marc 'Aurelio Ranzato and Pietro Perona at the California Institute of Technology.
Caltech 101 contains a total of 9,146 images, split between 101 distinct object categories (faces, watches, ants, pianos, etc.)
A large problem in comparing computer vision techniques is the fact that most groups use their own data sets.
However, a follow-up study demonstrated that tests based on uncontrolled natural images (like the Caltech 101 data set) can be seriously misleading, potentially guiding progress in the wrong direction.
The Caltech 101 data set was used to train and test several computer vision recognition and classification algorithms.
The first paper to use Caltech 101 was an incremental Bayesian approach to one-shot learning,[4] an attempt to classify an object using only a few examples, by building on prior knowledge of other classes.