Caltech 101 is a dataset of digital images created in September, 2003, compiled by Fei-Fei Li, Marco Andreetto, Marc 'Aurelio Ranzato and Pietro Perona at the California Institute of Technology. It is intended to facilitate Computer Vision research and techniques. It is most applicable to techniques interested in recognition, classification, and categorization. Caltech 101 contains a total of 9146 images, split between 101 distinct object (including faces, watches, ants, pianos, etc.) and a background category (for a total of 102 categories). Provided with the images are a set of annotations describing the outlines of each image, along with a Matlab script for viewing.