Caltech 101
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. It is intended to facilitate Computer Vision research and techniques and is most applicable to techniques involving image recognition classification and categorization. Caltech 101 contains a total of 9,146 images, split between 101 distinct object categories (faces, watches, ants, pianos, etc.) and a background category. Provided with the images are a set of annotations describing the outlines of each image, along with a Matlab script  for viewing. Purpose. Most Computer Vision and Machine Learning algorithms function by training on example inputs. They require a large and varied set of training data to work effectively. For example, the real-time face detection method used by Paul Viola and Michael J. Jones was trained on 4,916 hand-labeled faces. Cropping, re-sizing and hand-marking points of interest is tedious and time-consuming. Historically, most data sets used in computer vision research have been tailored to the specific needs of the project being worked on. A large problem in comparing computer vision techniques is the fact that  most groups use their own data sets. Each set may have different properties that make reported results from different methods harder to compare directly. For example, differences in image size, image quality, relative location of objects within the images and level of occlusion and clutter present can lead to varying results. The Caltech 101 data set aims at alleviating many of these common problems. However, a recent study demonstrates that tests based on uncontrolled natural images (like the Caltech 101 data set) can be seriously misleading, potentially guiding progress in the wrong direction. Data set. Images. The Caltech 101 data  set consists of a total of 9,146 images, split between 101 different object categories, as well as an additional background/clutter category. Each object category contains between 40 and 800 images. Common and popular categories such as faces tend to have a larger number of images than others. Each image is about 300x200 pixels. Images of oriented objects such as airplanes and motorcycles were mirrored to be left to right aligned and vertically oriented structures such as buildings were rotated to be off axis. Annotations. A set of annotations is provided for each image. Each set of annotations contains two  pieces of information: the general bounding box in which the object is located and a detailed human-specified outline enclosing the object. A Matlab script is provided with the annotations. It loads an image and its corresponding annotation file and displays them as a Matlab figure. Uses. 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, an attempt to classify an object using only a few examples, by building on prior knowledge of other classes . The Caltech 101 images, along with the annotations, were used for another one shot learning paper at Caltech. Other Computer Vision papers that report using the Caltech 101 data set include: Analysis and comparison. Advantages. Caltech 101 has several advantages over other similar data sets: Weaknesses. Weaknesses to the Caltech 101 data set may be conscious trade-offs, but others are limitations of the data set. Papers that rely solely on Caltech 101 are frequently rejected. Weaknesses include: