  Datasets for training object recognition systems are steadily increasing in
size. This paper investigates the question of whether existing detectors will
continue to improve as data grows, or saturate in performance due to limited
model complexity and the Bayes risk associated with the feature spaces in which
they operate. We focus on the popular paradigm of discriminatively trained
templates defined on oriented gradient features. We investigate the performance
of mixtures of templates as the number of mixture components and the amount of
training data grows. Surprisingly, even with proper treatment of regularization
and "outliers", the performance of classic mixture models appears to saturate
quickly ($\sim$10 templates and $\sim$100 positive training examples per
template). This is not a limitation of the feature space as compositional
mixtures that share template parameters via parts and that can synthesize new
templates not encountered during training yield significantly better
performance. Based on our analysis, we conjecture that the greatest gains in
detection performance will continue to derive from improved representations and
learning algorithms that can make efficient use of large datasets.
