  We have created a large diverse set of cars from overhead images, which are
useful for training a deep learner to binary classify, detect and count them.
The dataset and all related material will be made publically available. The set
contains contextual matter to aid in identification of difficult targets. We
demonstrate classification and detection on this dataset using a neural network
we call ResCeption. This network combines residual learning with
Inception-style layers and is used to count cars in one look. This is a new way
to count objects rather than by localization or density estimation. It is
fairly accurate, fast and easy to implement. Additionally, the counting method
is not car or scene specific. It would be easy to train this method to count
other kinds of objects and counting over new scenes requires no extra set up or
assumptions about object locations.
