  Camera traps are a valuable tool for studying biodiversity, but research
using this data is limited by the speed of human annotation. With the vast
amounts of data now available it is imperative that we develop automatic
solutions for annotating camera trap data in order to allow this research to
scale. A promising approach is based on deep networks trained on
human-annotated images. We provide a challenge dataset to explore whether such
solutions generalize to novel locations, since systems that are trained once
and may be deployed to operate automatically in new locations would be most
useful.
