  Biologists all over the world use camera traps to monitor biodiversity and
wildlife population density. The computer vision community has been making
strides towards automating the species classification challenge in camera
traps, but it has proven difficult to to apply models trained in one region to
images collected in different geographic areas. In some cases, accuracy falls
off catastrophically in new region, due to both changes in background and the
presence of previously-unseen species. We propose a pipeline that takes
advantage of a pre-trained general animal detector and a smaller set of labeled
images to train a classification model that can efficiently achieve accurate
results in a new region.
