  Camera Traps (or Wild Cams) enable the automatic collection of large
quantities of image data. Biologists all over the world use camera traps to
monitor biodiversity and population density of animal species. The computer
vision community has been making strides towards automating the species
classification challenge in camera traps, but as we try to expand the scope of
these models from specific regions where we have collected training data to
different areas we are faced with an interesting problem: how do you classify a
species in a new region that you may not have seen in previous training data?
  In order to tackle this problem, we have prepared a dataset and challenge
where the training data and test data are from different regions, namely The
American Southwest and the American Northwest. We use the Caltech Camera Traps
dataset, collected from the American Southwest, as training data. We add a new
dataset from the American Northwest, curated from data provided by the Idaho
Department of Fish and Game (IDFG), as our test dataset. The test data has some
class overlap with the training data, some species are found in both datasets,
but there are both species seen during training that are not seen during test
and vice versa. To help fill the gaps in the training species, we allow
competitors to utilize transfer learning from two alternate domains:
human-curated images from iNaturalist and synthetic images from Microsoft's
TrapCam-AirSim simulation environment.
