  Trail camera imagery has increasingly gained popularity amongst biologists
for conservation and ecological research. Minimal human interference required
to operate camera traps allows capturing unbiased species activities. Several
studies - based on human and wildlife interactions, migratory patterns of
various species, risk of extinction in endangered populations - are limited by
the lack of rich data and the time-consuming nature of manually annotating
trail camera imagery. We introduce a challenging wildlife camera trap
classification dataset collected from two different locations in Southwestern
Florida, consisting of 104,495 images featuring visually similar species,
varying illumination conditions, skewed class distribution, and including
samples of endangered species, i.e. Florida panthers. Experimental evaluations
with ResNet-50 architecture indicate that this image classification-based
dataset can further push the advancements in wildlife statistical modeling. We
will make the dataset publicly available.
