The FathomNet2023 Competition Dataset
Eric Orenstein Kevin Barnard Lonny Lundsten Geneviève Patterson Benjamin Woodward Kakani Katija
Ocean scientists have been collecting visual data to study marine organisms
for decades. These images and videos are extremely valuable both for basic
science and environmental monitoring tasks. There are tools for automatically
processing these data, but none that are capable of handling the extreme
variability in sample populations, image quality, and habitat characteristics
that are common in visual sampling of the ocean. Such distribution shifts can
occur over very short physical distances and in narrow time windows. Creating
models that are able to recognize when an image or video sequence contains a
new organism, an unusual collection of animals, or is otherwise out-of-sample
is critical to fully leverage visual data in the ocean. The FathomNet2023
competition dataset presents a realistic scenario where the set of animals in
the target data differs from the training data. The challenge is both to
identify the organisms in a target image and assess whether it is
out-of-sample.