  Advances in underwater imaging enable the collection of extensive seafloor
image datasets that are necessary for monitoring important benthic ecosystems.
The ability to collect seafloor imagery has outpaced our capacity to analyze
it, hindering expedient mobilization of this crucial environmental information.
Recent machine learning approaches provide opportunities to increase the
efficiency with which seafloor image datasets are analyzed, yet large and
consistent datasets necessary to support development of such approaches are
scarce. Here we present BenthicNet: a global compilation of seafloor imagery
designed to support the training and evaluation of large-scale image
recognition models. An initial set of over 11.4 million images was collected
and curated to represent a diversity of seafloor environments using a
representative subset of 1.3 million images. These are accompanied by 2.6
million annotations translated to the CATAMI scheme, which span 190,000 of the
images. A large deep learning model was trained on this compilation and
preliminary results suggest it has utility for automating large and small-scale
image analysis tasks. The compilation and model are made openly available for
use by the scientific community at https://doi.org/10.20383/103.0614.
