  Machine learning and neural networks are now ubiquitous in sonar perception,
but it lags behind the computer vision field due to the lack of data and
pre-trained models specifically for sonar images. In this paper we present the
Marine Debris Turntable dataset and produce pre-trained neural networks trained
on this dataset, meant to fill the gap of missing pre-trained models for sonar
images. We train Resnet 20, MobileNets, DenseNet121, SqueezeNet, MiniXception,
and an Autoencoder, over several input image sizes, from 32 x 32 to 96 x 96, on
the Marine Debris turntable dataset. We evaluate these models using transfer
learning for low-shot classification in the Marine Debris Watertank and another
dataset captured using a Gemini 720i sonar. Our results show that in both
datasets the pre-trained models produce good features that allow good
classification accuracy with low samples (10-30 samples per class). The Gemini
dataset validates that the features transfer to other kinds of sonar sensors.
We expect that the community benefits from the public release of our
pre-trained models and the turntable dataset.
