  Foraminifera are single-celled marine organisms, which may have a planktic or
benthic lifestyle. During their life cycle they construct shells consisting of
one or more chambers, and these shells remain as fossils in marine sediments.
Classifying and counting these fossils have become an important tool in e.g.
oceanography and climatology. Currently the process of identifying and counting
microfossils is performed manually using a microscope and is very time
consuming. Developing methods to automate this process is therefore considered
important across a range of research fields. The first steps towards developing
a deep learning model that can detect and classify microscopic foraminifera are
proposed. The proposed model is based on a VGG16 model that has been pretrained
on the ImageNet dataset, and adapted to the foraminifera task using transfer
learning. Additionally, a novel image dataset consisting of microscopic
foraminifera and sediments from the Barents Sea region is introduced.
