Work

Deep learning accurately identifies fjord benthic foraminifera

Year: 2025

Type: dataset

Abstract: This dataset includes images (n=3,003) and associated annotations for benthic foraminifera (22,138 individuals) from four Skagerrak fjords (Swedish west coast). Annotations were collected using the Roboflow platform (https://roboflow.com/) and are provided in both YOLOv7-PyTorch and COCO format. These data were used to train a YOLOv7 deep learning segmentation model which is able to identify 29 species of fjord benthic foraminifera with 90.3% mAP (mean average precision).

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.17608880

Authors: Plavetić, Marko, Hsiang, Allison, Josefson, Mats, Hulthe, Gustaf, Polovodova Asteman, Irina

Institutions: University of Gothenburg, Stockholm University, AstraZeneca (Sweden)

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Life below water

Open Access status: green

OpenAlex ID: W7107945641

Cites:

Cited by: 1

Related to: 10