Articles | Volume 18, issue 2
https://doi.org/10.5194/essd-18-945-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-18-945-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
05 Feb 2026
Review article |
| 05 Feb 2026
| 05 Feb 2026
Benchmark of plankton images classification: emphasizing features extraction over classifier complexity
Thelma Panaïotis
CORRESPONDING AUTHOR
National Oceanography Centre, European Way, Southampton, SO14 3ZH, UK
Laboratoire d'Océanographie de Villefranche, Sorbonne Université, 181 Chemin du Lazaret, 06230 Villefranche-sur-Mer, France
Emma Amblard
Laboratoire d'Océanographie de Villefranche, Sorbonne Université, 181 Chemin du Lazaret, 06230 Villefranche-sur-Mer, France
Fotonower, 48 Rue René Clair 75018 Paris, France
Guillaume Boniface-Chang
Google Research, 6 Pancras Sq, London N1C 4AG, UK
Gabriel Dulac-Arnold
Google Research, 8 Rue de Londres, 75009 Paris, France
Benjamin Woodward
CVision AI, 81 West St, Medford, MA 02155, USA
Jean-Olivier Irisson
Laboratoire d'Océanographie de Villefranche, Sorbonne Université, 181 Chemin du Lazaret, 06230 Villefranche-sur-Mer, France
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Short summary
To address the lack of performance benchmark in plankton image classification, we evaluated machine learning methods on six large and realistic datasets. Testing both traditional and more recent convolutional neural networks (deep learning), we find that relatively small deep networks performed best, particularly for uncommon classes, because they extract richer features. Our results indicate that such compact models are sufficient for classifying small grayscale plankton images.
To address the lack of performance benchmark in plankton image classification, we evaluated...
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