DAPlankton: Benchmark Dataset for Multi-instrument Plankton Recognition via Fine-grained Domain Adaptation

• URL: http://arxiv.org/abs/2402.05615v1
• Date: Thu, 8 Feb 2024 12:18:39 GMT
• Title: DAPlankton: Benchmark Dataset for Multi-instrument Plankton Recognition via Fine-grained Domain Adaptation
• Authors: Daniel Batrakhanov, Tuomas Eerola, Kaisa Kraft, Lumi Haraguchi, Lasse Lensu, Sanna Suikkanen, Mar\'ia Teresa Camarena-G\'omez, Jukka Sepp\"al\"a, Heikki K\"alvi\"ainen
• Abstract summary: Different imaging instruments cause domain shift between datasets hampering the development of general plankton recognition methods. We present a new DA dataset called DAPlankton which consists of phytoplankton images obtained with different instruments.
• Score: 0.06883165603899953
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Plankton recognition provides novel possibilities to study various environmental aspects and an interesting real-world context to develop domain adaptation (DA) methods. Different imaging instruments cause domain shift between datasets hampering the development of general plankton recognition methods. A promising remedy for this is DA allowing to adapt a model trained on one instrument to other instruments. In this paper, we present a new DA dataset called DAPlankton which consists of phytoplankton images obtained with different instruments. Phytoplankton provides a challenging DA problem due to the fine-grained nature of the task and high class imbalance in real-world datasets. DAPlankton consists of two subsets. DAPlankton_LAB contains images of cultured phytoplankton providing a balanced dataset with minimal label uncertainty. DAPlankton_SEA consists of images collected from the Baltic Sea providing challenging real-world data with large intra-class variance and class imbalance. We further present a benchmark comparison of three widely used DA methods.

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