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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