July 25, 2025Open Access

Fine-Grained Taxonomy with Vision Models: A Benchmark on Long-Tailed and Domain-Adaptive Classification

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MAIN FINDING: A Vision and Language Transformer achieves 97% genus-level and 94% species-level accuracy in beetle classification.
KEY EVIDENCE: Experiment shows efficient subsampling allows reduction of training data while maintaining model performance.
APPROACH: Utilized 12 vision models evaluated on four long-tailed datasets, including lab and field-collected images.
SIGNIFICANCE: Findings advance ecological computer vision by addressing sample efficiency and domain adaptation challenges.

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Benchmark evaluates classification accuracy in beetles across long-tailed datasets, suggesting improvements in domain adaptation.

Rayeed et al. (2025) studied this question.

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