Fine-Grained Beetle Taxonomy with Vision Models: A Benchmark on Long-Tailed and Domain-Adaptive Classification
Bibliographic record
Abstract
Ground beetles are a highly sensitive and speciose biological indicator, critical for biodiversity monitoring, yet their taxonomic classification remains underutilized due to the manual effort required for species differentiation based on subtle morphological variations. In this paper, we present a benchmark for fine-grained taxonomic classification, evaluating 12 vision models, across four diverse, long-tailed datasets spanning over 230 genera and 1769 species. These datasets include both controlled laboratory images and challenging field-collected (in-situ) photographs. We investigate two key real-world challenges: sample efficiency and domain adaptation. Our results show that 1) a Vision and Language Transformer with an MLP head achieves best performance, with 97% genus-level and 94% species-level accuracy; 2) efficient subsampling allows train data to be cut in half with minimal performance degradation; 3) model performance significantly drops in domain shift from lab to in-situ settings, highlighting a critical domain gap. Overall, our study lays a foundation for scalable, fine-grained taxonomic classification of beetles and supports broader applications in sample-efficient and domain-adaptive learning for ecological computer vision.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".