How many labels do you need? A decision framework for cross-habitat marine species recognition
This study presents a decision framework that quantifies the trade-off between labeling effort and recognition accuracy when transferring vision systems across marine habitats. The benchmark spans five datasets, three oceans, and three taxonomic groups. It finds that frozen self-supervised foundation features (DINOv2 + linear classifier) generalize well, requiring as few as 10-20 labeled images per species for reliable recognition at new sites, cutting annotation effort by roughly an order of magnitude.
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[Submitted on 28 Jun 2026]
Title:How many labels do you need? A decision framework for cross-habitat marine species recognition
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Abstract:Automated image recognition is increasingly used to scale ecological monitoring beyond manual annotation, yet ecologists lack evidence-based guidance on how much labelling effort reliable deployment at new sites requires. We present a decision framework quantifying the trade-off between labelling effort and recognition accuracy when transferring vision systems across marine habitats. The benchmark spans five datasets, three oceans, and three taxonomic groups (fish, corals, invertebrates), from tropical reefs in the Great Barrier Reef and French Polynesia to a temperate Danish fjord. We evaluated four recognition models (DINOv2, CLIP, ResNet-50, EfficientNet-B4) under four adaptation strategies (linear probing, LoRA, Visual Prompt Tuning, full fine-tuning) across three protocols: within-habitat transfer across 20 reef sites (240 runs), cross-dataset geographic transfer along a difficulty gradient (40 runs), and few-shot adaptation curves with 0-100 labelled samples per class (648 runs). Frozen self-supervised foundation features (DINOv2 + linear classifier, 1,538 trainable parameters) generalised to unseen reef sites at least as well as fully fine-tuned convolutional baselines four orders of magnitude larger; they learned species-diagnostic, habitat-invariant representations, whereas baselines encoded habitat-specific shortcuts that fail at new sites. As few as 10-20 labelled images per species sufficed to deploy reliable recognition at a new site, cutting annotation effort by roughly an order of magnitude.
Solution. Programmes expanding to new sites can deploy reliable recognition by pairing a frozen, open foundation model (DINOv2) with a simple linear classifier and annotating only 10-20 images per species - roughly 1-4 hours per site. The framework lets programmes budget labelling effort against expected accuracy across sites, ecosystems, and platforms.
Comments: 29 pages, 12 figures, 4 tables. Includes supplementary appendix
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.02559 [cs.CV]
(or arXiv:2607.02559v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.02559
arXiv-issued DOI via DataCite
Submission history
From: Alzayat Saleh [view email] [v1] Sun, 28 Jun 2026 03:06:45 UTC (4,687 KB)
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