Label-efficient underwater species classification with logistic regression on frozen foundation model embeddings

Abstract

Automated species classification from underwater imagery is bottlenecked by the cost of expert annotation, and supervised models trained on one dataset rarely transfer to new conditions. We investigate whether a simple classifier operating on frozen foundation model embeddings can close this gap. Using frozen DINOv3 ViT-B/16 embeddings with no fine-tuning, we train a logistic regression classifier and evaluate on the AQUA20 benchmark (20 marine species). At full supervision, logistic regression achieves 88.5% macro F1 compared to ConvNeXt's 88.9%, a gap of 0.4 percentage points, while outperforming the supervised baseline on 8 of 20 species. Under label scarcity, with 21 labeled examples per class (approximately 6% of training labels), macro F1 exceeds 80%. The near-parity with end-to-end supervised learning demonstrates that these general-purpose, frozen representations exhibit strong linear separability at the species level in the underwater domain. Our approach requires no deep learning training, no domain-specific data engineering, and no underwater-adapted models, establishing a practical, immediately deployable baseline for label-efficient marine species recognition. All results are reported on the held-out test set over 100 random seed initialisations. This is a preliminary report; further evaluations and ablations are forthcoming.