Dataset selection is critical for effective pre-training of fish detection models for underwater video
Bibliographic record
Abstract
Abstract Underwater digital monitoring systems using acoustics and video have the potential to transform marine monitoring and fisheries stock assessment but generate significant amounts of data, shifting the burden from data collection to data analysis. Machine learning (ML) is a potential solution but remains underutilized for marine monitoring, partly due to the time and cost of annotating new training datasets for each marine class and habitat. This raises the pivotal question: “How can we train marine machine learning models with limited annotated data?” We catalog publicly available marine datasets annotated for detection and classification, investigating the feasibility of leveraging a fish detector trained on three existing datasets to detect fish in a new small underwater marine dataset. We compare the accuracy and training time of pre-trained models to those without pre-training. We find pre-training with OzFish yields faster convergence and comparable performance with smaller training datasets. However, pre-training with some datasets reduced performance and increased training time. We expect our catalog of publicly available marine datasets will assist in the selection of pre-training datasets. Our results underscore the need for diverse, large, publicly available marine datasets with varied habitat and class distributions to develop and integrate ML models into automated systems for monitoring marine ecosystems.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".