Deep Learning for Marine Resources Classification in Non-Structured Scenarios: Training vs. Transfer Learning
Bibliographic record
Abstract
This paper proposes the use of Deep learning for Marine Resources classification (DeepMaRe), especially the classification of fish images captured in non-structured scenarios. Tests conducted using two state of the art deep CNN architectures show that Deep learning can be used efficiently in this type of classifications. AlexNet and GoogLeNet were both used to classify the images captured onboard of fishing boats. The best results were obtained using transfer learning and pretrained models. Using this strategy, AlexNet and GoogLeNet achieve respectively a success rate of 94.01% and 96.01%. These results are further improved by extracting and using fish areas for training and classification. The accuracy of cropped fish areas classification obtained 96.35% with AlexNet and 96.54% with GoogLeNet. Also, the top-2 accuracy obtained by GoogLeNet was equal to 97.87% for the full image classification and 98.94% for the cropped images.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".