Shrimp Classification Using Generative Adversarial Network with ResNet
Bibliographic record
Abstract
Shrimp species classification continues to be difficult in terms of morphological features, small datasets (in general), and environmental noise associated with image capture.Standard techniques based on handmade shallow features alone have proved very poor on challenging tasks.Recent developments in deep learning have demonstrated great potential; however, this relies heavily on a large and diverse amount of data, which limits its potential to be applied for shrimp studies, as such data resources are scarce.To alleviate the challenge, we propose a unified platform, which integrates shallow and deep Convolutional Neural Networks (CNN) features for improved classification accuracy.Furthermore, to bridge the gap in data availability and balance, Generative Adversarial Networks (GANs) are employed to synthesize realistic shrimp pictures, thereby broadening our training set with a wider range of possible inputs beyond those already used in traditional augmentation methods.Experimental results show the proposed method at 91.66% precision, 89.8% accuracy, and 0.94 F1-score, robustly.These data suggest that GAN-based augmentation and hybrid feature extraction contribute to a significant improvement of shrimp image classification and provide a great contribution for aquaculture monitoring and automatic marine species classification systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".