Detection of Wagyu beef sources with image classification using convolutional neural network
Bibliographic record
Abstract
Wagyu beef originated in Japan. There are many types of Wagyu beef in the market around the globe, though. Primary sources may include Australia, the United States of America, Canada, and the United Kingdom. The authentic Japanese Wagyu is well known for its intense marbling, juicy rich flavor, and tenderness. And there are differences in flavor, texture, and quality between the different types of Wagyu. Nowadays, there is a growing interest in deep learning as a remarkable solution for several domain problems such as computer vision and image classification. In this study, we thus present an AI-based approach to identifying Wagyu beef sources with image classification. A deep neural network, CNN, was constructed to detect the marbled fat patterns of two sources, Japanese Wagyu and Australian Wagyu. The images were collected from reliable sources on the internet and augmented with DCGAN. The prediction of Wagyu sources achieved high accuracy of 95%. The learning model of Convolutional Neural Networks was found to be a promising method for the rapid characterization of the unique patterns of marbled fat layers. The classifier would benefit the customers for buying what they expect from the products in terms of quality and taste.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".