Poultry Meat Classification Using MobileNetV2 Pretrained Model
Why this work is in the frame
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Bibliographic record
Abstract
In Morocco the meat business risks being targeted by fraud and adulteration, leading customers to probe the authenticity of the meat.The traditional styles for verifying meat types are expensive and consuming time.In this work, we propose a method based on computer vision and deep learning, which allows the bracket and isolation between turkey and chicken and Fayoumi and chicken farmer meat.We created a model grounded on the pre-trained Mobile Net V2 model and trained it with a Dataset containing the collected images of the four poultries.The evaluation of this model has given satisfactory results and has demonstrated that the model is suitable to predict the meat class with a delicacy of over 98%.The algorithm can be generalized to separate between authentic and fake meat.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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 it