A new method for automatic date fruit classification
Why this work is in the frame
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Bibliographic record
Abstract
Date fruit classification by human is tedious, slow and requires several workers. In this paper, we propose a method for automatic classification of dates. Because dates of the same variety may considerably vary in terms of hardness, maturity level and shape, we represent each variety with a Gaussian mixture model (GMM). Calinski-Harabasz index has been adopted to estimate the optimal number of components for each GMM. Furthermore, the normality of samples belonging to each component is checked using Mardia's multivariate tests. Our method is able to accurately classify dates in spite of the large variation within some varieties and the small variation between some varieties. Moreover, it doesn't require any human intervention. To validate our method and as, to our knowledge, no date benchmark is publicly available; we introduce a new benchmark of 5,000 images from ten varieties. Experimental results demonstrate the effectiveness and the strength of our method.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it