Seeds Image – Introduction and Baseline Experiments with the New Labeled Benchmark for Machine Learning Tasks
Bibliographic record
Abstract
The aim of this paper is to propose a new image data set for assessing the quality of solutions to machine learning tasks, in particular, deep neural networks. The data set is derived from X-ray images of wheat grains, in which three species, Kama, Rosa, and Canadian, are distinguished. In this paper, the structure of the data is presented in detail and ten pretrained deep neural networks are applied to identify individual wheat species. The Seeds Image Data Set, due to its compact nature, can compete with well-known and quite frequently used object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet, etc.). The compactness of the data set is based on a relatively small number of data instances, which shortens the rather time-consuming computing process. The proposed data set will be made available in a public repository, and the results presented will provide a starting point for other competing solutions for exploratory data analysis in the broad sense.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".