Astur Apple image Dataset
Bibliographic record
Abstract
Struture of file: 2022-v2-9classes-AsturApple-Balanced-SIZE224-train-dev-test.hdf5 ------------------------------------------------------------------------------------------- This file contains dataset of 6108 cider apple color images, 224x224 pixels to support the article "Transfer learning with convolutional neural networks for classification of cider apple varieties" results. -The images belong to nine apple classes: 'BLANQUINA' 'CARRIO' 'FLORINA' 'FUENTES' 'PRIETA' 'RAXAO' 'REINETA ENCARNADA' 'REINETA PINTA' 'REINETA ROJA DEL CANADA' -Full dataset is split in 4886 images for training, 611 for testing and 611 for validation. -Class labels are codified as one-hot enconding binary labels. (e.g. [0 0 0 0 0 1 0] ---> Reineta Pinta) -Training image set are store as tensor "trainX": (4168,224,224,3) -Training class labels "trainY": (4886,7) -Test image set tensor "testX": (611, 224,224,3) -Test image binary class labels "testY": (611, 7) -Validation image set tensor "devX": (611, 224,224,3) -Validation binary class labels "devY": (611, 7) * file was created with h5py module in Python.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.006 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.067 | 0.146 |
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".