DeepFruit: A dataset of fruit images for fruit classification and calories calculation
Bibliographic record
Abstract
A dataset of fully labeled images of 20 different kinds of fruits is developed for research purposes in the area of detection, recognition, and classification of fruits. Applications can range from fruit recognition to calorie estimation, and other innovative applications. Using this dataset, researchers are given the opportunity to research and develop automatic systems for the detection and recognition of fruit images using deep learning algorithms, computer vision, and machine learning algorithms. The main contribution is a very large dataset of fully labeled images that are publicly accessible and available for all researchers free of charge. The dataset is called "DeepFruit", which consists of 21,122 fruit images for 8 different fruit set combinations. Each image contains a different combination of four or five fruits. The fruit images were captured on different plate sizes, shapes, and colors with varying angles, brightness levels, and distances. The dataset images were captured with various angles and distances but could be cleared by utilizing the preprocessing techniques that allow for noise removal, centering of the image, and others. Preprocessing was done on the dataset such as image rotation & cropping, scale normalization, and others to make the images uniform. The dataset is randomly partitioned into an 80% training set (16,899 images) and a 20% testing set (4,223 images). The dataset along with the labels is publicly accessible at: https://data.mendeley.com/datasets/5prc54r4rt.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.010 |
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".