Image-Based Dietary Assessment Using the Swedish Plate Model: Evaluation of Deep Learning–Based You Only Look Once (YOLO) Models
Bibliographic record
Abstract
Background: Recent advances in computer vision, particularly in deep learning, have significantly enhanced object recognition capabilities in images. Among these, real-time object detection frameworks such as You Only Look Once (YOLO) have shown promise across various domains. This study explores the application of YOLO-based object detection for food identification and portion estimation, with a focus on its alignment with the Swedish plate model recommended by the National Food Agency. Objective: The primary aim of this study is to evaluate and compare the performance of 3 YOLO variants (YOLOv7, YOLOv8, and YOLOv9) in detecting individual food components and estimating their relative proportions within images, based on public health dietary guidelines. Methods: A custom dataset comprising 3707 annotated food images spanning 42 food classes was developed for this study. A series of preprocessing and data augmentation techniques were applied to enhance dataset quality and improve model generalization. The models were evaluated using standard metrics, including precision, recall, mean average precision, and F1-score. Results: Among the evaluated models, YOLOv8 outperformed YOLOv7 and YOLOv9 in both peak precision and F1-scores. It achieved a peak precision of 82.4%, compared with 73.34% for YOLOv7 and 80.11% for YOLOv9, indicating superior accuracy in both food classification and portion estimation tasks. YOLOv8 also demonstrated higher confidence in its predictions. However, all models faced challenges in distinguishing visually similar food items, underscoring the complexity of fine-grained food recognition. Conclusions: While YOLO-based models, particularly YOLOv8, show strong potential for food and portion recognition aligned with dietary models, further refinement is needed. Improvements in model architecture and greater diversity in training data are essential before these systems can be reliably deployed in health and dietary monitoring applications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".