2D Prediction of the Nutritional Composition of Dishes from Food Images: Deep Learning Algorithm Selection and Data Curation Beyond the Nutrition5k Project
Highlights
- Eight deep learning architectures were benchmarked for the direct 2D prediction of dish mass and nutrient content from food images, combining four feature extractors (ResNet 50 and 101, InceptionV3, ViT-B-16) with two regression heads.
- Model performance was evaluated across four curated ground-truth datasets derived from Nutrition5k, incorporating both U.S. and Italian food composition data and ingredient-level corrections.
- ResNet101 and ViT-B-16 architectures consistently outperformed the benchmark InceptionV3, suggesting their suitability for real-time, image-based nutrient estimation in digital health tools.
- An analysis of consistently mispredicted dishes across most algorithms revealed issues related to image quality and labeling, emphasizing the importance of automated quality control in scalable app development.
- Beyond dishes requiring ingredient mass corrections, commonly mispredicted groups included complex salads, chicken-based and egg-based dishes, and Western-style breakfasts.
- Instead of frame filtering on a subset of the test set including mispredicted dishes, ingredient mass correction substantially improved prediction metrics, highlighting the need for curated inputs in future mobile dietary assessment apps.
Abstract
1. Introduction
2. Materials and Methods
2.1. Analytical Framework and Ground Truth Datasets for Analysis
2.2. Deep Learning for Food Image Recognition
2.2.1. Splitting Data into Training, Validation, and Test Sets
2.2.2. Selecting the Architecture and Pre-Training Weights for the Automatic Feature Extraction
2.2.3. Extracting and Preprocessing Frames
2.2.4. Setting the Architecture for Problem Solving
2.2.5. Setting the Optimization of the Loss Function for Feature Extraction and Problem Solving
2.2.6. Setting the Performance Metrics for Problem Solving
2.2.7. Predicting Mass, Energy, and Macronutrient Content with and Without Ingredient–Mass Correction and Italian Nutritional Value Calculation
2.2.8. Sensitivity Analysis: Comparison of Predicted Versus Calculated Energy Content in 4-Task and 5-Task Scenarios
2.3. Statistical Analysis and Visual Inspection of Frames
3. Results
3.1. Descriptive Statistics on Observed and Predicted Values from the Test Set with and Without Ingredient–Mass Correction and Italian Nutritional Value Calculation
3.2. Comparison Between Observed and Predicted Values from the Test Set with and Without Ingredient–Mass Correction and Italian Nutritional Value Calculation
3.3. Comparison Between Metrics with and Without Ingredient–Mass Correction and Italian Nutritional Value Calculation
3.4. Selecting the Best-Performing Deep Learning Algorithms
3.5. Incorrectly Predicted Dishes Across Target Variables and Datasets: A Focus on Nutritional Characteristics of the Ingredients After Manual Frame Filtering
- 7 dishes (dish_1563566909, dish_1566414291, dish_1566501575, dish_1566501594, dish_1566589933, dish_1563566939, and dish_1563566965) showed mismatches between visible ingredients and the (lower number of) ingredients listed in the metadata, mostly leading to overestimated predictions; the only reasonable exception was represented by carbohydrates, where dish ingredients were not entirely captured by most available images;
- 5 dishes (dish_1558630325, dish_1558720236, dish_1562703447, dish_1563389626, and dish_1566838351) suffered from image quality problems in all frames, including poor framing, blurriness, cropping, operator hands/feet in the frame, and background elements (e.g., chairs, tables, cables, and phones).
- Salad-based group (44%): Complex dishes, with at least two vegetables (e.g., raw and/or cooked), two protein sources (e.g., fish and/or meat), two cereal types, herbs, and dressings (median number of ingredients per dish: 16, IQR: 11.25–20.5);
- Chicken-based group (25%): Dishes primarily composed of chicken (grilled, breaded, or sliced in a cold salad), simpler in composition, but sometimes visually ambiguous (median number of ingredients per dish: 4, IQR: 3–5);
- Eggs-based group (13%): Dishes featuring mainly scrambled eggs or boiled egg whites, with few ingredients, but often showing partial occlusion (median number of ingredients per dish: 5, IQR: 3–6);
- Western-inspired breakfast foods group (13%): Hybrid category featuring dishes with a limited number of ingredients, blending elements of both sweet (e.g., fruit, almonds, or brownies) and savory (e.g., potatoes, sausages, or cheese) breakfasts without a single dominant component (median number of ingredients per dish: 2, IQR: 1–3).
3.6. Confirming the Best-Performing Deep Learning Algorithms After Manual Frame Filtering
3.7. Sensitivity Analysis: Comparing Predicted Versus Calculated Energy Content Using the 4-Task and 5-Task Deep Learning Algorithms
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AI | Artificial Intelligence |
DL | Deep Learning |
FCDB | Food Composition DataBase |
Inc | Inception |
MAE | Mean Absolute Error |
MAPE | Mean Absolute Percentage Error |
MLP | Multi-Layer Perceptron |
ReLU | Rectified Linear Unit |
RGB | Red, Green, and Blue |
ResNet | Residual Network |
RMSE | Root Mean Squared Error |
ViT | Vision Transformer |
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Original Model Identifier 1 | Label Used in Tables and Figures 2 |
---|---|
Inception_V3_IMAGENET1K_V1_2+1 | IncV3_2+1 |
Inception_V3_IMAGENET1K_V1_2+2 | IncV3_2+2 |
ResNet101_IMAGENET1K_V2_2+1 | R101_2+1 |
ResNet101_IMAGENET1K_V2_2+2 | R101_2+2 |
ResNet50_IMAGENET1K_V2_2+1 | R50_2+1 |
ResNet50_IMAGENET1K_V2_2+2 | R50_2+2 |
ViT_B_16_IMAGENET1K_SWAG_E2E_V1_2+1 | ViT-B-16_2+1 |
ViT_B_16_IMAGENET1K_SWAG_E2E_V1_2+2 | ViT-B-16_2+2 |
Single Available Datasets 1 | Merged Dataset 1 | ||||
---|---|---|---|---|---|
Target Variable | US FCDB—No Correction | US FCDB—Correction | IT FCDB—No Correction | IT FCDB—Correction | |
Mass (g) | |||||
Median (Q1, Q3) | 142.0 (70.5, 250.0) | 142.0 (70.0, 249.0) | 143.0 (70.5, 251.5) | 142.0 (70.0, 249.0) | 142.0 (70.0, 250.0) |
Mean (SD) | 189.1 (332.1) | 177.3 (142.9) | 205.9 (467.1) | 177.4 (142.9) | 187.4 (304) |
Min, Max | 1.0, 7974.0 | 1.0, 871.0 | 1.0, 8094.0 | 1.0, 871.0 | 1.0, 8094.0 |
Energy content (kcal) | |||||
Median (Q1, Q3) | 161.9 (61.5, 343.4) | 160.0 (61.2, 339.7) | 167.2 (60.6, 333.3) | 165.6 (60.3, 329.8) | 164.5 (60.8, 335.3) |
Mean (SD) | 252.1 (536.2) | 224.0 (205.6) | 264.7 (687.8) | 227.5 (218.1) | 242.1 (461.0) |
Min, Max | 0.0, 9485.8 | 0.0, 1050.5 | 0.0, 12,376.1 | 0.0, 1332.6 | 0.0, 12,376.1 |
Protein content (g) | |||||
Median (Q1, Q3) | 8.7 (1.6, 23.0) | 8.5 (1.6, 22.7) | 7.9 (1.6, 22.0) | 7.9 (1.6, 21.8) | 8.3 (1.6, 22.2) |
Mean (SD) | 15.7 (19.3) | 15.4 (18.9) | 15.2 (18.7) | 14.9 (17.9) | 15.3 (18.7) |
Min, Max | 0.0, 105.6 | 0.0, 105.6 | 0.0, 123.6 | 0.0, 89.8 | 0.0, 123.6 |
Fat content (g) | |||||
Median (Q1, Q3) | 6.6 (0.6, 17.6) | 6.6 (0.6, 17.4) | 7.5 (0.4, 17.4) | 7.4 (0.4, 17.2) | 6.9 (0.5, 17.4) |
Mean (SD) | 13.6 (48.3) | 11.1 (13.5) | 15.4 (66.8) | 11.9 (15.1) | 13.0 (42.4) |
Min, Max | 0.0, 875.5 | 0.0, 84.2 | 0.0, 1221.8 | 0.0, 115.8 | 0.0, 1221.8 |
Carbohydrates content (g) | |||||
Median (Q1, Q3) | 13.1 (4.4, 25.7) | 13.0 (4.4, 25.4) | 9.1 (2.3, 20.5) | 9.0 (2.2, 20.5) | 11.3 (3.3, 23.5) |
Mean (SD) | 19.2 (31.5) | 17.5 (16.4) | 15.0 (17.9) | 14.6 (16.9) | 16.6 (21.7) |
Min, Max | 0.0, 506.1 | 0.0, 85.8 | 0.0, 138.6 | 0.0, 116.5 | 0.0, 506.1 |
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Bianco, R.; Coluccia, S.; Marinoni, M.; Falcon, A.; Fiori, F.; Serra, G.; Ferraroni, M.; Edefonti, V.; Parpinel, M. 2D Prediction of the Nutritional Composition of Dishes from Food Images: Deep Learning Algorithm Selection and Data Curation Beyond the Nutrition5k Project. Nutrients 2025, 17, 2196. https://doi.org/10.3390/nu17132196
Bianco R, Coluccia S, Marinoni M, Falcon A, Fiori F, Serra G, Ferraroni M, Edefonti V, Parpinel M. 2D Prediction of the Nutritional Composition of Dishes from Food Images: Deep Learning Algorithm Selection and Data Curation Beyond the Nutrition5k Project. Nutrients. 2025; 17(13):2196. https://doi.org/10.3390/nu17132196
Chicago/Turabian StyleBianco, Rachele, Sergio Coluccia, Michela Marinoni, Alex Falcon, Federica Fiori, Giuseppe Serra, Monica Ferraroni, Valeria Edefonti, and Maria Parpinel. 2025. "2D Prediction of the Nutritional Composition of Dishes from Food Images: Deep Learning Algorithm Selection and Data Curation Beyond the Nutrition5k Project" Nutrients 17, no. 13: 2196. https://doi.org/10.3390/nu17132196
APA StyleBianco, R., Coluccia, S., Marinoni, M., Falcon, A., Fiori, F., Serra, G., Ferraroni, M., Edefonti, V., & Parpinel, M. (2025). 2D Prediction of the Nutritional Composition of Dishes from Food Images: Deep Learning Algorithm Selection and Data Curation Beyond the Nutrition5k Project. Nutrients, 17(13), 2196. https://doi.org/10.3390/nu17132196