mlsanityData to 7 Oct 2026

Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food

arXiv 2103.03375 ↗First posted 4 Mar 2021cs.CV cs.LGcited by 58

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Abstract (arXiv)

Understanding the nutritional content of food from visual data is a challenging computer vision problem, with the potential to have a positive and widespread impact on public health. Studies in this area are limited to existing datasets in the field that lack sufficient diversity or labels required for training models with nutritional understanding capability. We introduce Nutrition5k, a novel dataset of 5k diverse, real world food dishes with corresponding video streams, depth images, component weights, and high accuracy nutritional content annotation. We demonstrate the potential of this dataset by training a computer vision algorithm capable of predicting the caloric and macronutrient values of a complex, real world dish at an accuracy that outperforms professional nutritionists. Further we present a baseline for incorporating depth sensor data to improve nutrition predictions. We will publicly release Nutrition5k in the hope that it will accelerate innovation in the space of nutritional understanding.

Key facts. Cited by 58 papers in the map (data through 7 Oct 2026). Builds on 7 of its 24 references that are in the map. First posted on arXiv 4 Mar 2021.

Builds on

7 of 24 references.

References · 7

  1. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  2. 2017Revisiting Unreasonable Effectiveness of Data in Deep Learning Eracited by 916
  3. 2018Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Imagescited by 118
  4. 2016Simultaneous Food Localization and Recognitioncited by 14
  5. 2019MUSEFood: Multi-sensor-based Food Volume Estimation on Smartphonescited by 9
  6. 2017Computer vision-based food calorie estimation: dataset, method, and experimentcited by 8
  7. 2020Multi-Task Learning for Calorie Prediction on a Novel Large-Scale Recipe Dataset Enriched with Nutritional Informationcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 4; 2023: 10; 2024: 16; 2025: 10; 2026: 14.

58 papers.

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  1. 2021Large Scale Visual Food Recognitioncited by 75
  2. 2021A Large-Scale Benchmark for Food Image Segmentationcited by 45
  3. 2023FoodLMM: A Versatile Food Assistant using Large Multi-modal Modelcited by 32
  4. 2024Position: Measure Dataset Diversity, Don't Just Claim Itcited by 26
  5. 2024NutriBench: A Dataset for Evaluating Large Language Models on Nutrition Estimation from Meal Descriptionscited by 17
  6. 2023An End-to-end Food Portion Estimation Framework Based on Shape Reconstruction from Monocular Imagecited by 15
  7. 2024On Domain-Adaptive Post-Training for Multimodal Large Language Modelscited by 13
  8. 2024Food Portion Estimation via 3D Object Scalingcited by 12
  9. 2024RoDE: Linear Rectified Mixture of Diverse Experts for Food Large Multi-Modal Modelscited by 11
  10. 2022Image Based Food Energy Estimation With Depth Domain Adaptationcited by 10
  11. 2023DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusioncited by 10
  12. 2024FoodSky: A Food-oriented Large Language Model that Passes the Chef and Dietetic Examinationcited by 10
38 more
  1. 2021A review on vision-based analysis for automatic dietary assessmentcited by 9
  2. 2022GrainSpace: A Large-scale Dataset for Fine-grained and Domain-adaptive Recognition of Cereal Grainscited by 9
  3. 2024From Canteen Food to Daily Meals: Generalizing Food Recognition to More Practical Scenarioscited by 9
  4. 2024MFP3D: Monocular Food Portion Estimation Leveraging 3D Point Cloudscited by 9
  5. 2021Applications of knowledge graphs for food science and industrycited by 8
  6. 2024MetaFood CVPR 2024 Challenge on Physically Informed 3D Food Reconstruction: Methods and Resultscited by 8
  7. 2025Are Vision-Language Models Ready for Dietary Assessment? Exploring the Next Frontier in AI-Powered Food Image Recognitioncited by 6
  8. 2026GLEN-Bench: A Graph-Language based Benchmark for Nutritional Healthcited by 6
  9. 2023NutritionVerse-3D: A 3D Food Model Dataset for Nutritional Intake Estimationcited by 5
  10. 2023Transferring Knowledge for Food Image Segmentation using Transformers and Convolutionscited by 5
  11. 2023Identifying the Defective: Detecting Damaged Grains for Cereal Appearance Inspectioncited by 5
  12. 2024OVFoodSeg: Elevating Open-Vocabulary Food Image Segmentation via Image-Informed Textual Representationcited by 5
  13. 2024VolETA: One- and Few-shot Food Volume Estimationcited by 5
  14. 2025Advancing Food Nutrition Estimation via Visual-Ingredient Feature Fusioncited by 5
  15. 2022Towards the Creation of a Nutrition and Food Group Based Image Databasecited by 4
  16. 2024Nutrition Estimation for Dietary Management: A Transformer Approach with Depth Sensingcited by 4
  17. 2023FoodFusion: A Latent Diffusion Model for Realistic Food Image Generationcited by 3
  18. 2024FoodMem: Near Real-time and Precise Food Video Segmentationcited by 3
  19. 2025Comprehensive Evaluation of Large Multimodal Models for Nutrition Analysis: A New Benchmark Enriched with Contextual Metadatacited by 3
  20. 2026Food Portion Estimation: From Pixels to Caloriescited by 3
  21. 2023NutritionVerse: Empirical Study of Various Dietary Intake Estimation Approachescited by 2
  22. 2023NutritionVerse-Synth: An Open Access Synthetically Generated 2D Food Scene Dataset for Dietary Intake Estimationcited by 2
  23. 2024NutritionVerse-Direct: Exploring Deep Neural Networks for Multitask Nutrition Prediction from Food Imagescited by 2
  24. 2025KERL: Knowledge-Enhanced Personalized Recipe Recommendation using Large Language Modelscited by 2
  25. 2025SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insightscited by 2
  26. 2026OmniFood8K: Single-Image Nutrition Estimation via Hierarchical Frequency-Aligned Fusioncited by 2
  27. 2024Shape-Preserving Generation of Food Images for Automatic Dietary Assessmentcited by 1
  28. 2025VolE: A Point-cloud Framework for Food 3D Reconstruction and Volume Estimationcited by 1
  29. 2025Dual-LoRA and Quality-Enhanced Pseudo Replay for Multimodal Continual Food Learningcited by 1
  30. 2025CookAnything: A Framework for Flexible and Consistent Multi-Step Recipe Image Generationcited by 1
  31. 2026NutriMLLM: Multimodal Large Language Models for Dietary Micronutrient Analysiscited by 1
  32. 2022Food Ingredients Recognition through Multi-label Learningcited by 0
  33. 2023Leveraging Human Salience to Improve Calorie Estimationcited by 0
  34. 2024A SAM based Tool for Semi-Automatic Food Annotationcited by 0
  35. 2025Investigating the Impact of Large-Scale Pre-training on Nutritional Content Estimation from 2D Imagescited by 0
  36. 2025SnappyMeal: Design and Longitudinal Evaluation of a Multimodal AI Food Logging Applicationcited by 0
  37. 2026DietDelta: A Vision-Language Approach for Dietary Assessment via Before-and-After Imagescited by 0
  38. 2026Not Your Stereo-Typical Estimator: Combining Vision and Language for Volume Perceptioncited by 0

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