{
  "abstract": "Background/Objectives: Accurate recognition of food items and estimation of portion sizes from images is a key challenge in advancing dietary assessment and nutritional monitoring. In this study, we benchmark twenty-six configurations (seventeen model releases) of state-of-the-art open-source Vision–Language Models (VLMs), varying in size (≤15 GB) and number of parameters (<25B), on real-world food photographs collected in a university canteen, doing so to evaluate their potential for supporting automated nutrition logging and health interventions. Methods: We construct a curated benchmark dataset with human-annotated labels, including both food item names and scale-measured portion sizes, and assess model predictions based on recognition performance and mean absolute error in the portion estimate. The semantic matcher used to score food recognition is validated against a panel of human judges, and all results are reported with bootstrap confidence intervals and stochastic replicates. Results: Our experiments show that the VLM qwen2.5vl:7b achieves the highest overall performance in food identification (F1=0.68), outperforming other open-source models in terms of precision and F1 score. Under the human-validated semantic matcher, it also outperforms the more recent successors of the same model families, which nevertheless improve portion estimation and, under a threshold-free LLM judge less sensitive to verbose descriptions, match or exceed it in recognition. For comparison, we evaluate five proprietary models (four OpenAI generations and one Anthropic model) as a benchmark and find that they tend to outperform open-source VLMs in both food recognition (best F1=0.77, GPT-4o) and portion size estimation error; notably, however, the most recent proprietary generations do not improve on their predecessors for this task under our evaluation protocol. Conclusions: These findings highlight the potential and current limitations of general-purpose VLMs in fine-grained food understanding tasks, with implications for the design of scalable, vision-based dietary assessment systems in digital health.",
  "abstract_shown": true,
  "affiliations": [
    "Dipartimento di Neuroscienze, Sezione di Biofisica, Università Cattolica del Sacro Cuore, 00168 Rome, Italy",
    "UOC Fisica per le Scienze della Vita, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS (Istituto di Ricovero e Cura a Carattere Scientifico), 00168 Rome, Italy",
    "Gruppo Fastal Blu Sistemi, Via Nomentana 263, 00161 Rome, Italy",
    "Digital Innovation Hub Roma, Chirale S.r.l., Via Ignazio Persico 32-46, 00154 Rome, Italy"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Tommaso Marchetti",
      "orcid": "0000-0003-0064-0692"
    },
    {
      "affs": [
        2
      ],
      "name": "Stefano Capezzone",
      "orcid": "0009-0001-1857-5043"
    },
    {
      "affs": [
        3
      ],
      "name": "Rosita Esposito",
      "orcid": "0009-0004-1350-5064"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Marco De Spirito",
      "orcid": "0000-0003-4260-5107"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Giuseppe Maulucci",
      "orcid": "0000-0002-2154-319X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dc34kcr59tfbq.bib",
  "cited_by": 0,
  "doi": "10.3390/nu18213582",
  "fulltext_url": null,
  "id": "dc34kcr59tfbq",
  "issns": [
    "2072-6643"
  ],
  "landing_url": "https://www.mdpi.com/2072-6643/18/21/3582",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/dc34kcr59tfbq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:56Z",
      "native_id": "10.3390/nu18213582",
      "source": "crossref",
      "url": "https://www.mdpi.com/2072-6643/18/21/3582"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "issn:2072-6643",
      "subfield": "nutrition"
    }
  ],
  "title": "Benchmarking Open-Source Vision–Language Models for Food Image Recognition and Portion Size Estimation",
  "title_text": "Benchmarking Open-Source Vision–Language Models for Food Image Recognition and Portion Size Estimation",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T00:21:06Z",
  "url": "https://research.draconitic.com/paper/dc34kcr59tfbq",
  "venue": "Nutrients",
  "versions": []
}