MM-Food-100K: A 100,000-Sample Multimodal Food Intelligence Dataset with Verifiable Provenance
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912537460604928 |
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| author | Dong, Yi Muraoka, Yusuke Shi, Scott Zhang, Yi |
| author_facet | Dong, Yi Muraoka, Yusuke Shi, Scott Zhang, Yi |
| contents | We present MM-Food-100K, a public 100,000-sample multimodal food intelligence dataset with verifiable provenance. It is a curated approximately 10% open subset of an original 1.2 million, quality-accepted corpus of food images annotated for a wide range of information (such as dish name, region of creation). The corpus was collected over six weeks from over 87,000 contributors using the Codatta contribution model, which combines community sourcing with configurable AI-assisted quality checks; each submission is linked to a wallet address in a secure off-chain ledger for traceability, with a full on-chain protocol on the roadmap. We describe the schema, pipeline, and QA, and validate utility by fine-tuning large vision-language models (ChatGPT 5, ChatGPT OSS, Qwen-Max) on image-based nutrition prediction. Fine-tuning yields consistent gains over out-of-box baselines across standard metrics; we report results primarily on the MM-Food-100K subset. We release MM-Food-100K for publicly free access and retain approximately 90% for potential commercial access with revenue sharing to contributors. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_10429 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MM-Food-100K: A 100,000-Sample Multimodal Food Intelligence Dataset with Verifiable Provenance Dong, Yi Muraoka, Yusuke Shi, Scott Zhang, Yi Artificial Intelligence Cryptography and Security Computer Vision and Pattern Recognition I.2.10; I.2.6 We present MM-Food-100K, a public 100,000-sample multimodal food intelligence dataset with verifiable provenance. It is a curated approximately 10% open subset of an original 1.2 million, quality-accepted corpus of food images annotated for a wide range of information (such as dish name, region of creation). The corpus was collected over six weeks from over 87,000 contributors using the Codatta contribution model, which combines community sourcing with configurable AI-assisted quality checks; each submission is linked to a wallet address in a secure off-chain ledger for traceability, with a full on-chain protocol on the roadmap. We describe the schema, pipeline, and QA, and validate utility by fine-tuning large vision-language models (ChatGPT 5, ChatGPT OSS, Qwen-Max) on image-based nutrition prediction. Fine-tuning yields consistent gains over out-of-box baselines across standard metrics; we report results primarily on the MM-Food-100K subset. We release MM-Food-100K for publicly free access and retain approximately 90% for potential commercial access with revenue sharing to contributors. |
| title | MM-Food-100K: A 100,000-Sample Multimodal Food Intelligence Dataset with Verifiable Provenance |
| topic | Artificial Intelligence Cryptography and Security Computer Vision and Pattern Recognition I.2.10; I.2.6 |
| url | https://arxiv.org/abs/2508.10429 |