FineVision: Open Data Is All You Need
The advancement of vision-language models (VLMs) is hampered by a fragmented
landscape of inconsistent and contaminated public datasets. We introduce
FineVision, a meticulously collected, curated, and unified corpus of 24 million
samples - the largest open resource of its kind. We unify more than 200 sources
into 185 subsets via a semi-automated, human-in-the-loop pipeline: automation
performs bulk ingestion and schema mapping, while reviewers audit mappings and
spot-check outputs to verify faithful consumption of annotations, appropriate
formatting and diversity, and safety; issues trigger targeted fixes and
re-runs. The workflow further applies rigorous de-duplication within and across
sources and decontamination against 66 public benchmarks. FineVision also
encompasses agentic/GUI tasks with a unified action space; reviewers validate
schemas and inspect a sample of trajectories to confirm executable fidelity.
Models trained on FineVision consistently outperform those trained on existing
open mixtures across a broad evaluation suite, underscoring the benefits of
scale, data hygiene, and balanced automation with human oversight. We release
the corpus and curation tools to accelerate data-centric VLM research.
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Luis Wiedmann
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Orr Zohar
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Amir Mahla
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Xiaohan Wang
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2025-10-21T14:03:29.907Z
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Xiaohan Wang
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nicholswang
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Rui Li
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Thibaud Frere
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Leandro von Werra
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Aritra Roy Gosthipaty
claimed_verified
2025-10-21T14:03:26.559Z
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Aritra Roy Gosthipaty
user
ariG23498
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Andrés Marafioti
claimed_verified
2025-10-22T15:45:57.947Z
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Andres Marafioti
user
andito
FineVision: Open Data Is All You Need
The advancement of vision-language models (VLMs) is hampered by a fragmented
landscape of inconsistent and contaminated public datasets. We introduce
FineVision, a meticulously collected, curated, and unified corpus of 24 million
samples - the largest open resource of its kind. We unify more than 200 sources
into 185 subsets via a semi-automated, human-in-the-loop pipeline: automation
performs bulk ingestion and schema mapping, while reviewers audit mappings and
spot-check outputs to verify faithful consumption of annotations, appropriate
formatting and diversity, and safety; issues trigger targeted fixes and
re-runs. The workflow further applies rigorous de-duplication within and across
sources and decontamination against 66 public benchmarks. FineVision also
encompasses agentic/GUI tasks with a unified action space; reviewers validate
schemas and inspect a sample of trajectories to confirm executable fidelity.
Models trained on FineVision consistently outperform those trained on existing
open mixtures across a broad evaluation suite, underscoring the benefits of
scale, data hygiene, and balanced automation with human oversight. We release
the corpus and curation tools to accelerate data-centric VLM research.
FineVision, a large-scale and curated dataset, enhances vision-language models through rigorous data collection, de-duplication, and human oversight, leading to improved performance.
vision-language models FineVision semi-automated pipeline human-in-the-loop schema mapping de-duplication decontamination agentic/GUI tasks unified action space data-centric research