FineVision: Open Data Is All You Need
Authors: name: Luis Wiedmann; name: Orr Zohar; name: Amir Mahla; name: Xiaohan Wang; name: Rui Li; name: Thibaud Frere; name: Leandro von Werra; name: Aritra Roy Gosthipaty; name: Andrés Marafioti
Published: 2025-10-20 07:54:46

Abstract: 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.