Paper 2510.17269
FineVision: Open Data Is All You Need
- Published
- Oct 2025
- Research lab
- Independent
- Citations
- 38
- GitHub
- Not linked
01 In brief
Summary
FineVision is a large-scale, open corpus of 24 million samples (17 million images, 89 million turns, 9.5 billion answer tokens) for training vision-language models.
It unifies over 200 public sources into 185 subsets via a semi-automated, human-in-the-loop pipeline that includes schema mapping, cleaning, de-duplication, and decontamination against 66 benchmarks.
Models trained on FineVision outperform those trained on existing open mixtures (The Cauldron, Cambrian-7M, LLaVA-OneVision) by 10.8, 5.1, and 13.8 percentage points on average across 11 benchmarks, respectively.
FineVision also includes agentic/GUI data with a unified action space, enabling new capabilities.
The dataset is released publicly along with curation tools.
02 From the paper
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.