VisionFoundry-10K: 10,000 Synthetic VQA Samples for 10 Vision Tasks

VisionFoundry-10K is a synthetic visual question answering dataset containing 10,000 image-question-answer triples. The data was created by the VisionFoundry pipeline, which uses an LLM to generate task-aware content and a text-to-image model to synthesize images, with samples filtered by a multimodal verifier. It was authored by zlab-princeton and last updated on Hugging Face in April 2026.

Use Cases

• Training vision-language models on synthetic visual question answering data.
• Benchmarking model performance across 10 distinct vision-centric tasks.
• Studying the efficacy of synthetic data pipelines for generating aligned multimodal samples.
• Developing or fine-tuning models for tasks requiring visual perception and reasoning.

Strengths

• Contains 10,000 precisely aligned image-question-answer triples.
• Covers a diverse range of 10 distinct vision-centric tasks.
• Uses a verifier to filter samples, which likely improves alignment quality.

Limitations

• Data is entirely synthetic, which may not reflect real-world image distributions or complexities.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is known, but other metadata like license and file formats is unknown.

Provenance

Source

zlab-princeton via Hugging Face.

Collection Method

Generated by the VisionFoundry pipeline: an LLM creates questions, answers, and prompts; a text-to-image model synthesizes images; a multimodal verifier filters for alignment.

Time Range

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Freshness

Last updated 2026-04-13 05:17:20; freshness should be verified.

Geography

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