metadata
license: mit
task_categories:
- image-to-text
- visual-question-answering
tags:
- vlm
- document-intelligence
- quantization
- benchmark
- synthetic
VisionFlow evaluation set and benchmark results
Companion data for VisionFlow, an edge-first quantized VLM pipeline for local document intelligence.
Contents
| Path | What it is |
|---|---|
sample_images/ |
Three synthetic document images: a surgical report, a cold-chain shipping manifest, and a bar chart |
labeled_set.json |
Ground-truth field labels for those images |
results/ |
Raw output from vf bench, vf accuracy, and the ONNX provider benchmark |
Ground truth
Labels are the literal strings drawn into each image by the generator script, so they are exact by construction rather than human-annotated. The trade-off is scope: this is 3 images and 21 fields, sized to expose relative differences between quantization levels — not to support an absolute accuracy claim.
Synthetic data notice
Every name, MRN, date, diagnosis code, SKU, lot number, and revenue figure here is fabricated. No real patient, shipment, or company data is present. The medical image is a plausible-looking surgical report and is not a real medical record.
Reproducing
pip install visionflow
vf bench
vf accuracy