| --- |
| 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](https://github.com/vineetha00/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 |
|
|
| ```bash |
| pip install visionflow |
| vf bench |
| vf accuracy |
| ``` |
|
|
