---
license: cc-by-4.0
task_categories:
- visual-question-answering
- image-classification
tags:
- ppe
- construction-safety
- vqa
pretty_name: PPE Detection Benchmark Eval Set
---

# PPE Benchmark Eval Set (v1)

A held-out, human-verified benchmark for evaluating vision-language models on
personal protective equipment (PPE) detection — specifically hardhat and
safety-vest presence — framed as a VQA-style classification task.

## What this is

96 images, balanced 24/24/24/24 across the four hardhat × vest combinations
(yes/yes, yes/no, no/yes, no/no). Sourced from a forked, filtered subset of
the [karabuk-university PPE dataset](https://universe.roboflow.com/karabuk-university-kb29f/ppe-oevsc)
on Roboflow Universe (CC BY 4.0), then individually visually verified —
**not just filtered by class label** — to remove:
- Mislabeled images (e.g. life jackets tagged as safety vests)
- Images with an undetected second person in frame
- Images where PPE was held, not worn
- Off-context images (e.g. a child wearing a hardhat, watermarked stock photos)

~40% of the initial `no-hardhat + vest` bucket was found to be contaminated
by these issues before this cleanup pass.

## Labels

Each image has:
- `hardhat_label`: yes / no
- `vest_label`: yes / no
- `framing`: full-body / half-body
- `angle`: front / side / rear / low / high
- `lighting`: studio / indoor / daylight / shade / dim
- `ambiguity_type`: flags 5 genuine hard-negative cases (cap or beanie worn
  instead of a hardhat) for testing false-positive robustness

Framing split: 56 half-body / 40 full-body. Lighting: studio 25, indoor 42,
daylight 22, shade 3, dim 4.

## Known limitations

**This is a small benchmark (24 images/class).** It's sufficient for
catching large, obvious differences between models, but:
- Confidence intervals on close model comparisons will be wide
- Per-condition slicing (e.g. "accuracy in dim lighting") has very few
  samples per cell and should be treated as indicative, not conclusive
- Only 5 hard-negative examples exist — not enough for a standalone
  hard-negative accuracy metric

v2 (planned) will expand volume using the same per-image verification
methodology, particularly for the `no-hardhat + no-vest` bucket, which was
the rarest combination in the source pool.

## Intended use

Held-out evaluation only — do not use for training. Built to benchmark
finetuned PPE-detection VLMs (SmolVLM, Qwen3.5, Qwen3-VL) against a common,
verified ground truth.

## License

Images sourced under CC BY 4.0 from Roboflow Universe
(karabuk-university/ppe-oevsc). Attribution: karabuk-university.