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image
imagewidth (px)
640
640
hardhat_label
stringclasses
2 values
vest_label
stringclasses
2 values
framing
stringclasses
2 values
angle
stringclasses
5 values
lighting
stringclasses
5 values
ambiguity_type
stringclasses
4 values
image_id
stringlengths
20
20
source
stringclasses
1 value
yes
yes
half-body
front
studio
none
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roboflow_karabuk_ppe
yes
yes
full-body
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roboflow_karabuk_ppe
yes
yes
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roboflow_karabuk_ppe
yes
yes
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roboflow_karabuk_ppe
yes
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roboflow_karabuk_ppe
yes
yes
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none
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roboflow_karabuk_ppe
yes
yes
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roboflow_karabuk_ppe
yes
yes
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roboflow_karabuk_ppe
yes
yes
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yes
yes
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roboflow_karabuk_ppe
yes
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yes
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no
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no
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no
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none
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none
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roboflow_karabuk_ppe
no
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none
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roboflow_karabuk_ppe
no
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full-body
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none
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roboflow_karabuk_ppe
no
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roboflow_karabuk_ppe

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 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.

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