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AbdullahKhanSherwani/dubai-taxi-advertising-synthetic

Dubai Taxi Advertising Compliance (Synthetic) 63 synthetic photorealistic images of Dubai RTA taxis carrying advertising, built to evaluate whether vision-language models can judge out-of-home (OOH) advertising compliance rules from a single photograph. Each image is generated to be an unambiguous pass or fail against one specific rule from a Dubai taxi advertising technical checklist. The dataset is an evaluation set — it is small, adversarially balanced, and deliberately… See the full description on the dataset page: https://huggingface.co/datasets/AbdullahKhanSherwani/dubai-taxi-advertising-synthetic.

sourceHugging Facecc-by-4.0updated 2mo agoView on Hugging Face
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Dataset Card

Dubai Taxi Advertising Compliance (Synthetic)

63 synthetic photorealistic images of Dubai RTA taxis carrying advertising, built to evaluate whether vision-language models can judge out-of-home (OOH) advertising compliance rules from a single photograph.

Each image is generated to be an unambiguous pass or fail against one specific rule from a Dubai taxi advertising technical checklist. The dataset is an evaluation set — it is small, adversarially balanced, and deliberately includes rules that are hard or impossible to judge from an image alone.

Contents

Images63 PNG
Rules8
Verdicts31 pass / 32 fail
Viewsside (39), rear (16), front (8)
Generatorgoogle/gemini-3-pro-image

Fields

  • —file_name — image file
  • —scenario_id — unique id, e.g. TAXI_POS_01_pass_v2
  • —rule_id — which rule this image tests
  • —view — camera angle: front, side, rear
  • —intended_verdict — pass or fail, the label
  • —intended_note — one line on what makes it pass or fail
  • —generator — model that produced the image

Rules

RuleWhat it testsPassFail
TAXI_POS_01Ad placement below the boundary line, rearAd on boot lid below tail lightsAd across rear windscreen glass
TAXI_POS_02Ad placement below the boundary line, sideAd on door metal only, glass bareAd continues onto door glass
TAXI_VIS_01Front elevation free of advertisingFront cleanAd on bonnet/bumper
TAXI_VIS_02Tail lights unobstructedLenses bareVinyl over tail-light lenses
TAXI_WIN_01Perforated film on rear side windows onlyRear passenger glass onlyFront/driver glass also covered
TAXI_WIN_04≤50% of side window glass covered~25% coveredNearly full pane covered
TAXI_WIN_05Commuter can still see outPerforated, cabin visibleSolid opaque panel
TAXI_BODY_01Operator logo remains visibleLogo clear on bonnet and doorFull wrap hides the logo

Two rules (TAXI_POS_01, TAXI_POS_02) reference a red-line placement boundary defined in an external diagram from the source checklist. That diagram is not included here — models evaluated on those rules were shown it as a second image. Without it, those two rules are under-specified.

Intended use

Benchmarking VLMs on rule-grounded visual compliance judgement. A correct evaluation should let a model answer cannot_determine: TAXI_WIN_04 asks for a percentage of glass area, which is a genuine measurement question, and abstention there is a legitimate result rather than a failure.

Limitations

Labels are intended, not verified. Each label states what the image was prompted to depict. No human has independently confirmed that every generated image actually shows what its prompt asked for, and image generators do drift from prompts. Treat these as weak labels and spot-check before drawing strong conclusions.

Synthetic, not real. These are generated images, not photographs of real vehicles. Livery details, plates, and street furniture are approximations of Dubai RTA taxis and are not authoritative. Any text rendered in the images — brand names, taglines — is invented.

Small and unbalanced. 7–8 images per rule. Per-rule accuracy on this set has wide error bars, and one rule (TAXI_WIN_05) has 7 rather than 8 images.

Not a regulatory reference. The rule wordings are paraphrases written for model evaluation. Do not use this dataset or its rule text to determine actual compliance with Dubai RTA advertising regulation.