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.
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
Fields
file_name— image filescenario_id— unique id, e.g.TAXI_POS_01_pass_v2rule_id— which rule this image testsview— camera angle:front,side,rearintended_verdict—passorfail, the labelintended_note— one line on what makes it pass or failgenerator— model that produced the image
Rules
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.
