Datasets:
Intended use and limitations
MunicipalBench measures image-level violation classification and instance localisation on a fixed synthetic test set. Use it to compare systems under the same scoring rules and to inspect differences across classes and domains. It has no training split and has not been validated for operational municipal report processing.
Synthetic scenes and available context
All distributed images are synthetic. A high score therefore does not establish accuracy on real reports, where capture conditions, scene frequencies and ambiguity can differ. Historical real controls are omitted and do not provide a released real-world validation set.
Some classes require information outside the image, such as whether a vehicle has permission to park. The taxonomy records definitions and exclusions, but a single frame cannot verify every condition. Treat scores as agreement with the benchmark's visual labels, not legal determinations.
Generation artefacts and repeated templates may influence predictions. The release does not establish independence between images from the same generation family. Its image-bootstrap intervals do not account for that unrecorded clustering. Publicly available images and labels also create a contamination risk; disclose prior exposure and record the dataset revision.
Annotation dependence and resolved labels
First-pass records do not rule out shared prefill. High agreement, particularly box overlap, may partly reflect the annotation interface rather than independent judgements. Conversely, low rating agreement indicates that raters did not consistently apply those quality judgements; it is not repaired by evaluating agreement after resolution.
Final labels combine automatic rules, VLM votes, human adjudication and earlier manager selections. These routes have different guarantees. For example, the native automatic box criterion can pass despite unmatched instances, and VLM votes can retain shared errors. The release records the method for every image so those differences remain inspectable. It does not claim an independent expert correctness audit of all final labels.
Resolved quality ratings can use conservative minima. Their means and histograms describe final labels rather than an unbiased survey of image quality. See the annotation protocol and resolution rules.
Uneven support and comparison limits
Class frequencies vary, and multi-label images belong to several domain slices. Micro scores give more influence to frequent targets; macro scores and per-class results expose a different view. Small differences between model point estimates are not evidence of statistically significant superiority. Model-score tables do not include paired confidence intervals for differences.
The model comparison has specific execution limitations, including Gemini's mixed box-axis interpretation and mutable hosted providers. These remain in model provenance. The reported scores describe saved outputs under those settings; they are not controlled hardware, latency or cost comparisons.
Semantic relaxation can credit related but genuinely distinct classes, so its higher scores must not replace exact-class results. Historical studies use old gold, different populations and, in some cases, only three runs or a positive-only subset. Their individual limits are explained beside their findings in the supplementary guide.