---
license: other
license_name: govtech-singapore
license_link: https://huggingface.co/datasets/govtech/municipalbench-full/blob/main/LICENSE
pretty_name: MunicipalBench
language:
  - en
task_categories:
  - object-detection
  - image-classification
tags:
  - evaluation
  - synthetic
  - municipal-services
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    default: true
    data_files:
      - split: test
        path: data/test/**
    drop_labels: true
---

# MunicipalBench

MunicipalBench is an **evaluation benchmark** of 11,534 synthetic
municipal-scene images, with 97 violation classes across 13 domains. It supports
image-level multi-label classification and instance localisation. It is not
validated as training data or as a measure of performance on real municipal
reports.

**Version v1.0.0-rc1.**

## Licence

MunicipalBench is provided under the [GovTech Singapore licence][licence], which
incorporates MIT terms with additional GovTech conditions. It covers the
repository contents subject to the exclusions stated in the licence, including
specified government branding, third-party material and restricted assets. The
full terms include Singapore governing law and SIAC arbitration.

## Contents

| Population         | Images |
| ------------------ | -----: |
| Ordinary synthetic | 10,838 |
| Synthetic traps    |    696 |
| Violation present  |  8,455 |
| No violation       |  3,079 |
| Total              | 11,534 |

All distributed images have final annotations and original first-pass records.
There are 19,337 final violation boxes and 23,710 first-pass submissions. Real
controls and unusable images are excluded. `trap` is a source category; it does
not imply a negative final annotation.

```text
data/test/                  original images and ImageFolder metadata
annotations/                final annotations, first passes, COCO export
taxonomy/                   class definitions, domains, semantic policy
evaluation/dataset/         composition, IAA and image quality
evaluation/models/          released-population results and predictions
evaluation/supplementary/   historical results and selected secondary analyses
code/                       scoring and IAA source with checksum inventory
examples/                   loading, overlay and reproduction commands
release/                    manifest, provenance and checksums
docs/                       annotation, scoring, supplementary studies and limits
```

Start with [ground_truth.json](annotations/ground_truth.json) for complete final
annotations.
[first_pass_annotations.json](annotations/first_pass_annotations.json) contains
the original human submissions used for inter-annotator agreement (IAA), not a
second final ground truth. The optional
[ground_truth_coco.json](annotations/ground_truth_coco.json) is the same labels
and boxes in the **Common Objects in Context (COCO)** interchange format. It is
not additional annotation. Native boxes are normalised XYWH; COCO boxes are
pixel XYWH. See the [data dictionary](docs/data-dictionary.md).

## Load and inspect

```python
from datasets import load_dataset

# Pin revision to a reviewed commit SHA for a reproducible evaluation.
dataset = load_dataset("govtech/municipalbench-full", split="test")
print(dataset[0]["image_id"], dataset[0]["objects"])
dataset[0]["image"].show()
```

After downloading a snapshot, install `code/requirements.txt` in your Python
environment. Run `python examples/load_dataset.py --root .` to check the
ImageFolder dataset, or `python examples/render_annotation.py --root .` for an
annotated preview. No custom remote dataset script is required.

## Dataset evaluation

Composition, annotation reliability and final quality ratings answer different
questions. [Composition results](evaluation/dataset/composition.json) describe
class/domain support and label resolution. The agreement results below measure
consistency between first-pass annotators.
[Final quality distributions](evaluation/dataset/image_quality.json) describe
the resolved plausibility and naturalness ratings; the
[annotation guide](docs/annotation-protocol.md) explains their scales and why
their averages must not be confused with agreement.

The primary IAA analysis uses first passes for exactly the released 11,534
images, before final resolution. One reported submission is excluded from
eligible annotation comparisons; its original record remains available.

| Measure | Estimate | 95% interval |
| --- | ---: | --- |
| Plausibility ordinal alpha | 0.2957 | 0.2792–0.3119 |
| Naturalness ordinal alpha | 0.2242 | 0.2069–0.2418 |
| Exact class-set agreement | 0.5829 | 0.5739–0.5912 |
| Positive label agreement | 0.7248 | 0.7187–0.7308 |
| Instance F1 at IoU 0.50 | 0.5726 | 0.5629–0.5824 |

<details>
<summary>Annotation agreement by domain</summary>

| Domain | Positive-label agreement (95% CI) | Instance F1 (95% CI) |
| --- | --- | --- |
| Estate maintenance | 0.6623 (0.6524–0.6716) | 0.4861 (0.4711–0.4995) |
| Roads and footpaths | 0.6622 (0.6421–0.6816) | 0.5081 (0.4849–0.5322) |
| Animals | 0.9235 (0.9035–0.9419) | 0.8510 (0.8046–0.8920) |
| Pests | 0.8862 (0.8649–0.9076) | 0.7357 (0.6867–0.7838) |
| Parks and greenery | 0.6793 (0.6492–0.7068) | 0.5752 (0.5391–0.6082) |
| Abandoned equipment | 0.8252 (0.8019–0.8476) | 0.7151 (0.6776–0.7483) |
| Illegal parking | 0.6618 (0.6211–0.7000) | 0.6083 (0.5638–0.6519) |
| Drains and sewers | 0.8098 (0.7848–0.8339) | 0.7247 (0.6884–0.7582) |
| Water bodies | 0.8197 (0.7953–0.8427) | 0.6423 (0.5899–0.6946) |
| Fire safety and emergency access | 0.8058 (0.7741–0.8384) | 0.7104 (0.6660–0.7519) |
| Safety | 0.7796 (0.7561–0.8022) | 0.7206 (0.6955–0.7469) |
| Public transport infrastructure | 0.8959 (0.8665–0.9210) | 0.6688 (0.6176–0.7197) |
| Street furniture and amenities | 0.8906 (0.8636–0.9153) | 0.7994 (0.7576–0.8392) |

Each domain is evaluated across the 11,534 released images, considering only
that domain's labels and boxes. Instance F1 uses exact classes and IoU ≥ 0.50.
Parentheses contain the existing 95% image-bootstrap intervals. N/A means
undefined; † flags limited support. See the
[full agreement results](evaluation/dataset/annotation_agreement.json) and
[CSV](evaluation/dataset/agreement.csv) for class-level results, rating
agreement, support flags and sensitivity analyses.

</details>

<details>
<summary>Plausibility and naturalness agreement by domain</summary>

| Domain | Plausibility ordinal alpha (95% CI) | Naturalness ordinal alpha (95% CI) |
| --- | --- | --- |
| Estate maintenance | 0.2129 (0.1883–0.2406) | 0.1938 (0.1664–0.2239) |
| Roads and footpaths | 0.1818 (0.1325–0.2303) | 0.1873 (0.1376–0.2369) |
| Animals | 0.1876 (0.0835–0.2861) | 0.2485 (0.1439–0.3363) |
| Pests | 0.0533 (-0.0309–0.1279) | 0.3809 (0.3092–0.4497) |
| Parks and greenery | 0.1535 (0.0825–0.2227) | 0.1370 (0.0650–0.2054) |
| Abandoned equipment | 0.0683 (-0.0131–0.1457) | 0.1682 (0.0853–0.2459) |
| Illegal parking | 0.0365 (-0.0688–0.1378) | 0.1899 (0.0885–0.2926) |
| Drains and sewers | 0.1038 (0.0204–0.1869) | 0.2171 (0.1352–0.2982) |
| Water bodies | 0.1833 (0.1118–0.2530) | 0.2912 (0.2201–0.3587) |
| Fire safety and emergency access | 0.2516 (0.1449–0.3504) | 0.2333 (0.1209–0.3471) |
| Safety | 0.0588 (-0.0210–0.1318) | 0.2139 (0.1386–0.2878) |
| Public transport infrastructure | 0.0010 (-0.0992–0.1094) | 0.1314 (0.0220–0.2358) |
| Street furniture and amenities | 0.1839 (0.0671–0.2962) | 0.1100 (0.0022–0.2125) |

These are ordinal Krippendorff's alpha estimates from first-pass ratings,
measuring annotator consistency rather than average image quality. Each domain
uses images with at least one final gold label in that domain and eligible
rating pairs. These subsets overlap and differ in size. Parentheses contain 95%
image-bootstrap intervals; N/A means undefined and † flags limited support.
Exact support counts remain in the
[full agreement results](evaluation/dataset/annotation_agreement.json).

</details>

Intervals are 95% percentile bootstrap intervals resampling images, with 2,000
replicates. Exact class-set agreement includes agreed negatives; positive
agreement and instance F1 provide complementary views. Agreement on quality
ratings uses ordinal Krippendorff's alpha. IAA measures consistency, not
final-label correctness. Shared prefill may affect independence;
generation-family dependence is not modelled. Read the
[annotation protocol](docs/annotation-protocol.md) and
[limitations](docs/limitations.md) before interpreting these values.

Final resolution uses GT-001/002/003/004/005/008, with method provenance on each
image. [Ground-truth methods](docs/ground-truth-methods.md) documents the rules
and counts. Agreement is measured on original first passes before resolution.

## Model evaluation

The [six-model comparison](evaluation/models/synthetic11534/README.md) includes
YOLO-World, Gemma 4 12B, GLM 5.3 Flash, GPT-5.6 Luna, Muse Glimmer 30B and
Gemini 3.8 Flash. Each run covers all 11,534 images with valid responses. The
table uses **11,534 shared-valid images**, exact class matching and localisation
IoU ≥ 0.50.

| Model | Classification micro F2 | Localisation micro F2 |
| --- | ---: | ---: |
| YOLO-World | 0.1193 | 0.0604 |
| Gemma 4 12B | 0.3764 | 0.1454 |
| GLM 5.3 Flash | 0.6264 | 0.3442 |
| GPT-5.6 Luna | 0.5937 | 0.2928 |
| Muse Glimmer 30B | 0.5990 | 0.2676 |
| Gemini 3.8 Flash | 0.6710 | 0.3239 |

<details>
<summary>Classification performance by domain</summary>

Classification micro F2 for each domain, using exact class matching on the same
11,534 shared-valid images. Counts are pooled across the domain's classes.

| Domain | YOLO-World | Gemma 4 12B | GLM 5.3 Flash | GPT-5.6 Luna | Muse Glimmer 30B | Gemini 3.8 Flash |
| --- | ---: | ---: | ---: | ---: | ---: | ---: |
| Estate maintenance | 0.0633 | 0.3967 | 0.5929 | 0.5659 | 0.5676 | 0.6204 |
| Roads and footpaths | 0.0759 | 0.3510 | 0.6323 | 0.6638 | 0.6070 | 0.6934 |
| Animals | 0.3184 | 0.5131 | 0.8822 | 0.8507 | 0.8784 | 0.9167 |
| Pests | 0.0082 | 0.2665 | 0.6872 | 0.5261 | 0.7003 | 0.7470 |
| Parks and greenery | 0.1264 | 0.2383 | 0.5195 | 0.5528 | 0.5919 | 0.5850 |
| Abandoned equipment | 0.5743 | 0.2980 | 0.7933 | 0.4577 | 0.5246 | 0.7118 |
| Illegal parking | 0.1760 | 0.1283 | 0.4883 | 0.4067 | 0.2527 | 0.5719 |
| Drains and sewers | 0.0000 | 0.3401 | 0.5999 | 0.5761 | 0.5641 | 0.7105 |
| Water bodies | 0.0152 | 0.4950 | 0.6711 | 0.6540 | 0.6292 | 0.7376 |
| Fire safety and emergency access | 0.0302 | 0.3364 | 0.6374 | 0.6222 | 0.6578 | 0.8198 |
| Safety | 0.2384 | 0.4693 | 0.8101 | 0.7452 | 0.7864 | 0.8246 |
| Public transport infrastructure | 0.1043 | 0.3687 | 0.6023 | 0.6968 | 0.6843 | 0.7335 |
| Street furniture and amenities | 0.0916 | 0.3351 | 0.5220 | 0.4458 | 0.5465 | 0.5208 |

</details>

<details>
<summary>Localisation performance by domain</summary>

Localisation micro F2 for each domain, using exact class matching and one-to-one
box matching at IoU ≥ 0.50 on the same 11,534 shared-valid images.

| Domain | YOLO-World | Gemma 4 12B | GLM 5.3 Flash | GPT-5.6 Luna | Muse Glimmer 30B | Gemini 3.8 Flash |
| --- | ---: | ---: | ---: | ---: | ---: | ---: |
| Estate maintenance | 0.0273 | 0.1242 | 0.2672 | 0.2212 | 0.1984 | 0.2830 |
| Roads and footpaths | 0.0249 | 0.1641 | 0.3085 | 0.3063 | 0.2712 | 0.2786 |
| Animals | 0.1878 | 0.3255 | 0.7247 | 0.7062 | 0.5242 | 0.5167 |
| Pests | 0.0047 | 0.1245 | 0.3920 | 0.3083 | 0.2864 | 0.4308 |
| Parks and greenery | 0.0770 | 0.1497 | 0.3696 | 0.3794 | 0.3992 | 0.3210 |
| Abandoned equipment | 0.3083 | 0.2199 | 0.6684 | 0.3516 | 0.3970 | 0.4706 |
| Illegal parking | 0.0610 | 0.1001 | 0.4563 | 0.3586 | 0.2170 | 0.3351 |
| Drains and sewers | 0.0000 | 0.1766 | 0.3945 | 0.3572 | 0.3542 | 0.4104 |
| Water bodies | 0.0063 | 0.1361 | 0.3953 | 0.3280 | 0.2960 | 0.3808 |
| Fire safety and emergency access | 0.0000 | 0.1068 | 0.4709 | 0.3710 | 0.3837 | 0.4828 |
| Safety | 0.1504 | 0.2668 | 0.6181 | 0.5401 | 0.5202 | 0.3711 |
| Public transport infrastructure | 0.0299 | 0.1208 | 0.2778 | 0.3052 | 0.2813 | 0.2934 |
| Street furniture and amenities | 0.0368 | 0.1631 | 0.3786 | 0.3061 | 0.3686 | 0.1811 |

</details>

Domain scores are point estimates; small differences do not establish a
statistically significant ranking. Domain class support varies, and domain
scores are not averaged to obtain the overall micro score. N/A means undefined.
The [full model results](evaluation/models/synthetic11534/results.json) include
per-class scores, domain precision/recall and IoU sensitivity.

Per-model valid scopes and coverage are also published. Parse failures, if
present, must be counted separately and excluded from scoring, never treated as
correct negatives. Scoring does not filter or rank retained detections by
confidence. YOLO's inference cutoff was 0.10; VLMs had no numerical confidence
cutoff. No pre-run qualification was performed for the hosted runs. GLM was
restricted from a larger run; Gemini retains a known box-axis interpretation
limitation. See the [run provenance](evaluation/models/synthetic11534/README.md)
and [evaluation protocol](docs/evaluation.md).

Historical 302- and 1,200-image benchmark aggregates are supplementary. Their
source populations included **1 and 107 undistributed real images**,
respectively, and used older gold. These historical benchmarks cannot be fully
reproduced from this release. They are not subsets to use for reproducing the
new primary benchmark scores. No real-image assets or per-image historical
records are included.

## Citation and further results

The [supplementary guide](docs/supplementary.md) explains the questions,
comparisons and main findings for semantic relaxation, historical benchmarks,
Gemma prompt grouping, repeatability, sampling and parking prompts. It links
each study to its machine-readable results.

Cite MunicipalBench using this repository URL and the exact commit SHA used in
your evaluation.

[licence]: https://huggingface.co/datasets/govtech/municipalbench-full/blob/main/LICENSE