README.md

---
license: cc-by-4.0
task_categories:
- visual-question-answering
language:
- en
tags:
- vlm-evaluation
- metric-estimation
- counterfactual-interventions
- spatial-reasoning
pretty_name: Metric VQA
size_categories:
- 10K<n<100K
configs:
- config_name: itw
  data_files: itw/questions.jsonl
- config_name: objectron
  data_files: objectron/questions.jsonl
---

# Metric VQA

A two-split benchmark for diagnosing evidence use in vision-language models on
monocular metric object-size estimation: given a single RGB image and a
natural-language dimension query, predict the physical dimension in
centimeters.

- **In-the-wild (ITW):** 331 queries over 70 cluttered scene photographs,
  every ground-truth dimension measured by tape measure (0.8–245 cm,
  ~99 distinct objects). Each query carries a human-verified instance mask.
- **Objectron:** 10,482 queries over 3,494 frames from ~2,600 object
  instances in 9 categories of Google Objectron, ground truth from the
  dataset's oriented 3D bounding boxes.

The benchmark is a diagnostic instrument, not a leaderboard: the companion
intervention suite (object-only, object-ring, wrong-object, random-mask,
zoom, lens distortion, text-only) probes which evidence channels a model's
estimate actually depends on.

**Paper:** [Ill-Posed by Design: Probing Evidence Use in VLMs (arXiv:2606.24335)](https://arxiv.org/abs/2606.24335) · **Code:** https://github.com/bmeivar/MetricVQA

## Layout

```
itw/questions.jsonl         331 records (schema below)
itw/questions.csv           same records, flat CSV
itw/images/                 70 scene photographs (PNG)
itw/masks/                  331 binary instance masks (PNG, one per query)
objectron/questions.jsonl   10,482 records
objectron/questions.csv     same records, flat CSV
```

Objectron frames are distributed separately (see below) to respect the
source dataset's distribution terms; the extraction script reproduces them
deterministically from the public Objectron release.

## Record schema (JSONL, one object per line)

| field | type | splits | description |
|---|---|---|---|
| `query_id` | str | both | stable id, `itw_0001` / `obj_00001` |
| `split` | str | both | `itw` or `objectron` |
| `image` | str | both | path relative to the split directory |
| `question` | str | both | verbatim query; at evaluation time it is followed by the instruction *"Answer with a single float number in centimeters"* |
| `answer_cm` | float | both | ground-truth dimension in centimeters |
| `axis` | str | both | `length` / `height` / `width` / `depth` / `diameter` / `size`, parsed from the query |
| `target_phrase` | str | itw | referring expression grounding the query to one object |
| `mask` | str | itw | binary PNG instance mask for the target |
| `bbox_xyxy` | [int×4] | itw | tight box of the mask, pixel coordinates |
| `mask_source` | str | itw | `sam3_auto_verified` (SAM 3 from the phrase, passed human review) or `manual_box_prompt` (re-extracted from a human-drawn box) |
| `gt_method` | str | both | `tape_measure` or `objectron_3d_box` |
| `category` | str | objectron | one of the 9 Objectron categories |

## Annotation process (ITW)

Scenes were photographed and measured by the authors. Target masks were
initialized with SAM 3 from each query's referring phrase; a human annotator
reviewed all 331 masks and re-extracted those that failed review (32 objects,
covering 53 queries) using manually drawn box prompts, so every mask in the
release is human-verified. The `mask_source` field preserves provenance per
query.

## Evaluation protocol

Primary metric: **Acc@10%** — fraction of queries with |ŷ−y|/y ≤ 0.1.
Responses with no parsable number are excluded from accuracy denominators
(a parse failure is an instruction-following failure, not a size estimate);
report parse rates alongside. Paired intervention deltas are computed on
rows parsable in both conditions. Significance: scene-clustered sign-flip
permutation tests with Benjamini–Hochberg correction (queries sharing a
scene flip together).

## Licensing

- ITW images, masks, and annotations: **CC-BY-4.0**.
- Objectron-derived annotations: released under the terms of the
  [Objectron dataset license](https://github.com/google-research-datasets/Objectron),
  with attribution; frames are re-derivable from the public source.
- Evaluation and intervention code: **MIT**.

## Intended use and limitations

Intended for diagnosing evidence use and for benchmarking metric estimation
under monocular ambiguity. The ITW split is evaluation-only. Ground truth is
a single tape measurement per dimension; the human-reference row reported in
the paper is a single annotator, not a population estimate. Queries are in
English.