Datasets:

bmeivar
/

MetricVQA

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) · 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, 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.