---
dataset_info:
  features:
  - name: images
    list:
      image:
        decode: false
  - name: image_group_id
    dtype: string
  - name: qa
    list:
    - name: view
      dtype: string
    - name: inputs
      dtype: string
    - name: choices_list
      list: string
    - name: labels
      dtype: int64
    - name: metadata
      dtype: string
  splits:
  - name: test
    num_bytes: 5096142
    num_examples: 828
  - name: train
    num_bytes: 510314876
    num_examples: 83190
  - name: validation
    num_bytes: 5089681
    num_examples: 834
  download_size: 520708749
  dataset_size: 520500699
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
language:
- en
license: mit
source_datasets: []
task_categories:
- multiple-choice
- visual-question-answering
tags:
- decision-model
- grounding
- procedural
---

# shapeworld-grounding

Procedurally verified color/shape and spatial grounding using ShapeWorld's renderer.

Native scene objects provide every candidate box. Queries are retained only when
exactly one object satisfies their attributes and, where used, spatial relation.
Non-overlapping, fully visible scenes have three to six shapes. Independent
split/scene seeds and image hashes prevent train/evaluation image overlap.
Images are generated as PNG, stored once per scene with up to two grouped QAs.
ShapeWorld's MIT license applies; this is a derived grounding task, not its
original caption-agreement benchmark. No language model labels or distractors.

Original data: [original distribution](https://github.com/AlexKuhnle/ShapeWorld). Repackaged as parquet for [tasksource](https://github.com/sileod/tasksource) by [scripts/repackage_dataset/procedural/shapeworld_grounding.py](https://github.com/sileod/tasksource/blob/main/scripts/repackage_dataset/procedural/shapeworld_grounding.py#L44).

Images are stored once with grouped `qa` records: test: 1,000 questions / 828 image groups, validation: 1,000 questions / 834 image groups, train: 100,000 questions / 83,190 image groups.

[Conversion provenance](provenance.json): source revisions, hashes, configuration and exclusions.

[Coarse source/recast audit](https://github.com/sileod/tasksource/blob/main/docs/jev/audits/shapeworld-grounding-recast.json).