Datasets:

lance-format
/

gqa-testdev-balanced-lance

license: cc-by-4.0
task_categories:
  - visual-question-answering
  - image-text-to-text
language:
  - en
tags:
  - gqa
  - compositional-vqa
  - vqa
  - vision-language
  - lance
  - clip-embeddings
pretty_name: gqa-testdev-balanced-lance
size_categories:
  - 10K<n<100K

GQA testdev-balanced (Lance Format)

Lance-formatted version of the canonical GQA testdev_balanced slice — 12,578 compositional VQA questions joined with the matching 398 images — sourced from lmms-lab/GQA.

lmms-lab/GQA exposes instructions and images as separate parquet configs; this Lance dataset joins them on imageId, so each row has the question, the answer, the GQA reasoning-program tags, and the image bytes inline.

Splits

Split	Rows	Distinct images
`testdev.lance`	12,578	398

Train (train_balanced_instructions × train_balanced_images, ~943k Q's × 72k images, ~10 GB images) and val splits are not bundled by default — pass --instr-config/--images-config to gqa/dataprep.py to extend.

Schema

Column	Type	Notes
`id`	`int64`	Row index
`image`	`large_binary`	Inline JPEG bytes (image is duplicated across rows that share an `image_id`)
`image_id`	`string`	GQA scene-graph image id
`question_id`	`string`	GQA question id
`question`	`string`	Compositional natural-language question
`answers`	`list<string>`	One-element list (the GQA short answer)
`answer`	`string`	Same short answer (canonical / FTS target)
`full_answer`	`string?`	Full sentence answer
`structural`	`string?`	One of `verify`, `query`, `compare`, `choose`, `logical`
`semantic`	`string?`	One of `attr`, `cat`, `global`, `obj`, `rel`
`detailed`	`string?`	Fine-grained type (e.g. `weatherVerifyC`)
`is_balanced`	`bool`	GQA balanced subset flag
`group_global` / `group_local`	`string?`	GQA reasoning-group ids
`semantic_str`	`string?`	Compact description of the reasoning program
`image_emb`	`fixed_size_list<float32, 512>`	CLIP image embedding (cosine-normalized)
`question_emb`	`fixed_size_list<float32, 512>`	CLIP text embedding of the question

Pre-built indices

IVF_PQ on image_emb and question_emb — metric=cosine
INVERTED (FTS) on question and answer
BITMAP on structural, semantic, detailed
BTREE on image_id, question_id

Quick start

import lance
ds = lance.dataset("hf://datasets/lance-format/gqa-testdev-balanced-lance/data/testdev.lance")
print(ds.count_rows(), ds.schema.names, ds.list_indices())

Filter by reasoning type

import lance
ds = lance.dataset("hf://datasets/lance-format/gqa-testdev-balanced-lance/data/testdev.lance")
verify_qs = ds.scanner(filter="structural = 'verify'", columns=["question", "answer"], limit=5).to_table()

Why Lance?

One dataset for the joined image + question + answer + reasoning-program metadata + dual embeddings + indices — no instructions/images parquet split to keep in sync.
Schema evolution: add columns (alternate scene graphs, model predictions) without rewriting the data.

Source & license

Converted from lmms-lab/GQA. GQA is released under CC BY 4.0 by Hudson and Manning (Stanford NLP).

Citation

@inproceedings{hudson2019gqa,
  title={GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering},
  author={Hudson, Drew A. and Manning, Christopher D.},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2019}
}