lance-format/gqa-testdev-balanced-lance

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}
}