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())

Load with LanceDB

These tables can also be consumed by LanceDB, the multimodal lakehouse and embedded search library built on top of Lance, for simplified vector search and other queries.

import lancedb

db = lancedb.connect("hf://datasets/lance-format/gqa-testdev-balanced-lance/data")
tbl = db.open_table("testdev")
print(f"LanceDB table opened with {len(tbl)} image-question pairs")

LanceDB vector search

import lancedb

db = lancedb.connect("hf://datasets/lance-format/gqa-testdev-balanced-lance/data")
tbl = db.open_table("testdev")

ref = tbl.search().limit(1).select(["question_emb", "question"]).to_list()[0]
query_embedding = ref["question_emb"]

results = (
    tbl.search(query_embedding, vector_column_name="question_emb")
    .metric("cosine")
    .select(["question", "answer"])
    .limit(5)
    .to_list()
)

LanceDB full-text search

import lancedb

db = lancedb.connect("hf://datasets/lance-format/gqa-testdev-balanced-lance/data")
tbl = db.open_table("testdev")

results = (
    tbl.search("color of the car")
    .select(["question", "answer"])
    .limit(10)
    .to_list()
)

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()

Filter with LanceDB

import lancedb

db = lancedb.connect("hf://datasets/lance-format/gqa-testdev-balanced-lance/data")
tbl = db.open_table("testdev")
verify_qs = (
    tbl.search()
    .where("structural = 'verify'")
    .select(["question", "answer"])
    .limit(5)
    .to_list()
)

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}
}