Datasets:
File size: 3,789 Bytes
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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`](https://huggingface.co/datasets/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
```python
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
```python
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`](https://huggingface.co/datasets/lmms-lab/GQA). GQA is released under [CC BY 4.0](https://creativecommons.org/licenses/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}
}
```
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