| --- |
| 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} |
| } |
| ``` |
|
|
