Datasets:
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license: cc-by-4.0
task_categories:
- visual-question-answering
- image-text-to-text
- image-feature-extraction
language:
- en
tags:
- textvqa
- ocr
- vqa
- vision-language
- lance
- clip-embeddings
pretty_name: textvqa-lance
size_categories:
- 10K<n<100K
---
# TextVQA (Lance Format)
Lance-formatted version of [TextVQA](https://textvqa.org/) — VQA where the question requires *reading* text in the image — sourced from [`lmms-lab/textvqa`](https://huggingface.co/datasets/lmms-lab/textvqa).
Each row carries the image bytes, the question, the 10 reference answers, the OCR tokens detected by the dataset's pre-processing, and CLIP image + question embeddings.
## Splits
| Split | Rows |
|-------|------|
| `validation.lance` | 5,000 |
| `train.lance` | 34,602 |
## Schema
| Column | Type | Notes |
|---|---|---|
| `id` | `int64` | Row index within split |
| `image` | `large_binary` | Inline JPEG bytes |
| `image_id` | `string?` | TextVQA image id |
| `question_id` | `string?` | TextVQA question id |
| `question` | `string` | The question text |
| `answers` | `list<string>` | 10 annotator answers |
| `answer` | `string` | First answer — used as canonical / FTS target |
| `ocr_tokens` | `list<string>` | OCR tokens detected on the image |
| `image_classes` | `list<string>` | OpenImages-style scene tags from the source |
| `set_name` | `string?` | Source partition (`train`, `val`) |
| `image_emb` | `fixed_size_list<float32, 512>` | OpenCLIP image embedding (cosine-normalized) |
| `question_emb` | `fixed_size_list<float32, 512>` | OpenCLIP text embedding of the question |
## Pre-built indices
- `IVF_PQ` on `image_emb` and `question_emb` — `metric=cosine`
- `INVERTED` (FTS) on `question` and `answer`
- `BTREE` on `image_id`, `question_id`, `set_name`
## Quick start
```python
import lance
ds = lance.dataset("hf://datasets/lance-format/textvqa-lance/data/validation.lance")
print(ds.count_rows(), ds.schema.names, ds.list_indices())
```
## Cross-modal text→image search
```python
import lance, pyarrow as pa, open_clip, torch
model, _, _ = open_clip.create_model_and_transforms("ViT-B-32", pretrained="laion2b_s34b_b79k")
tokenizer = open_clip.get_tokenizer("ViT-B-32")
model = model.eval().cuda().half()
with torch.no_grad():
q = model.encode_text(tokenizer(["what brand is on this billboard?"]).cuda())
q = (q / q.norm(dim=-1, keepdim=True)).float().cpu().numpy()[0]
ds = lance.dataset("hf://datasets/lance-format/textvqa-lance/data/validation.lance")
emb_field = ds.schema.field("image_emb")
hits = ds.scanner(
nearest={"column": "image_emb", "q": pa.array([q.tolist()], type=emb_field.type)[0], "k": 10},
columns=["question", "answer", "ocr_tokens"],
).to_table().to_pylist()
```
## Why Lance?
- One dataset for images + questions + answers + OCR + dual embeddings + indices — no JSON/feature folders.
- Cross-modal search and OCR-text filtering work on the same dataset on the Hub.
- Schema evolution: add columns (alternate OCR systems, model predictions) without rewriting the data.
## Source & license
Converted from [`lmms-lab/textvqa`](https://huggingface.co/datasets/lmms-lab/textvqa). TextVQA is released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) by Singh et al. (Facebook AI Research).
## Citation
```
@inproceedings{singh2019towards,
title={Towards VQA models that can read},
author={Singh, Amanpreet and Natarajan, Vivek and Shah, Meet and Jiang, Yu and Chen, Xinlei and Batra, Dhruv and Parikh, Devi and Rohrbach, Marcus},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2019}
}
```
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