lance-format/textvqa-lance

TextVQA (Lance Format)

Lance-formatted version of TextVQA — VQA where the question requires reading text in the image — sourced from 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

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

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. TextVQA is released under CC 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}
}