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())

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/textvqa-lance/data")
tbl = db.open_table("validation")
print(f"LanceDB table opened with {len(tbl)} image-question pairs")

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()

LanceDB cross-modal text→image search

import lancedb, 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]

db = lancedb.connect("hf://datasets/lance-format/textvqa-lance/data")
tbl = db.open_table("validation")

results = (
    tbl.search(q.tolist(), vector_column_name="image_emb")
    .metric("cosine")
    .select(["question", "answer", "ocr_tokens"])
    .limit(10)
    .to_list()
)

LanceDB full-text search

import lancedb

db = lancedb.connect("hf://datasets/lance-format/textvqa-lance/data")
tbl = db.open_table("validation")

results = (
    tbl.search("brand name")
    .select(["question", "answer"])
    .limit(10)
    .to_list()
)

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}
}