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