| --- |
| license: other |
| license_name: openxvqa-research-only |
| license_link: https://arxiv.org/abs/2310.08864 |
| task_categories: |
| - visual-question-answering |
| - multiple-choice |
| language: |
| - en |
| size_categories: |
| - 1K<n<10K |
| pretty_name: OpenXVQA |
| configs: |
| - config_name: default |
| data_files: |
| - split: test |
| path: data/test-*.parquet |
| --- |
| |
| # OpenXVQA |
|
|
| VQA benchmark derived from [Open X-Embodiment](https://arxiv.org/abs/2310.08864) |
| robot manipulation data. Tests visual understanding of diverse robotic scenes |
| and actions. |
|
|
| ## Format |
|
|
| Each row contains: |
|
|
| ```json |
| { |
| "id": "<unique id>", |
| "question": "<question text>", |
| "choices": "['option1', 'option2', ...]", |
| "correct_answer": <int index>, |
| "image": <PNG bytes> |
| } |
| ``` |
|
|
| `choices` is a Python-literal string of a list of answer options. |
| `correct_answer` is the integer index into `choices` of the correct option. |
| `image` is embedded as binary in the parquet (HF `Image()` decodes on load). |
|
|
| ## Citation |
|
|
| ``` |
| @article{padalkar2023open, |
| title={Open X-Embodiment: Robotic Learning Datasets and RT-X Models}, |
| author={Padalkar, Abhishek and ...}, |
| journal={arXiv:2310.08864}, |
| year={2023} |
| } |
| ``` |
|
|
| ## License |
|
|
| Annotations are derived from Open X-Embodiment, which is openly published. |
| The MCQ formatting here is a research-only artifact. |
|
|
