Datasets:
Add FVQA, InfoSeek and EncyclopedicVQA evaluation splits (full + _mini)
Adds the three multimodal search benchmarks (FVQA, InfoSeek, EncyclopedicVQA), each as a full split and a _mini split (300 questions, pandas.DataFrame.sample(n=300, random_state=42) of the full split; _mini is the default evaluation split).
| Split | Rows | Image archive |
|---|---|---|
| FVQA_test | 1800 | images/FVQA.zip |
| FVQA_test_mini | 300 | images/FVQA.zip |
| Infoseek_GDM_test | 2000 | images/Infoseek_GDM.zip |
| Infoseek_GDM_test_mini | 300 | images/Infoseek_GDM.zip |
| EncyclopedicVQA_test | 1960 | images/EncyclopedicVQA.zip |
| EncyclopedicVQA_test_mini | 300 | images/EncyclopedicVQA.zip |
Same layout as the existing splits: image_path is relative to images/<folder>/ inside images/<folder>.zip (stored, one top-level folder). All other TSV columns are byte-identical to the evaluation TSVs used in our runs, and every image was checked against its source file by sha256. README changes are additions only (configs, table rows, a note on _mini, and source/citation entries).
Note: the README title/pretty_name still reads "counting & grounding benchmarks"; left unchanged for the maintainers to decide.