{
  "name": "VQAv2",
  "release_date": "2026-05-26",
  "subsets": {
    "main": {
      "language": [
        "en"
      ],
      "modalities": [
        "single_image_start"
      ],
      "task_type": "short_answer_qa",
      "score_pipeline": [
        "vqa-accuracy"
      ],
      "score_protocol": {
        "reference": "lmms-eval lmms_eval/tasks/vqav2/utils.py:15-50 (vqav2_process_results) — official VQA accuracy: EvalAIAnswerProcessor punctuation/digit-article normalization on response and each reference, then per-annotator acc=min(1, #matches-among-other-9/3), averaged over the 10-answer list (lines 31-42).",
        "note": "Per-sample scores are fractional (multiples of 1/3 capped at 1, averaged over 10 leave-one-out folds). `answer` carries the full 10-reference list from the source `answers` column (annotator dicts {answer, answer_confidence, answer_id} reduced to their `answer` strings, order preserved; answer_confidence/answer_id dropped). The majority-vote `multiple_choice_answer` is preserved as the pass-through field `mc_answer`."
      },
      "prompt_template": "<image>{{ question }}\nAnswer the question using a single word or phrase.",
      "prompt_template_source": {
        "origin": "official",
        "reference": "https://github.com/EvolvingLMMs-Lab/lmms-eval/blob/main/lmms_eval/tasks/vqav2/utils.py (vqav2_doc_to_text — canonical short-answer)",
        "notes": "Tier 4: lmms-eval VQAv2 canonical evaluation prompt."
      },
      "mapping_from_source": {
        "media": {
          "from": "image",
          "type": "list",
          "min_items": 1,
          "max_items": 1
        },
        "id": {
          "from": "question_id"
        },
        "question": {
          "from": "question"
        },
        "answer": {
          "from": "answers",
          "optional": true,
          "note": "Source `answers` is a list of 10 {answer, answer_confidence, answer_id} dicts; reduced to the list of 10 `answer` strings (order preserved) during pre-processing."
        },
        "extra": {
          "mc_answer": {
            "from": "multiple_choice_answer"
          },
          "image_id": {
            "from": "image_id"
          },
          "question_type": {
            "from": "question_type"
          },
          "answer_type": {
            "from": "answer_type"
          }
        },
        "source": {
          "format": "huggingface",
          "url": {
            "validation": "https://huggingface.co/datasets/lmms-lab/VQAv2"
          }
        }
      }
    }
  }
}