VQAv2

Overview

VQAv2 is the balanced Visual Question Answering benchmark built on COCO images. It evaluates whether multimodal models can answer open-ended natural-language questions grounded in image content.

Task Description

Task Type: Open-ended visual question answering
Input: Image + natural-language question
Output: Short answer phrase
Domains: General image understanding, object recognition, counting, attributes, relations

Evaluation Notes

Default data source: lmms-lab/VQAv2 on ModelScope, validation split
Primary metric: VQAv2 soft accuracy over human annotator answers
Also reports normalized exact match against the available answer set
The adapter accepts common answer formats: list of strings, list of answer dicts, or multiple_choice_answer

Properties

Property	Value
Benchmark Name	`vqav2`
Dataset ID	lmms-lab/VQAv2
Paper	Paper
Tags	`MultiModal`, `QA`
Metrics	`vqa_score`, `exact_match`
Default Shots	0-shot
Evaluation Split	`validation`

Data Statistics

Metric	Value
Total Samples	214,354
Prompt Length (Mean)	185.83 chars
Prompt Length (Min/Max)	165 / 255 chars

Image Statistics:

Metric	Value
Total Images	214,354
Images per Sample	min: 1, max: 1, mean: 1
Resolution Range	120x120 - 640x640
Formats	jpeg, png

Sample Example

Subset: default

{
  "input": [
    {
      "id": "7410f0b5",
      "content": [
        {
          "text": "Answer the question according to the image using a short phrase.\nWhere is he looking?\nThe last line of your response should be of the form \"ANSWER: [ANSWER]\" (without quotes)."
        },
        {
          "image": "[BASE64_IMAGE: jpeg, ~102.7KB]"
        }
      ]
    }
  ],
  "target": "[\"down\", \"down\", \"at table\", \"skateboard\", \"down\", \"table\", \"down\", \"down\", \"down\", \"down\"]",
  "id": 0,
  "group_id": 0,
  "metadata": {
    "question": "Where is he looking?",
    "answers": [
      "down",
      "down",
      "at table",
      "skateboard",
      "down",
      "table",
      "down",
      "down",
      "down",
      "down"
    ],
    "multiple_choice_answer": "down",
    "question_id": 262148000,
    "question_type": "none of the above",
    "answer_type": "other"
  }
}

Prompt Template

Prompt Template:

Answer the question according to the image using a short phrase.
{question}
The last line of your response should be of the form "ANSWER: [ANSWER]" (without quotes).

Usage

Using CLI

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets vqav2 \
    --limit 10  # Remove this line for formal evaluation

Using Python

from evalscope import run_task
from evalscope.config import TaskConfig

task_cfg = TaskConfig(
    model='YOUR_MODEL',
    api_url='OPENAI_API_COMPAT_URL',
    api_key='EMPTY_TOKEN',
    datasets=['vqav2'],
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)

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