General-VQA#

Overview#

General-VQA is a customizable visual question answering benchmark for evaluating multimodal models. It supports OpenAI-compatible message format with flexible image input (local paths, URLs, or base64).

Task Description#

  • Task Type: Visual Question Answering

  • Input: Images + questions in OpenAI chat format

  • Output: Free-form text answer

  • Flexibility: Supports custom datasets via TSV/JSONL files

Key Features#

  • OpenAI-compatible message format

  • Supports multiple image input methods (path, URL, base64)

  • Flexible evaluation with BLEU and Rouge metrics

  • Custom dataset support via local file loading

  • Extensible for various VQA use cases

Evaluation Notes#

  • Default configuration uses 0-shot evaluation

  • Default metrics: BLEU, Rouge (Rouge-L-R as main score)

  • Evaluates on test split

  • See User Guide for dataset format

Properties#

Property

Value

Benchmark Name

general_vqa

Dataset ID

general_vqa

Paper

N/A

Tags

Custom, MultiModal, QA

Metrics

BLEU, Rouge

Default Shots

0-shot

Evaluation Split

test

Data Statistics#

Statistics not available.

Sample Example#

Sample example not available.

Prompt Template#

No prompt template defined.

Usage#

Using CLI#

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets general_vqa \
    --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=['general_vqa'],
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)