139 lines
3.5 KiB
Markdown
139 lines
3.5 KiB
Markdown
# SimpleVQA
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## Overview
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SimpleVQA is the first comprehensive multimodal benchmark to evaluate the factuality ability of MLLMs to answer natural language short questions. It features high-quality, challenging queries with static and timeless reference answers.
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## Task Description
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- **Task Type**: Factual Visual Question Answering
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- **Input**: Image + factual question
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- **Output**: Short factual answer
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- **Domains**: Factuality, visual reasoning, knowledge recall
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## Key Features
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- Covers multiple tasks and scenarios
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- High-quality, challenging questions
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- Static and timeless reference answers (no temporal dependencies)
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- Straightforward evaluation methodology
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- Tests genuine factual knowledge beyond pattern matching
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## Evaluation Notes
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- Default evaluation uses the **test** split
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- Primary metric: **Accuracy** with LLM judge
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- Three-grade evaluation: CORRECT, INCORRECT, NOT_ATTEMPTED
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- LLM judge uses detailed grading rubric for semantic matching
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- Rich metadata includes language, source, and atomic facts
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `simple_vqa` |
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| **Dataset ID** | [m-a-p/SimpleVQA](https://modelscope.cn/datasets/m-a-p/SimpleVQA/summary) |
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| **Paper** | N/A |
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| **Tags** | `MultiModal`, `QA`, `Reasoning` |
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| **Metrics** | `acc` |
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| **Default Shots** | 0-shot |
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| **Evaluation Split** | `test` |
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## Data Statistics
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| Metric | Value |
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|--------|-------|
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| Total Samples | 2,025 |
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| Prompt Length (Mean) | 56.22 chars |
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| Prompt Length (Min/Max) | 27 / 1015 chars |
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**Image Statistics:**
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| Metric | Value |
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|--------|-------|
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| Total Images | 2,025 |
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| Images per Sample | min: 1, max: 1, mean: 1 |
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| Resolution Range | 106x56 - 5119x3413 |
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| Formats | jpeg, png |
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## Sample Example
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**Subset**: `default`
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```json
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{
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"input": [
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{
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"id": "4340fc24",
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"content": [
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{
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"text": "Answer the question:\n\n图中所示穴位所属的经脉是什么？"
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},
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{
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"image": "[BASE64_IMAGE: jpeg, ~26.5KB]"
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}
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]
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}
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],
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"target": "足阳明胃经",
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"id": 0,
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"group_id": 0,
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"metadata": {
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"data_id": 0,
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"image_description": "",
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"language": "CN",
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"original_category": "中华文化_中医",
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"source": "https://baike.baidu.com/item/%E4%BC%8F%E5%85%94%E7%A9%B4/3503684#:~:text\\u003d%E4%BA%BA%E4%BD%93%E7%A9%B4%E4%BD%8D%E5%90%8D%E4%BC%8F%E5%85%94%E7%A9%B4F%C3%BA%20t%C3%B9%EF%BC%88ST32%EF%BC%89%E5%B1%9E%E8%B6%B3%E9%98%B3%E6%98%8E%E8%83%83%E7%BB%8 ... [TRUNCATED] ... 4%BE%A7%E7%AB%AF%E7%9A%84%E8%BF%9E%E7%BA%BF%E4%B8%8A%EF%BC%8C%E9%AB%8C%E9%AA%A8%E4%B8%8A%E7%BC%98%E4%B8%8A6%E5%AF%B8%E3%80%82%E4%BC%8F%E5%85%94%E5%88%AB%E5%90%8D%E5%A4%96%E4%B8%98%E3%80%81%E5%A4%96%E5%8B%BE%EF%BC%8C%E4%BD%8D%E4%BA%8E%E5%A4%A7",
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"atomic_question": "图中所示穴位的名称是什么？",
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"atomic_fact": "伏兔"
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}
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}
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```
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*Note: Some content was truncated for display.*
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## Prompt Template
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**Prompt Template:**
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```text
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Answer the question:
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{question}
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```
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## Usage
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### Using CLI
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```bash
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evalscope eval \
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--model YOUR_MODEL \
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--api-url OPENAI_API_COMPAT_URL \
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--api-key EMPTY_TOKEN \
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--datasets simple_vqa \
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--limit 10 # Remove this line for formal evaluation
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```
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### Using Python
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```python
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from evalscope import run_task
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from evalscope.config import TaskConfig
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task_cfg = TaskConfig(
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model='YOUR_MODEL',
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api_url='OPENAI_API_COMPAT_URL',
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api_key='EMPTY_TOKEN',
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datasets=['simple_vqa'],
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limit=10, # Remove this line for formal evaluation
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)
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run_task(task_cfg=task_cfg)
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```
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