138 lines
4.0 KiB
Markdown
138 lines
4.0 KiB
Markdown
# InfoVQA
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## Overview
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InfoVQA (Infographic Visual Question Answering) is a benchmark designed to evaluate AI models' ability to answer questions based on information-dense images such as charts, graphs, diagrams, maps, and infographics. It focuses on understanding complex visual information presentations.
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## Task Description
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- **Task Type**: Infographic Question Answering
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- **Input**: Infographic image + natural language question
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- **Output**: Single word or phrase answer
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- **Domains**: Data visualization, information graphics, visual reasoning
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## Key Features
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- Focuses on information-dense visual content
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- Covers charts, graphs, diagrams, maps, and infographics
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- Requires understanding visual layouts and data representations
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- Tests information extraction and reasoning abilities
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- Questions vary in complexity from direct lookup to inference
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## Evaluation Notes
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- Default evaluation uses the **validation** split
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- Primary metric: **ANLS** (Average Normalized Levenshtein Similarity)
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- Answers should be in format "ANSWER: [ANSWER]"
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- Includes OCR text extraction as metadata for analysis
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- Uses same dataset source as DocVQA (InfographicVQA subset)
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `infovqa` |
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| **Dataset ID** | [lmms-lab/DocVQA](https://modelscope.cn/datasets/lmms-lab/DocVQA/summary) |
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| **Paper** | N/A |
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| **Tags** | `Knowledge`, `MultiModal`, `QA` |
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| **Metrics** | `anls` |
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| **Default Shots** | 0-shot |
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| **Evaluation Split** | `validation` |
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## Data Statistics
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| Metric | Value |
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|--------|-------|
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| Total Samples | 2,801 |
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| Prompt Length (Mean) | 273.38 chars |
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| Prompt Length (Min/Max) | 222 / 390 chars |
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**Image Statistics:**
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| Metric | Value |
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|--------|-------|
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| Total Images | 2,801 |
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| Images per Sample | min: 1, max: 1, mean: 1 |
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| Resolution Range | 600x340 - 6250x9375 |
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| Formats | jpeg |
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## Sample Example
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**Subset**: `InfographicVQA`
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```json
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{
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"input": [
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{
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"id": "03ab3147",
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"content": [
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{
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"text": "Answer the question according to the image using a single word or phrase.\nWhich social platform has heavy female audience?\nThe last line of your response should be of the form \"ANSWER: [ANSWER]\" (without quotes) where [ANSWER] is the answer to the question."
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},
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{
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"image": "[BASE64_IMAGE: png, ~249.7KB]"
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}
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]
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}
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],
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"target": "[\"pinterest\"]",
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"id": 0,
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"group_id": 0,
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"metadata": {
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"questionId": "98313",
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"answer_type": [
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"single span"
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],
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"image_url": "https://blogs.constantcontact.com/wp-content/uploads/2019/03/Social-Media-Infographic.png",
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"ocr": "['{\"PAGE\": [{\"BlockType\": \"PAGE\", \"Geometry\": {\"BoundingBox\": {\"Width\": 0.9994840025901794, \"Height\": 0.9997748732566833, \"Left\": 0.0, \"Top\": 0.0}, \"Polygon\": [{\"X\": 0.0, \"Y\": 0.0}, {\"X\": 0.9994840025901794, \"Y\": 0.0}, {\"X\": 0.999484002590179 ... [TRUNCATED] ... 184143, \"Y\": 0.9778721332550049}, {\"X\": 0.5701684951782227, \"Y\": 0.9778721332550049}, {\"X\": 0.5701684951782227, \"Y\": 0.9896419048309326}, {\"X\": 0.47732439637184143, \"Y\": 0.9896419048309326}]}, \"Id\": \"43af6e92-c2ef-483c-b947-8b2d2073d756\"}]}']"
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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 according to the image using a single word or phrase.
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{question}
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The last line of your response should be of the form "ANSWER: [ANSWER]" (without quotes) where [ANSWER] is the answer to the 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 infovqa \
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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=['infovqa'],
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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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