145 lines
3.9 KiB
Markdown
145 lines
3.9 KiB
Markdown
# A-OKVQA
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## Overview
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A-OKVQA (Augmented OK-VQA) is a benchmark designed to evaluate commonsense reasoning and external world knowledge in visual question answering. It extends beyond basic VQA tasks that rely solely on image content, requiring models to leverage a broad spectrum of commonsense and factual knowledge about the world.
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## Task Description
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- **Task Type**: Visual Question Answering with Knowledge Reasoning
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- **Input**: Image + natural language question requiring external knowledge
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- **Output**: Answer (multiple-choice or open-ended)
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- **Domains**: Commonsense reasoning, factual knowledge, visual understanding
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## Key Features
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- Requires commonsense reasoning beyond direct visual observation
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- Combines visual understanding with external world knowledge
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- Includes both multiple-choice and open-ended question formats
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- Questions annotated with rationales explaining the reasoning process
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- More challenging than standard VQA benchmarks
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## Evaluation Notes
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- Default evaluation uses the **validation** split
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- Primary metric: **Accuracy** for multiple-choice questions
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- Uses Chain-of-Thought (CoT) prompting for better reasoning
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- Questions require reasoning beyond what is directly visible in images
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `a_okvqa` |
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| **Dataset ID** | [HuggingFaceM4/A-OKVQA](https://modelscope.cn/datasets/HuggingFaceM4/A-OKVQA/summary) |
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| **Paper** | N/A |
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| **Tags** | `Knowledge`, `MCQ`, `MultiModal` |
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| **Metrics** | `acc` |
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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 | 1,145 |
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| Prompt Length (Mean) | 310.84 chars |
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| Prompt Length (Min/Max) | 276 / 405 chars |
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**Image Statistics:**
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| Metric | Value |
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|--------|-------|
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| Total Images | 1,145 |
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| Images per Sample | min: 1, max: 1, mean: 1 |
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| Resolution Range | 305x229 - 640x640 |
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| Formats | jpeg |
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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": "3d2b0351",
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"content": [
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{
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"text": "Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of A,B,C,D. Think step by step before answering.\n\nWhat is in the motorcyclist's mouth?\n\nA) toothpick\nB) food\nC) popsicle stick\nD) cigarette"
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},
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{
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"image": "[BASE64_IMAGE: jpeg, ~53.4KB]"
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}
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]
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}
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],
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"choices": [
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"toothpick",
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"food",
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"popsicle stick",
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"cigarette"
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],
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"target": "D",
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"id": 0,
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"group_id": 0,
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"metadata": {
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"question_id": "22jbM6gDxdaMaunuzgrsBB",
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"direct_answers": "['cigarette', 'cigarette', 'cigarette', 'cigarette', 'cigarette', 'cigarette', 'cigarette', 'cigarette', 'cigarette', 'cigarette']",
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"difficult_direct_answer": false,
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"rationales": [
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"He's smoking while riding.",
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"The motorcyclist has a lit cigarette in his mouth while he rides on the street.",
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"The man is smoking."
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]
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}
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}
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```
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## Prompt Template
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**Prompt Template:**
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```text
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Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of {letters}. Think step by step before answering.
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{question}
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{choices}
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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 a_okvqa \
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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=['a_okvqa'],
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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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