139 lines
3.2 KiB
Markdown
139 lines
3.2 KiB
Markdown
# VQAv2
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## Overview
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VQAv2 is the balanced Visual Question Answering benchmark built on COCO images. It evaluates whether
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multimodal models can answer open-ended natural-language questions grounded in image content.
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## Task Description
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- **Task Type**: Open-ended visual question answering
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- **Input**: Image + natural-language question
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- **Output**: Short answer phrase
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- **Domains**: General image understanding, object recognition, counting, attributes, relations
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## Evaluation Notes
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- Default data source: `lmms-lab/VQAv2` on ModelScope, `validation` split
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- Primary metric: **VQAv2 soft accuracy** over human annotator answers
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- Also reports normalized exact match against the available answer set
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- The adapter accepts common answer formats: list of strings, list of answer dicts, or `multiple_choice_answer`
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `vqav2` |
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| **Dataset ID** | [lmms-lab/VQAv2](https://modelscope.cn/datasets/lmms-lab/VQAv2/summary) |
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| **Paper** | [Paper](https://arxiv.org/abs/1612.00837) |
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| **Tags** | `MultiModal`, `QA` |
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| **Metrics** | `vqa_score`, `exact_match` |
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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 | 214,354 |
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| Prompt Length (Mean) | 185.83 chars |
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| Prompt Length (Min/Max) | 165 / 255 chars |
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**Image Statistics:**
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| Metric | Value |
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|--------|-------|
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| Total Images | 214,354 |
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| Images per Sample | min: 1, max: 1, mean: 1 |
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| Resolution Range | 120x120 - 640x640 |
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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": "7410f0b5",
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"content": [
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{
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"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)."
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},
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{
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"image": "[BASE64_IMAGE: jpeg, ~102.7KB]"
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}
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]
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}
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],
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"target": "[\"down\", \"down\", \"at table\", \"skateboard\", \"down\", \"table\", \"down\", \"down\", \"down\", \"down\"]",
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"id": 0,
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"group_id": 0,
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"metadata": {
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"question": "Where is he looking?",
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"answers": [
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"down",
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"down",
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"at table",
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"skateboard",
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"down",
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"table",
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"down",
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"down",
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"down",
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"down"
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],
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"multiple_choice_answer": "down",
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"question_id": 262148000,
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"question_type": "none of the above",
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"answer_type": "other"
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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 question according to the image using a short 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).
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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 vqav2 \
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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=['vqav2'],
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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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