134 lines
4.0 KiB
Markdown
134 lines
4.0 KiB
Markdown
# InfoVQA
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## 概述
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InfoVQA（信息图视觉问答）是一个用于评估 AI 模型基于信息密集型图像（如图表、图形、示意图、地图和信息图）回答问题能力的基准测试。该基准侧重于理解复杂的视觉信息呈现方式。
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## 任务描述
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- **任务类型**：信息图问答
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- **输入**：信息图图像 + 自然语言问题
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- **输出**：单个单词或短语形式的答案
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- **领域**：数据可视化、信息图形、视觉推理
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## 主要特点
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- 聚焦于信息密集型视觉内容
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- 涵盖图表、图形、示意图、地图和信息图
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- 需要理解视觉布局和数据表示方式
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- 测试信息提取与推理能力
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- 问题复杂度从直接查找到推理不等
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## 评估说明
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- 默认使用 **验证集**（validation split）进行评估
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- 主要指标：**ANLS**（Average Normalized Levenshtein Similarity，平均归一化编辑距离相似度）
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- 答案格式应为 `"ANSWER: [ANSWER]"`
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- 包含 OCR 文本提取结果作为元数据供分析使用
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- 使用与 DocVQA 相同的数据源（InfographicVQA 子集）
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## 属性
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| 属性 | 值 |
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|----------|-------|
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| **基准测试名称** | `infovqa` |
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| **数据集 ID** | [lmms-lab/DocVQA](https://modelscope.cn/datasets/lmms-lab/DocVQA/summary) |
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| **论文** | N/A |
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| **标签** | `Knowledge`, `MultiModal`, `QA` |
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| **指标** | `anls` |
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| **默认示例数量** | 0-shot |
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| **评估划分** | `validation` |
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## 数据统计
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| 指标 | 值 |
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|--------|-------|
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| 总样本数 | 2,801 |
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| 提示词长度（平均） | 273.38 字符 |
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| 提示词长度（最小/最大） | 222 / 390 字符 |
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**图像统计：**
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| 指标 | 值 |
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|--------|-------|
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| 图像总数 | 2,801 |
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| 每样本图像数 | 最小: 1, 最大: 1, 平均: 1 |
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| 分辨率范围 | 600x340 - 6250x9375 |
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| 格式 | jpeg |
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## 样例示例
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**子集**：`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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*注：部分内容因显示需要已被截断。*
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## 提示模板
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**提示模板：**
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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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## 使用方法
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### 使用 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 # 正式评估时请删除此行
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```
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### 使用 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, # 正式评估时请删除此行
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)
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run_task(task_cfg=task_cfg)
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``` |