124 lines
4.1 KiB
Markdown
124 lines
4.1 KiB
Markdown
# MVBench
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## 概述
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MVBench 是一个公开的多模态视频理解基准测试，涵盖时间感知、属性/状态推理、符号排序和高级认知任务。此原生适配器默认使用 ModelScope 上的 `PKU-Alignment/MVBench` 镜像，该镜像提供 JSON 标注文件及优化后的视频压缩包。
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## 任务描述
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- **任务类型**：视频多项选择题问答（Video multiple-choice question answering）
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- **输入**：视频 + 问题 + 答案选项
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- **输出**：单个正确答案字母
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- **子集**：20 个 MVBench 任务；默认的冒烟测试子集为 `action_antonym`
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## 评估说明
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- 默认配置使用 **0-shot** 评估
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- 主要指标：**准确率（Accuracy）**
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- 默认的 `action_antonym` 子集会下载一个小型公开 MP4 压缩包用于快速验证
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- 可通过设置 `subset_list` 参数指定额外的 MVBench 子集以进行完整基准测试
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- 对于带时间范围的记录，保留起始/结束元数据，并在提示词中添加简短的片段指令
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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** | `mvbench` |
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| **数据集ID** | [PKU-Alignment/MVBench](https://modelscope.cn/datasets/PKU-Alignment/MVBench/summary) |
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| **论文** | [Paper](https://arxiv.org/abs/2311.17005) |
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| **标签** | `MCQ`, `MultiModal` |
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| **指标** | `acc` |
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| **默认示例数** | 0-shot |
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| **评估划分** | `train` |
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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 | 4,000 |
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**各子集统计信息：**
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| 子集 | 样本数 | 提示词平均长度 | 提示词最小长度 | 提示词最大长度 |
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|--------|---------|-------------|------------|------------|
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| `action_antonym` | 200 | N/A | N/A | N/A |
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| `action_count` | 200 | N/A | N/A | N/A |
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| `action_localization` | 200 | N/A | N/A | N/A |
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| `action_prediction` | 200 | N/A | N/A | N/A |
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| `action_sequence` | 200 | N/A | N/A | N/A |
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| `character_order` | 200 | N/A | N/A | N/A |
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| `counterfactual_inference` | 200 | N/A | N/A | N/A |
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| `egocentric_navigation` | 200 | N/A | N/A | N/A |
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| `episodic_reasoning` | 200 | N/A | N/A | N/A |
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| `fine_grained_action` | 200 | N/A | N/A | N/A |
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| `fine_grained_pose` | 200 | N/A | N/A | N/A |
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| `moving_attribute` | 200 | N/A | N/A | N/A |
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| `moving_count` | 200 | N/A | N/A | N/A |
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| `moving_direction` | 200 | N/A | N/A | N/A |
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| `object_existence` | 200 | N/A | N/A | N/A |
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| `object_interaction` | 200 | N/A | N/A | N/A |
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| `object_shuffle` | 200 | N/A | N/A | N/A |
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| `scene_transition` | 200 | N/A | N/A | N/A |
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| `state_change` | 200 | N/A | N/A | N/A |
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| `unexpected_action` | 200 | N/A | N/A | N/A |
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## 样例示例
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*样例示例不可用。*
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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 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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## 额外参数
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| 参数 | 类型 | 默认值 | 描述 |
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|-----------|------|---------|-------------|
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| `dataset_id` | `str` | `PKU-Alignment/MVBench` | MVBench 标注和视频的数据集仓库 ID 或本地数据集根目录。 |
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| `dataset_hub` | `str` | `modelscope` | 用于加载标注和视频压缩包的数据集平台。可选值：['huggingface', 'modelscope', 'local'] |
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| `dataset_revision` | `str` | `` | 可选的数据集版本；留空则使用平台默认版本。 |
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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 mvbench \
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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=['mvbench'],
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dataset_args={
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'mvbench': {
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# subset_list: ['action_antonym', 'action_count', 'action_localization'] # 可选，用于评估特定子集
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# extra_params: {} # 使用默认额外参数
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}
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},
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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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``` |