{
  "meta": {
    "pretty_name": "MVBench",
    "dataset_id": "PKU-Alignment/MVBench",
    "paper_url": "https://arxiv.org/abs/2311.17005",
    "tags": [
      "MultiModal",
      "MCQ"
    ],
    "metrics": [
      "acc"
    ],
    "few_shot_num": 0,
    "eval_split": "train",
    "train_split": "",
    "subset_list": [
      "action_antonym"
    ],
    "description": "\n## Overview\n\nMVBench is a public multimodal video understanding benchmark covering temporal perception,\nattribute/state reasoning, symbolic ordering, and high-level cognition. This native adapter uses\nthe ModelScope `PKU-Alignment/MVBench` mirror by default, which provides JSON annotations plus\noptimized video archives.\n\n## Task Description\n\n- **Task Type**: Video multiple-choice question answering\n- **Input**: Video + question + answer choices\n- **Output**: Single correct answer letter\n- **Subsets**: 20 MVBench tasks; the default smoke-test subset is `action_antonym`\n\n## Evaluation Notes\n\n- Default configuration uses **0-shot** evaluation\n- Primary metric: **Accuracy**\n- The default `action_antonym` subset downloads a small public MP4 archive for quick validation\n- Full benchmark evaluation can be requested by setting `subset_list` to additional MVBench subsets\n- Time-bounded records keep start/end metadata and add a short segment instruction to the prompt\n",
    "prompt_template": "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.\n\n{question}\n\n{choices}",
    "system_prompt": "",
    "few_shot_prompt_template": "",
    "aggregation": "mean",
    "extra_params": {
      "dataset_id": {
        "type": "str",
        "description": "Dataset repository ID or local dataset root for MVBench annotations and videos.",
        "value": "PKU-Alignment/MVBench"
      },
      "dataset_hub": {
        "type": "str",
        "description": "Dataset hub used to load annotations and video archives.",
        "value": "modelscope",
        "choices": [
          "huggingface",
          "modelscope",
          "local"
        ]
      },
      "dataset_revision": {
        "type": "str",
        "description": "Optional dataset revision; leave empty to use the hub default.",
        "value": ""
      }
    },
    "sandbox_config": {},
    "category": "vlm"
  },
  "statistics": {
    "total_samples": 4000,
    "subset_stats": [
      {
        "name": "action_antonym",
        "sample_count": 200
      },
      {
        "name": "action_count",
        "sample_count": 200
      },
      {
        "name": "action_localization",
        "sample_count": 200
      },
      {
        "name": "action_prediction",
        "sample_count": 200
      },
      {
        "name": "action_sequence",
        "sample_count": 200
      },
      {
        "name": "character_order",
        "sample_count": 200
      },
      {
        "name": "counterfactual_inference",
        "sample_count": 200
      },
      {
        "name": "egocentric_navigation",
        "sample_count": 200
      },
      {
        "name": "episodic_reasoning",
        "sample_count": 200
      },
      {
        "name": "fine_grained_action",
        "sample_count": 200
      },
      {
        "name": "fine_grained_pose",
        "sample_count": 200
      },
      {
        "name": "moving_attribute",
        "sample_count": 200
      },
      {
        "name": "moving_count",
        "sample_count": 200
      },
      {
        "name": "moving_direction",
        "sample_count": 200
      },
      {
        "name": "object_existence",
        "sample_count": 200
      },
      {
        "name": "object_interaction",
        "sample_count": 200
      },
      {
        "name": "object_shuffle",
        "sample_count": 200
      },
      {
        "name": "scene_transition",
        "sample_count": 200
      },
      {
        "name": "state_change",
        "sample_count": 200
      },
      {
        "name": "unexpected_action",
        "sample_count": 200
      }
    ],
    "computed_at": "2026-05-12T10:36:11.076276"
  },
  "sample_example": {
    "subset": "action_antonym",
    "record": {
      "video": "166583.mp4",
      "question": "What is the action performed by the person in the video?",
      "candidates": [
        "Not sure",
        "Scattering something down",
        "Piling something up"
      ],
      "answer": "Piling something up"
    }
  },
  "readme": {
    "en": "# MVBench\n\n\n## Overview\n\nMVBench is a public multimodal video understanding benchmark covering temporal perception,\nattribute/state reasoning, symbolic ordering, and high-level cognition. This native adapter uses\nthe ModelScope `PKU-Alignment/MVBench` mirror by default, which provides JSON annotations plus\noptimized video archives.\n\n## Task Description\n\n- **Task Type**: Video multiple-choice question answering\n- **Input**: Video + question + answer choices\n- **Output**: Single correct answer letter\n- **Subsets**: 20 MVBench tasks; the default smoke-test subset is `action_antonym`\n\n## Evaluation Notes\n\n- Default configuration uses **0-shot** evaluation\n- Primary metric: **Accuracy**\n- The default `action_antonym` subset downloads a small public MP4 archive for quick validation\n- Full benchmark evaluation can be requested by setting `subset_list` to additional MVBench subsets\n- Time-bounded records keep start/end metadata and add a short segment instruction to the prompt\n\n\n## Properties\n\n| Property | Value |\n|----------|-------|\n| **Benchmark Name** | `mvbench` |\n| **Dataset ID** | [PKU-Alignment/MVBench](https://modelscope.cn/datasets/PKU-Alignment/MVBench/summary) |\n| **Paper** | [Paper](https://arxiv.org/abs/2311.17005) |\n| **Tags** | `MCQ`, `MultiModal` |\n| **Metrics** | `acc` |\n| **Default Shots** | 0-shot |\n| **Evaluation Split** | `train` |\n\n\n## Data Statistics\n\n| Metric | Value |\n|--------|-------|\n| Total Samples | 4,000 |\n\n**Per-Subset Statistics:**\n\n| Subset | Samples | Prompt Mean | Prompt Min | Prompt Max |\n|--------|---------|-------------|------------|------------|\n| `action_antonym` | 200 | N/A | N/A | N/A |\n| `action_count` | 200 | N/A | N/A | N/A |\n| `action_localization` | 200 | N/A | N/A | N/A |\n| `action_prediction` | 200 | N/A | N/A | N/A |\n| `action_sequence` | 200 | N/A | N/A | N/A |\n| `character_order` | 200 | N/A | N/A | N/A |\n| `counterfactual_inference` | 200 | N/A | N/A | N/A |\n| `egocentric_navigation` | 200 | N/A | N/A | N/A |\n| `episodic_reasoning` | 200 | N/A | N/A | N/A |\n| `fine_grained_action` | 200 | N/A | N/A | N/A |\n| `fine_grained_pose` | 200 | N/A | N/A | N/A |\n| `moving_attribute` | 200 | N/A | N/A | N/A |\n| `moving_count` | 200 | N/A | N/A | N/A |\n| `moving_direction` | 200 | N/A | N/A | N/A |\n| `object_existence` | 200 | N/A | N/A | N/A |\n| `object_interaction` | 200 | N/A | N/A | N/A |\n| `object_shuffle` | 200 | N/A | N/A | N/A |\n| `scene_transition` | 200 | N/A | N/A | N/A |\n| `state_change` | 200 | N/A | N/A | N/A |\n| `unexpected_action` | 200 | N/A | N/A | N/A |\n\n## Sample Example\n\n*Sample example not available.*\n\n## Prompt Template\n\n**Prompt Template:**\n```text\nAnswer 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.\n\n{question}\n\n{choices}\n```\n\n## Extra Parameters\n\n| Parameter | Type | Default | Description |\n|-----------|------|---------|-------------|\n| `dataset_id` | `str` | `PKU-Alignment/MVBench` | Dataset repository ID or local dataset root for MVBench annotations and videos. |\n| `dataset_hub` | `str` | `modelscope` | Dataset hub used to load annotations and video archives. Choices: ['huggingface', 'modelscope', 'local'] |\n| `dataset_revision` | `str` | `` | Optional dataset revision; leave empty to use the hub default. |\n\n## Usage\n\n### Using CLI\n\n```bash\nevalscope eval \\\n    --model YOUR_MODEL \\\n    --api-url OPENAI_API_COMPAT_URL \\\n    --api-key EMPTY_TOKEN \\\n    --datasets mvbench \\\n    --limit 10  # Remove this line for formal evaluation\n```\n\n### Using Python\n\n```python\nfrom evalscope import run_task\nfrom evalscope.config import TaskConfig\n\ntask_cfg = TaskConfig(\n    model='YOUR_MODEL',\n    api_url='OPENAI_API_COMPAT_URL',\n    api_key='EMPTY_TOKEN',\n    datasets=['mvbench'],\n    dataset_args={\n        'mvbench': {\n            # subset_list: ['action_antonym', 'action_count', 'action_localization']  # optional, evaluate specific subsets\n            # extra_params: {}  # uses default extra parameters\n        }\n    },\n    limit=10,  # Remove this line for formal evaluation\n)\n\nrun_task(task_cfg=task_cfg)\n```\n\n\n",
    "zh": "# MVBench\n\n\n## 概述\n\nMVBench 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。\n\n## 任务描述\n\n- **任务类型**：视频多项选择题问答（Video multiple-choice question answering）\n- **输入**：视频 + 问题 + 答案选项\n- **输出**：单个正确答案字母\n- **子集**：20 个 MVBench 任务；默认的冒烟测试子集为 `action_antonym`\n\n## 评估说明\n\n- 默认配置使用 **0-shot** 评估\n- 主要指标：**准确率（Accuracy）**\n- 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\n- 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\n- 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\n\n## 属性\n\n| 属性 | 值 |\n|----------|-------|\n| **基准测试名称** | `mvbench` |\n| **数据集ID** | [PKU-Alignment/MVBench](https://modelscope.cn/datasets/PKU-Alignment/MVBench/summary) |\n| **论文** | [Paper](https://arxiv.org/abs/2311.17005) |\n| **标签** | `MCQ`, `MultiModal` |\n| **指标** | `acc` |\n| **默认示例数** | 0-shot |\n| **评估划分** | `train` |\n\n\n## 数据统计\n\n| 指标 | 值 |\n|--------|-------|\n| 总样本数 | 4,000 |\n\n**各子集统计信息：**\n\n| 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 |\n|--------|---------|-------------|------------|------------|\n| `action_antonym` | 200 | N/A | N/A | N/A |\n| `action_count` | 200 | N/A | N/A | N/A |\n| `action_localization` | 200 | N/A | N/A | N/A |\n| `action_prediction` | 200 | N/A | N/A | N/A |\n| `action_sequence` | 200 | N/A | N/A | N/A |\n| `character_order` | 200 | N/A | N/A | N/A |\n| `counterfactual_inference` | 200 | N/A | N/A | N/A |\n| `egocentric_navigation` | 200 | N/A | N/A | N/A |\n| `episodic_reasoning` | 200 | N/A | N/A | N/A |\n| `fine_grained_action` | 200 | N/A | N/A | N/A |\n| `fine_grained_pose` | 200 | N/A | N/A | N/A |\n| `moving_attribute` | 200 | N/A | N/A | N/A |\n| `moving_count` | 200 | N/A | N/A | N/A |\n| `moving_direction` | 200 | N/A | N/A | N/A |\n| `object_existence` | 200 | N/A | N/A | N/A |\n| `object_interaction` | 200 | N/A | N/A | N/A |\n| `object_shuffle` | 200 | N/A | N/A | N/A |\n| `scene_transition` | 200 | N/A | N/A | N/A |\n| `state_change` | 200 | N/A | N/A | N/A |\n| `unexpected_action` | 200 | N/A | N/A | N/A |\n\n## 样例示例\n\n*样例示例不可用。*\n\n## 提示模板\n\n**提示模板：**\n```text\nAnswer 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.\n\n{question}\n\n{choices}\n```\n\n## 额外参数\n\n| 参数 | 类型 | 默认值 | 描述 |\n|-----------|------|---------|-------------|\n| `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。 |\n| `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'] |\n| `dataset_revision` | `str` | `` | 可选的数据集版本；留空则使用平台默认版本。 |\n\n## 使用方法\n\n### 使用 CLI\n\n```bash\nevalscope eval \\\n    --model YOUR_MODEL \\\n    --api-url OPENAI_API_COMPAT_URL \\\n    --api-key EMPTY_TOKEN \\\n    --datasets mvbench \\\n    --limit 10  # 正式评估时请删除此行\n```\n\n### 使用 Python\n\n```python\nfrom evalscope import run_task\nfrom evalscope.config import TaskConfig\n\ntask_cfg = TaskConfig(\n    model='YOUR_MODEL',\n    api_url='OPENAI_API_COMPAT_URL',\n    api_key='EMPTY_TOKEN',\n    datasets=['mvbench'],\n    dataset_args={\n        'mvbench': {\n            # subset_list: ['action_antonym', 'action_count', 'action_localization']  # 可选，用于评估特定子集\n            # extra_params: {}  # 使用默认额外参数\n        }\n    },\n    limit=10,  # 正式评估时请删除此行\n)\n\nrun_task(task_cfg=task_cfg)\n```",
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  "updated_at": "2026-05-18T10:23:17.450629",
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