13274243a0
Keep K3 suite selection and report-schema scoring in bash, merge K3/vision dataset_args into dpv4 yamls, and pin EvalScope at 735d920ee911 with local patches. Co-authored-by: Cursor <cursoragent@cursor.com>
120 lines
3.6 KiB
Markdown
120 lines
3.6 KiB
Markdown
# MVBench
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## Overview
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MVBench is a public multimodal video understanding benchmark covering temporal perception,
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attribute/state reasoning, symbolic ordering, and high-level cognition. This native adapter uses
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the ModelScope `PKU-Alignment/MVBench` mirror by default, which provides JSON annotations plus
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optimized video archives.
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## Task Description
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- **Task Type**: Video multiple-choice question answering
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- **Input**: Video + question + answer choices
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- **Output**: Single correct answer letter
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- **Subsets**: 20 MVBench tasks; the default smoke-test subset is `action_antonym`
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## Evaluation Notes
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- Default configuration uses **0-shot** evaluation
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- Primary metric: **Accuracy**
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- The default `action_antonym` subset downloads a small public MP4 archive for quick validation
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- Full benchmark evaluation can be requested by setting `subset_list` to additional MVBench subsets
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- Time-bounded records keep start/end metadata and add a short segment instruction to the prompt
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `mvbench` |
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| **Dataset ID** | [PKU-Alignment/MVBench](https://modelscope.cn/datasets/PKU-Alignment/MVBench/summary) |
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| **Paper** | [Paper](https://arxiv.org/abs/2311.17005) |
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| **Tags** | `MCQ`, `MultiModal`, `Video` |
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| **Metrics** | `accuracy` |
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| **Default Shots** | 0-shot |
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| **Evaluation Split** | `train` |
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## Data Statistics
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| Metric | Value |
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|--------|-------|
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| Total Samples | 4,000 |
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**Per-Subset Statistics:**
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| Subset | Samples | Prompt Mean | Prompt Min | Prompt Max |
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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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## Sample Example
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*Sample example not available.*
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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 mvbench \
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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=['mvbench'],
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dataset_args={
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'mvbench': {
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# subset_list: ['action_antonym', 'action_count', 'action_localization'] # optional, evaluate specific subsets
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}
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},
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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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