97 lines
2.3 KiB
Markdown
97 lines
2.3 KiB
Markdown
# General-VMCQ
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## Overview
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General-VMCQ is a customizable visual multiple-choice question answering benchmark for multimodal models.
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It uses MMMU-style format with image/video placeholders in text, supporting flexible media inputs.
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## Task Description
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- **Task Type**: Visual Multiple-Choice Question Answering
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- **Input**: Question with `<image N>`/`<video N>` placeholders + choice options + media
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- **Output**: Selected answer choice
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- **Flexibility**: Supports custom datasets via local files
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## Key Features
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- MMMU-style format (not OpenAI message format)
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- Supports up to 100 images and 100 videos per sample
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- Flexible image/video input (path, URL, or base64 data URL)
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- Chain-of-thought prompt template option
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- Custom dataset support via local file loading
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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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- Train split: **dev**, Eval split: **val**
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- Images/videos are plain strings (do not wrap in `{{"url": ...}}`)
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- See [User Guide](https://evalscope.readthedocs.io/en/latest/advanced_guides/custom_dataset/vlm.html) for dataset format
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `general_vmcq` |
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| **Dataset ID** | `general_vmcq` |
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| **Paper** | N/A |
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| **Tags** | `Custom`, `MCQ`, `MultiModal` |
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| **Metrics** | `acc` |
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| **Default Shots** | 0-shot |
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| **Evaluation Split** | `val` |
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| **Train Split** | `dev` |
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## Data Statistics
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*Statistics not available.*
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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 general_vmcq \
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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=['general_vmcq'],
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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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