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>
87 lines
2.2 KiB
Markdown
87 lines
2.2 KiB
Markdown
# General-VQA
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## Overview
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General-VQA is a customizable visual question answering benchmark for evaluating multimodal models.
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It supports OpenAI-compatible message format with flexible image/video/audio input (local paths, URLs, or base64).
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## Task Description
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- **Task Type**: Visual Question Answering
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- **Input**: Images/videos/audio + questions in OpenAI chat format
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- **Output**: Free-form text answer
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- **Flexibility**: Supports custom datasets via TSV/JSONL files
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## Key Features
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- OpenAI-compatible message format
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- Supports multiple image/video/audio input methods (path, URL, base64)
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- **Media placeholders**: Use ``<image N>`` / ``<video N>`` / ``<audio N>`` in plain-text user messages with indexed media columns (``image_1``, ``video_1``, ``audio_1``, etc.) for a simpler data format
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- Flexible evaluation with BLEU and Rouge metrics
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- Custom dataset support via local file loading
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- Extensible for various VQA use cases
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## Evaluation Notes
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- Default configuration uses **0-shot** evaluation
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- Default metrics: **BLEU**, **Rouge** (Rouge-L-R as main score)
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- Evaluates on **test** split
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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_vqa` |
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| **Dataset ID** | `general_vqa` |
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| **Paper** | N/A |
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| **Tags** | `Custom`, `MultiModal`, `QA` |
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| **Metrics** | `BLEU`, `Rouge` |
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| **Default Shots** | 0-shot |
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| **Evaluation Split** | `test` |
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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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*No prompt template defined.*
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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_vqa \
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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_vqa'],
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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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