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>
147 lines
4.3 KiB
Markdown
147 lines
4.3 KiB
Markdown
# PMC-VQA
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## Overview
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PMC-VQA is a large-scale medical visual question answering benchmark built from figures of
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biomedical papers in the PubMed Central Open Access subset. This integration evaluates the
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manually verified **test_clean** split, the 2,000-question subset the authors recommend for
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reporting results.
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## Task Description
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- **Task Type**: Medical Visual Question Answering (single-answer multiple choice)
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- **Input**: A biomedical figure plus a question with four candidate answers
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- **Output**: A single answer letter (A/B/C/D)
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- **Domain**: Medicine and biomedical imaging (radiology, pathology, microscopy, plus charts and diagrams found in papers)
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## Key Features
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- 2,000 questions over 1,440 distinct figures, each with exactly four answer options
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- Questions were generated from figure captions and then manually verified, so test_clean is
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substantially cleaner than the raw 50k test split
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- Covers a wide range of imaging modalities and diseases, as well as non-photographic figures
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such as plots and diagrams
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- Requires reading fine-grained visual detail together with biomedical domain knowledge
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## Evaluation Notes
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- Primary metric: **Accuracy** over the four options
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- Answers are extracted from the `ANSWER: [LETTER]` line requested by the prompt; the original
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paper instead matches free-form generations to the closest option string, which is only needed
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for models that cannot follow an answer format
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- Keep `max_tokens` generous enough for the model to finish its answer line: when no
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`ANSWER:` line is present, the shared multiple-choice parser falls back to the last
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upper-case letter in the reply, so a truncated response may be scored as a lenient guess
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- Images are shipped as a single `images.zip` (about 18 GB) in the dataset repository. It is
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downloaded once and the figures needed for the evaluated samples are read directly from the
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archive, so no extracted copy is kept on disk
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- [Paper](https://arxiv.org/abs/2305.10415) | [GitHub](https://github.com/xiaoman-zhang/PMC-VQA)
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `pmc_vqa` |
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| **Dataset ID** | [evalscope/PMC-VQA](https://modelscope.cn/datasets/evalscope/PMC-VQA/summary) |
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| **Paper** | [Paper](https://arxiv.org/abs/2305.10415) |
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| **Tags** | `MCQ`, `Medical`, `MultiModal` |
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| **Metrics** | `accuracy` |
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| **Default Shots** | 0-shot |
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| **Evaluation Split** | `test_clean` |
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## Data Statistics
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| Metric | Value |
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|--------|-------|
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| Total Samples | 2,000 |
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| Prompt Length (Mean) | 343.61 chars |
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| Prompt Length (Min/Max) | 241 / 1105 chars |
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**Image Statistics:**
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| Metric | Value |
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|--------|-------|
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| Total Images | 2,000 |
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| Images per Sample | min: 1, max: 1, mean: 1 |
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| Resolution Range | 17x21 - 4130x3564 |
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| Formats | jpeg |
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## Sample Example
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**Subset**: `default`
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```json
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{
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"input": [
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{
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"id": "03f4a772",
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"content": [
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{
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"image": "[BASE64_IMAGE: jpeg, ~93.0KB]"
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},
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{
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"text": "Answer the following multiple choice question. The entire content of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of A,B,C,D.\n\nWhat is the name of the medical imaging technique used in this case?\n\nA) X-ray\nB) Magnetic resonance imaging\nC) Computed tomography\nD) Ultrasound"
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}
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]
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}
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],
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"choices": [
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"X-ray",
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"Magnetic resonance imaging",
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"Computed tomography",
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"Ultrasound"
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],
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"target": "B",
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"id": 0,
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"group_id": 0,
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"metadata": {
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"figure_path": "PMC8415802_FIG1.jpg"
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}
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}
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```
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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 entire content of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of {letters}.
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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 pmc_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=['pmc_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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