148 lines
3.4 KiB
Markdown
148 lines
3.4 KiB
Markdown
# POPE
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## Overview
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POPE (Polling-based Object Probing Evaluation) is a benchmark specifically designed to evaluate object hallucination in Large Vision-Language Models (LVLMs). It tests models' ability to accurately identify objects present in images through yes/no questions.
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## Task Description
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- **Task Type**: Object Hallucination Detection (Yes/No Q&A)
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- **Input**: Image with question "Is there a [object] in the image?"
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- **Output**: YES or NO answer
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- **Focus**: Measuring accuracy vs. hallucination rate
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## Key Features
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- Three sampling strategies: random, popular, adversarial
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- Tests for false positive object claims (hallucination)
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- Based on MSCOCO images
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- Simple yes/no question format for objective evaluation
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- Measures alignment between model responses and visual content
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## Evaluation Notes
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- Default configuration uses **0-shot** evaluation
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- Five metrics: accuracy, precision, recall, F1 score, yes_ratio
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- F1 score is the primary aggregation metric
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- Three subsets: `popular`, `adversarial`, `random`
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- "Popular" and "adversarial" subsets are more challenging
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- yes_ratio indicates model's tendency to answer "yes"
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `pope` |
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| **Dataset ID** | [lmms-lab/POPE](https://modelscope.cn/datasets/lmms-lab/POPE/summary) |
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| **Paper** | N/A |
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| **Tags** | `Hallucination`, `MultiModal`, `Yes/No` |
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| **Metrics** | `accuracy`, `precision`, `recall`, `f1_score`, `yes_ratio` |
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| **Default Shots** | 0-shot |
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| **Evaluation Split** | `N/A` |
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| **Aggregation** | `f1` |
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## Data Statistics
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| Metric | Value |
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|--------|-------|
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| Total Samples | 9,000 |
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| Prompt Length (Mean) | 79.4 chars |
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| Prompt Length (Min/Max) | 75 / 87 chars |
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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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| `popular` | 3,000 | 79.27 | 75 | 87 |
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| `adversarial` | 3,000 | 79.36 | 75 | 87 |
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| `random` | 3,000 | 79.59 | 75 | 87 |
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**Image Statistics:**
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| Metric | Value |
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|--------|-------|
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| Total Images | 9,000 |
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| Images per Sample | min: 1, max: 1, mean: 1 |
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| Resolution Range | 500x243 - 640x640 |
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| Formats | jpeg |
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## Sample Example
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**Subset**: `popular`
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```json
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{
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"input": [
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{
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"id": "8847a5a3",
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"content": [
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{
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"text": "Is there a snowboard in the image?\nPlease answer YES or NO without an explanation."
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},
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{
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"image": "[BASE64_IMAGE: png, ~87.2KB]"
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}
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]
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}
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],
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"target": "YES",
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"id": 0,
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"group_id": 0,
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"metadata": {
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"id": "3000",
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"answer": "YES",
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"category": "popular",
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"question_id": "1"
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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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{question}
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Please answer YES or NO without an explanation.
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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 pope \
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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=['pope'],
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dataset_args={
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'pope': {
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# subset_list: ['popular', 'adversarial', 'random'] # 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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