2026-07-08 08:57:50 +00:00

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# HPD-v2
## Overview
HPD-v2 (Human Preference Dataset v2) is a text-to-image benchmark that evaluates generated images based on human preferences. It uses the HPSv2.1 score metric trained on large-scale human preference data.
## Task Description
- **Task Type**: Text-to-Image Generation Evaluation
- **Input**: Text prompt for image generation
- **Output**: Generated image evaluated against human preferences
- **Metric**: HPSv2.1 Score
## Key Features
- Human preference-aligned evaluation metric
- Trained on large-scale human preference data
- Tests aesthetic quality and prompt alignment
- Supports diverse prompt categories
- Objective, reproducible scoring
## Evaluation Notes
- Default configuration uses **0-shot** evaluation
- HPSv2.1 Score metric measures human preference alignment
- Supports local prompt files
- Category tags available in metadata
- Can evaluate existing images or generate new ones
## Properties
| Property | Value |
|----------|-------|
| **Benchmark Name** | `hpdv2` |
| **Dataset ID** | [AI-ModelScope/T2V-Eval-Prompts](https://modelscope.cn/datasets/AI-ModelScope/T2V-Eval-Prompts/summary) |
| **Paper** | N/A |
| **Tags** | `TextToImage` |
| **Metrics** | `HPSv2.1Score` |
| **Default Shots** | 0-shot |
| **Evaluation Split** | `test` |
## Data Statistics
| Metric | Value |
|--------|-------|
| Total Samples | 3,200 |
| Prompt Length (Mean) | 81.71 chars |
| Prompt Length (Min/Max) | 9 / 404 chars |
## Sample Example
**Subset**: `HPDv2`
```json
{
"input": [
{
"id": "c971b3c7",
"content": "Spongebob depicted in the style of Dragon Ball Z."
}
],
"id": 0,
"group_id": 0,
"metadata": {
"id": "HPDv2_0",
"prompt": "Spongebob depicted in the style of Dragon Ball Z.",
"category": "Animation",
"tags": {
"category": "Animation"
},
"image_path": ""
}
}
```
## Prompt Template
*No prompt template defined.*
## Usage
### Using CLI
```bash
evalscope eval \
--model YOUR_MODEL \
--api-url OPENAI_API_COMPAT_URL \
--api-key EMPTY_TOKEN \
--datasets hpdv2 \
--limit 10 # Remove this line for formal evaluation
```
### Using Python
```python
from evalscope import run_task
from evalscope.config import TaskConfig
task_cfg = TaskConfig(
model='YOUR_MODEL',
api_url='OPENAI_API_COMPAT_URL',
api_key='EMPTY_TOKEN',
datasets=['hpdv2'],
limit=10, # Remove this line for formal evaluation
)
run_task(task_cfg=task_cfg)
```