114 lines
2.4 KiB
Markdown
114 lines
2.4 KiB
Markdown
# HPD-v2
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## Overview
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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.
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## Task Description
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- **Task Type**: Text-to-Image Generation Evaluation
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- **Input**: Text prompt for image generation
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- **Output**: Generated image evaluated against human preferences
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- **Metric**: HPSv2.1 Score
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## Key Features
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- Human preference-aligned evaluation metric
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- Trained on large-scale human preference data
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- Tests aesthetic quality and prompt alignment
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- Supports diverse prompt categories
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- Objective, reproducible scoring
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## Evaluation Notes
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- Default configuration uses **0-shot** evaluation
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- HPSv2.1 Score metric measures human preference alignment
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- Supports local prompt files
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- Category tags available in metadata
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- Can evaluate existing images or generate new ones
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `hpdv2` |
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| **Dataset ID** | [AI-ModelScope/T2V-Eval-Prompts](https://modelscope.cn/datasets/AI-ModelScope/T2V-Eval-Prompts/summary) |
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| **Paper** | N/A |
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| **Tags** | `TextToImage` |
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| **Metrics** | `HPSv2.1Score` |
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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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| Metric | Value |
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|--------|-------|
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| Total Samples | 3,200 |
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| Prompt Length (Mean) | 81.71 chars |
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| Prompt Length (Min/Max) | 9 / 404 chars |
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## Sample Example
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**Subset**: `HPDv2`
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```json
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{
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"input": [
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{
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"id": "c971b3c7",
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"content": "Spongebob depicted in the style of Dragon Ball Z."
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}
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],
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"id": 0,
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"group_id": 0,
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"metadata": {
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"id": "HPDv2_0",
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"prompt": "Spongebob depicted in the style of Dragon Ball Z.",
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"category": "Animation",
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"tags": {
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"category": "Animation"
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},
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"image_path": ""
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}
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}
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```
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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 hpdv2 \
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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=['hpdv2'],
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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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