---
license: mit
task_categories:
  - text-to-image
  - image-segmentation
language:
  - en
  - zh
tags:
  - web-interaction
  - multimodal
  - benchmark
  - llm-evaluation
  - vue.js
  - frontend
pretty_name: MultiInteract-Bench
size_categories:
  - n<1K
---

# MultiInteract-Bench Dataset

<div align="center">

**A Benchmark Dataset for Evaluating Web Interaction Reconstruction from Image Sequences**

[![HuggingFace Dataset](https://img.shields.io/badge/Dataset-HuggingFace-yellow.svg)](https://huggingface.co/datasets/zionzionzion/MultiInteract-Bench)
[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

</div>

## 📋 Overview

MultiInteract-Bench is a comprehensive dataset designed to evaluate the capabilities of multimodal large language models in reproducing web-based interactions from image sequences. The dataset contains real-world web interface snapshots showing progressive states of web applications through user interactions.

### Key Features

- **Multi-turn Interactions**: Each task includes a sequence of web page states showing the progression of user interactions
- **Real-world Applications**: Covers popular web applications like Spotify, Stripe, and more
- **Comprehensive Metadata**: Each task includes detailed metadata describing interaction steps
- **High-quality Images**: PNG format screenshots with clear visual elements
- **Diverse Scenarios**: Includes music players, payment forms, and various web UI patterns

## 📊 Dataset Structure

### Task Format

Each task in the dataset follows this structure:

```
task_name_timestamp/
├── metadata.json           # Task metadata and interaction descriptions
├── step_00.png           # Initial state (before any interaction)
├── step_01.png           # State after step 1 interaction
├── step_02.png           # State after step 2 interaction
└── ...                   # Additional interaction steps
```

### Metadata Structure

Each `metadata.json` file contains:

```json
{
  "id": "task_name_timestamp",
  "description": "Brief description of the web application",
  "steps": [
    {
      "step_index": 0,
      "description": "Initial state description",
      "image": "step_00.png"
    },
    {
      "step_index": 1,
      "description": "First interaction description",
      "image": "step_01.png"
    }
  ]
}
```

## 📦 Dataset Contents

This dataset includes:

- **Total Tasks**: Multiple real-world web interaction scenarios
- **Steps per Task**: Typically 5-7 interaction steps
- **Image Format**: PNG
- **Image Resolution**: High-resolution screenshots
- **Applications**: Various popular web platforms

## 🎯 Use Cases

MultiInteract-Bench is designed for:

1. **Model Evaluation**: Benchmarking multimodal LLMs on web interaction reconstruction
2. **Web Development**: Testing automated web page generation systems
3. **UI/UX Research**: Studying web interface patterns and interactions
4. **Computer Vision**: Evaluating image-to-code generation capabilities
5. **Agent Systems**: Training and testing web automation agents

## 🚀 Quick Start

### Download the Dataset

```bash
# Using huggingface-cli
huggingface-cli download zionzionzion/MultiInteract-Bench --repo-type dataset

# Or download the zip file directly
wget https://huggingface.co/datasets/zionzionzion/MultiInteract-Bench/resolve/main/dataset_multi_turn.zip
unzip dataset_multi_turn.zip
```

### Load in Python

```python
import json
from pathlib import Path

# Load a specific task
task_path = "dataset_multi_turn/Spotify_1766618072"
with open(f"{task_path}/metadata.json", "r") as f:
    metadata = json.load(f)

print(f"Task: {metadata['id']}")
print(f"Description: {metadata['description']}")
print(f"Number of steps: {len(metadata['steps'])}")

# Access images
for step in metadata['steps']:
    image_path = f"{task_path}/{step['image']}"
    print(f"Step {step['step_index']}: {step['description']}")
    print(f"  Image: {image_path}")
```

## 🔧 Related Repository

For the complete evaluation framework including:

- Model reproduction scripts
- Visual metrics calculation
- Automated screenshot capture
- Statistical analysis tools

Please visit our [GitHub repository](https://github.com/zion-zion-zion/MultiInteract-Bench).

### Evaluation Metrics

The associated repository implements 8 evaluation metrics:

1. **CLIP Similarity** - Semantic alignment (0-1, higher is better)
2. **LPIPS Distance** - Perceptual similarity (0-∞, lower is better)
3. **Style Loss** - Artistic style consistency (0-∞, lower is better)
4. **Text Similarity** - Text content preservation (0-1, higher is better)
5. **Color Histogram Similarity** - Color distribution (0-1, higher is better)
6. **Dominant Color Similarity** - Primary color consistency (0-1, higher is better)
7. **DINO Similarity** - Structural layout (0-1, higher is better)
8. **SSIM** - Structural fidelity (0-1, higher is better)

## 📊 Dataset Statistics

| Metric | Value |
|--------|-------|
| Total Tasks | Multiple scenarios |
| Total Images | 5-7 per task |
| Image Format | PNG |
| Metadata Format | JSON |
| Languages | English, Chinese |

## 📝 Citation

If you use MultiInteract-Bench in your research, please cite:

```bibtex
@dataset{multinteract_bench_2026,
  title = {MultiInteract-Bench: A Benchmark Dataset for Evaluating Web Interaction Reconstruction from Image Sequences},
  author = {Yang, Tiankun},
  year = {2026},
  publisher = {HuggingFace},
  url = {https://huggingface.co/datasets/zionzionzion/MultiInteract-Bench}
}
```

## 📧 Contact

For questions, issues, or suggestions regarding this dataset, please contact:

**Email**: yangtiankun25@mails.ucas.cn

## 📄 License

This dataset is provided under the MIT License. See the LICENSE file for details.

## 🔗 Links

- [GitHub Repository](https://github.com/zion-zion-zion/MultiInteract-Bench)
- [Dataset Download](https://huggingface.co/datasets/zionzionzion/MultiInteract-Bench)
- [HuggingFace Space](https://huggingface.co/spaces/zionzionzion/) (if applicable)

---

**Note**: This dataset is intended for research and educational purposes. Please respect the terms of service of the web applications from which screenshots were captured.