--- license: apache-2.0 task_categories: - text-generation language: - en tags: - agent size_categories: - n<1K configs: - config_name: webpage data_files: - split: test path: "webpage/test.parquet" - config_name: frontend data_files: - split: test path: "frontend/test.parquet" - config_name: website data_files: - split: test path: "website/test.parquet" --- # Vision2Web: A Hierarchical Benchmark for Visual Website Development with Agent Verification ![Web Development](https://img.shields.io/badge/Task-Web%20Development-red) ![Multi-Modal](https://img.shields.io/badge/Task-Multi--Modal-red) ![Vision2Web](https://img.shields.io/badge/Dataset-Vision2Web-blue)
[[🏠 Project Page](https://vision2web-bench.github.io/)] [[📖 arXiv Paper](https://arxiv.org/abs/2603.26648)] [[🏆 Leaderboard](https://vision2web-bench.github.io/#leaderboard)] [[📮 Submit Results](https://huggingface.co/datasets/zai-org/Vision2Web-Leaderboard)]

Vision2Web is a comprehensive benchmark designed to evaluate multimodal coding agents on **visual website development tasks spanning the full software development lifecycle**. This dataset repository contains the **benchmark tasks, UI prototypes, test workflows, and resources** used to evaluate agent performance. --- # 👀 Introduction Vision2Web is a hierarchical benchmark for evaluating multimodal coding agents on **end-to-end visual website development**, measuring their ability to integrate: - UI understanding - requirements reasoning - interactive logic - full-stack implementation in **long-horizon development scenarios**.

The benchmark is organized into three progressive levels: ### Level 1 – Static Webpage Generate responsive executable webpages from multi-device UI prototypes (desktop / tablet / mobile). **Metric** - Visual Score (VS) --- ### Level 2 – Interactive Frontend Develop multi-page interactive frontends from multiple prototypes and textual specifications. **Metrics** - Visual Score (VS) - Functional Score (FS) --- ### Level 3 – Full-Stack Website Build complete full-stack web systems from requirement documents and UI prototypes. Agents must implement: - backend logic - state management - frontend interactions **Metrics** - Visual Score (VS) - Functional Score (FS) --- Evaluation uses a **workflow-based agent verification paradigm** combining: - **GUI Agent verifiers** for functional correctness - **VLM-based judges** for visual fidelity This enables **scalable and implementation-agnostic evaluation** across increasing levels of complexity. --- # 📊 Benchmark Statistics Vision2Web contains: - **193 tasks** - **16 subcategories** - **4 major domains** Domains include: - E-Commerce - SaaS - Content Platforms - Public Service The dataset includes: - **918 prototype images** - **1,255 functional test cases**


--- # 📥 Using the Dataset The dataset can be downloaded directly from Hugging Face. After downloading, extract the dataset and place it in your project directory with the following structure: ``` datasets/ ├── webpage/ # Level 1: Static Webpage (100 tasks) ├── frontend/ # Level 2: Interactive Frontend (66 tasks) └── website/ # Level 3: Full-Stack Website (27 tasks) ``` Each task directory contains the following components: | File / Folder | Description | |---|---| | `prototypes/` | UI prototype images (desktop / tablet / mobile) | | `resources/` | Multimedia assets used in tasks | | `workflow.json` | Functional test workflow specification | | `prompt.txt` | Textual requirements (Level 2 only) | | `prd.md` | Requirement document (Level 3 only) | Once extracted, ensure the dataset directory is placed at the root of the Vision2Web project so that the evaluation pipeline can locate the benchmark tasks correctly. --- # ⚠️ License Vision2Web is released under the **CC-BY-NC-SA-4.0 license**. --- # ✒️ Citation If you find Vision2Web useful in your research, please cite: ```bibtex @misc{he2026vision2webhierarchicalbenchmarkvisual, title={Vision2Web: A Hierarchical Benchmark for Visual Website Development with Agent Verification}, author={Zehai He and Wenyi Hong and Zhen Yang and Ziyang Pan and Mingdao Liu and Xiaotao Gu and Jie Tang}, year={2026}, eprint={2603.26648}, archivePrefix={arXiv}, primaryClass={cs.SE}, url={https://arxiv.org/abs/2603.26648}, } ```