--- license: mit dataset_info: features: - name: image dtype: image - name: bbox dtype: string - name: text dtype: string splits: - name: train num_bytes: 4189624847.8344607 num_examples: 19514 - name: test num_bytes: 523649431.37584555 num_examples: 2439 - name: val num_bytes: 523864129.7896938 num_examples: 2440 download_size: 4642434734 dataset_size: 5237138409 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: val path: data/val-* task_categories: - image-to-text tags: - code pretty_name: vision2ui size_categories: - 100M Automatically generating UI code from webpage design visions can significantly alleviate the burden of developers, enabling beginner developers or designers to directly generate Web pages from design diagrams. Currently, prior research has accomplished the objective of generating UI code from rudimentary design visions or sketches through designing deep neural networks. Inspired by the groundbreaking advancements achieved by Multimodal Large Language Models (MLLMs), the automatic generation of UI code from high-fidelity design images is now emerging as a viable possibility. Nevertheless, our investigation reveals that existing MLLMs are hampered by the scarcity of authentic, high-quality, and large-scale datasets, leading to unsatisfactory performance in automated UI code generation. To mitigate this gap, we present a novel dataset, termed VISION2UI, extracted from real-world scenarios, augmented with comprehensive layout information, tailored specifically for finetuning MLLMs in UI code generation. Specifically, this dataset is derived through a series of operations, encompassing collecting, cleaning, and filtering of the open-source Common Crawl dataset. In order to uphold its quality, a neural scorer trained on labeled samples is utilized to refine the data, retaining higher-quality instances. Ultimately, this process yields a dataset comprising 2,000 (Much more is coming soon) parallel samples encompassing design visions and UI code. The paper can be accessed at: https://arxiv.org/abs/2404.06369 Much more data is coming soon!