---
library_name: transformers
license: mit
datasets:
- biglab/jitteredwebsites-merged-224-paraphrased
base_model:
- openai/clip-vit-base-patch32
---

# Model Card for Model ID

UIClip is a model designed to quantify the design quality and releveance of a user interface (UI) screenshot given a textual description.

### Model Description

UIClip is a model designed to quantify the design quality and releveance of a user interface (UI) screenshot given a textual description.
This model can also be used to generate natural language design suggestions (see paper).
This is a model described in the publication "UIClip: A Data-driven Model for Assessing User Interface Design" presented at UIST 2024 (https://arxiv.org/abs/2404.12500).

User interface (UI) design is a difficult yet important task for ensuring the usability, accessibility, and aesthetic qualities of applications. In our paper, we develop a machine-learned model, UIClip, for assessing the design quality and visual relevance of a UI given its screenshot and natural language description. To train UIClip, we used a combination of automated crawling, synthetic augmentation, and human ratings to construct a large-scale dataset of UIs, collated by description and ranked by design quality. Through training on the dataset, UIClip implicitly learns properties of good and bad designs by i) assigning a numerical score that represents a UI design's relevance and quality and ii) providing design suggestions. In an evaluation that compared the outputs of UIClip and other baselines to UIs rated by 12 human designers, we found that UIClip achieved the highest agreement with ground-truth rankings. Finally, we present three example applications that demonstrate how UIClip can facilitate downstream applications that rely on instantaneous assessment of UI design quality: i) UI code generation, ii) UI design tips generation, and iii) quality-aware UI example search.


- **Developed by:** BigLab
- **Model type:** CLIP-style Multi-modal Dual-encoder Transformer
- **Language(s) (NLP):** English
- **License:** MIT