Stars
Python library for Internal Clustering Validity Measures
AppAgent: Multimodal Agents as Smartphone Users, an LLM-based multimodal agent framework designed to operate smartphone apps.
A Figma plugin that allows you to do a simple usability test in Figma using AppAgent
Official codebase for I-JEPA, the Image-based Joint-Embedding Predictive Architecture. First outlined in the CVPR paper, "Self-supervised learning from images with a joint-embedding predictive arch…
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Examples and guides for using the OpenAI API
Code and documentation to train Stanford's Alpaca models, and generate the data.
The simplest way to run LLaMA on your local machine
An open source implementation of CLIP.
A LLM based research assistant that allows you to have a conversation with a research paper
Use Hugging Face with JavaScript
SUITE―스위트는 정원과 직각, 직선과 사선의 기하학적 조형 요소에 집중하여 간결한 표정을 가진 UI 헤드라인 타입페이스입니다.
SUIT―수트는 반복되는 노력을 기울이지 않아도 완성도 높은 형태를 유지하며, 소모적인 커뮤니케이션도 줄일 수 있도록 제작한 UI 본문용 폰트입니다.
Your API ⇒ Paid MCP. Instantly.
Stable Diffusion with Core ML on Apple Silicon
This is the official PyTorch implementation of the paper Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP.
We release the UICaption dataset. The dataset consists of UI images (icons and screenshots) and associated text descriptions. This dataset was used to pre-train the Lexi model which provides a gene…
Image to prompt with BLIP and CLIP
A library for preparing data for machine translation research (monolingual preprocessing, bitext mining, etc.) built by the FAIR NLLB team.
The dataset includes screen summaries that describes Android app screenshot's functionalities. It is used for training and evaluation of the screen2words models (our paper accepted by UIST'21 will …
Mobile App Tasks with Iterative Feedback (MoTIF): Addressing Task Feasibility in Interactive Visual Environments
It includes two datasets that are used in the downstream tasks for evaluating UIBert: App Similar Element Retrieval data and Visual Item Selection (VIS) data. Both datasets are written TFRecords.