Instructions to use gavin199502/deki-yolo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use gavin199502/deki-yolo with ultralytics:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("gavin199502/deki-yolo", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
deki-yolo: Mobile UI Element Detection Model
This is a YOLO model trained to identify common UI elements in mobile screenshots. It is the core detection model for the deki huggingface space or deki github
Model Description
The model is trained to detect the following four classes of UI elements:
View: General-purpose containers.ImageView: Icons and images.Text: Text elements.Line: Separators and lines.
This model can be used as a foundational component for applications that need to understand screen layouts, such as AI agents for mobile automation, accessibility tools, and UI code generation.
YOLO examples
Bounding boxes with classes for bb_1:
Bounding boxes without classes but with IDs after NMS for bb_1:
Bounding boxes with classes for bb_2:
Bounding boxes without classes but with IDs after NMS for bb_2:
YOLO model accuracy
The model was trained on 486 images and was tested on 60 images.
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