metadata

metrics:
  - accuracy
base_model:
  - Ultralytics/YOLOv8
pipeline_tag: object-detection
tags:
  - UI/UX
  - test-automation
  - object-detection
  - yolov8
license: apache-2.0
language:
  - en
new_version: yasirfaizahmed/android_ui_detection_yolov8
library_name: ultralytics
datasets:
  - yasirfaizahmed/android_ui_detection_yolov8

Android UI Detection – YOLOv8

This YOLOv8 model is trained to detect various Android UI elements in app/game screenshots, such as buttons, cards, toolbars, text views, and more.

Trained using YOLOv8 Nano
Detects 21 Android UI classes
Ideal for UI automation, testing, and design analysis

Installation

pip install ultralytics

How to Load and Use the Model

from ultralytics import YOLO

# Load the model directly from Hugging Face
model = YOLO("yasirfaizahmed/android_ui_detection_yolov8")

# Run detection on an image
results = model("your_image.jpg")  # Replace with your actual image path

# Show results with bounding boxes
results[0].show()

Classes Detected

[
  'BackgroundImage', 'Bottom_Navigation', 'Card', 'CheckBox', 'Checkbox',
  'CheckedTextView', 'Drawer', 'EditText', 'Icon', 'Image', 'Map', 'Modal',
  'Multi_Tab', 'PageIndicator', 'Remember', 'Spinner', 'Switch', 'Text',
  'TextButton', 'Toolbar', 'UpperTaskBar'
]

Model Structure

Trained with: yolov8n.pt base
Format: YOLOv8 PyTorch
Dataset: Custom Pascal VOC-style Android UI dataset

Training Configuration

Recommended image size: 640×640
Supports predict, val, export, and train pipelines from Ultralytics
Use .predict(source="folder_or_image.jpg") for batch inference

[More Information Needed]