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---
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
```bash
pip install ultralytics
```
---
## How to Load and Use the Model
```python
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
```python
[
'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] |