---
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]