Web Form UI Field Detection Model
| Property | Value |
|---|---|
| Architecture | YOLOv8s |
| Training Dataset | 600 annotated images |
| Performance | mAP@0.95: 0.52196 |
| Training Hardware | NVIDIA GeForce RTX 3090 |
What is web-form-ui-field-detection?
The web-form-ui-field-detection is a specialized object detection model built on YOLOv8 architecture, designed to identify and locate UI form elements in web interfaces. The model can detect various form components including name fields, numbers, email inputs, passwords, buttons, and radio buttons, making it valuable for automated form analysis and processing.
Implementation Details
Built using the ultralytics library, this model employs advanced object detection techniques with a focus on web form elements. It was trained for 1 hour on an RTX 3090 GPU using the Adam optimizer with a 1e-4 learning rate. The model achieves impressive metrics with 0.80 precision and 0.70 recall scores.
- Utilizes YOLOv8s architecture for accurate object detection
- Implements confidence threshold of 0.25 and IoU threshold of 0.45
- Supports up to 1000 detections per image
- Trained on diverse form layouts and lighting conditions
Core Capabilities
- Accurate detection of form input fields and buttons
- Robust performance across different web form layouts
- Support for multiple field types including text, email, and password inputs
- Automated form structure analysis and field extraction
Frequently Asked Questions
Q: What makes this model unique?
This model specializes in web form UI detection, offering high precision in identifying form elements while maintaining real-world applicability through extensive training on diverse form layouts.
Q: What are the recommended use cases?
The model is ideal for automated form processing, UI testing automation, form accessibility analysis, and data extraction from web forms. It's particularly useful for applications requiring automated form field detection and layout analysis.
