PTA-1: Prompt-to-Automation Model
| Property | Value |
|---|---|
| Parameter Count | 271M |
| License | MIT |
| Base Model | Microsoft Florence-2-base |
| Language | English |
| Tensor Type | F32 |
What is PTA-1?
PTA-1 is a revolutionary vision-language model designed specifically for computer and phone automation tasks. Built on Microsoft's Florence-2 architecture, it achieves remarkable performance in GUI text and element localization despite its relatively compact size of 271M parameters. The model excels at processing screenshots and textual descriptions to precisely locate interface elements, making it ideal for automation tasks.
Implementation Details
The model operates on a simple yet powerful premise: it takes a screenshot and a description of a target element as input and outputs the corresponding bounding box coordinates. Implementation requires basic dependencies including PyTorch, transformers, and Pillow, making it accessible for developers. The model supports both CPU and GPU execution, with automatic tensor type optimization.
- Efficient architecture with only 271M parameters
- Outperforms larger models in GUI element detection
- Supports F32 tensor operations
- Local execution capability for reduced latency
Core Capabilities
- Precise element localization with 79.98% mean accuracy
- Exceptional performance on Wave-UI dataset (90.69%)
- Strong text-based element detection (76.28% on PTA-text)
- Efficient processing of screenshots and natural language queries
Frequently Asked Questions
Q: What makes this model unique?
PTA-1 stands out for achieving superior performance with significantly fewer parameters than competitors. It outperforms models up to 30 times larger, making it ideal for local deployment and real-time applications.
Q: What are the recommended use cases?
The model is particularly well-suited for GUI automation tasks, including test automation, accessibility tools, and interactive computer vision applications where precise element localization is crucial.
