Pix2Struct Large
| Property | Value |
|---|---|
| Parameter Count | 1.34B |
| License | Apache 2.0 |
| Supported Languages | English, French, Romanian, German, Multilingual |
| Paper | View Paper |
| Model Type | Image-to-Text |
What is pix2struct-large?
Pix2Struct-large is a sophisticated image encoder-text decoder model designed for visual language understanding. With 1.34 billion parameters, it represents a significant advancement in processing visually-situated language across various domains, from textbooks and web pages to mobile apps and natural images.
Implementation Details
The model utilizes a unique pretraining strategy where it learns to parse masked screenshots of web pages into simplified HTML. This approach inherently incorporates multiple learning signals including OCR, language modeling, and image captioning. The architecture features a variable-resolution input representation and flexible integration of language and vision inputs.
- Transformer-based architecture with safetensors implementation
- F32 tensor type for precise computations
- Supports five languages including English, French, Romanian, German, and multilingual capabilities
- Designed for fine-tuning on specific visual language tasks
Core Capabilities
- Document parsing and understanding
- Illustration interpretation
- User interface analysis
- Natural image processing
- Visual question answering
- Image captioning
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its pretraining approach using web page screenshots and HTML parsing, which provides a comprehensive foundation for understanding various forms of visually-situated language. It achieves state-of-the-art results in six out of nine tasks across four different domains.
Q: What are the recommended use cases?
The model is particularly well-suited for fine-tuning on tasks involving visual language understanding, including document analysis, UI interpretation, diagram understanding, and natural image processing. It's designed for researchers and developers working on complex visual-language applications.
