layoutlmv3-base-finetuned-publaynet | PromptLayer Models
layoutlmv3-base-finetuned-publaynet
HYPJUDY
LayoutLMv3 model fine-tuned on PubLayNet dataset, achieving 95.1 mAP @ IOU for document layout analysis. Built for document AI tasks.
| Property | Value |
|---|---|
| License | CC BY-NC-SA 4.0 |
| Paper | LayoutLMv3 Paper |
| Performance | 95.1 mAP @ IOU [0.50:0.95] |
What is layoutlmv3-base-finetuned-publaynet?
This is a specialized document AI model that builds upon Microsoft's LayoutLMv3 base architecture, fine-tuned specifically on the PubLayNet dataset. It's designed to understand and analyze document layouts by processing both text and image information in a unified manner.
Implementation Details
The model is based on the microsoft/layoutlmv3-base architecture and has been fine-tuned specifically for document layout analysis tasks. It implements a unified text and image masking approach, making it particularly effective for document AI applications.
- Built on Microsoft's LayoutLMv3 architecture
- Fine-tuned on PubLayNet dataset
- Achieves 95.1 mAP on validation set
- Supports unified text and image masking
Core Capabilities
- Document layout analysis
- Integrated text and image understanding
- High-accuracy layout detection
- Suitable for academic and research documents
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its unified approach to text and image masking in document analysis, achieving exceptional accuracy (95.1 mAP) on the PubLayNet validation set. It's specifically optimized for understanding document layouts in academic and research contexts.
Q: What are the recommended use cases?
The model is ideal for document layout analysis tasks, particularly in academic and research document processing. It's well-suited for applications requiring accurate identification and classification of document elements like tables, figures, and text blocks in scientific publications.