Document Image Transformer (DiT)
| Property | Value |
|---|---|
| Author | Microsoft |
| Paper | DiT: Self-supervised Pre-training for Document Image Transformer |
| Dataset | RVL-CDIP (400,000 images, 16 classes) |
| Downloads | 6,539 |
What is dit-base-finetuned-rvlcdip?
The Document Image Transformer (DiT) is a specialized vision transformer model designed for document image analysis. Pre-trained on 42 million document images from IIT-CDIP and fine-tuned on RVL-CDIP dataset, it represents a significant advancement in document understanding technology. The model shares its architecture with BEiT and processes images as sequences of 16x16 patches.
Implementation Details
This implementation utilizes a transformer encoder architecture that processes document images through a series of self-attention layers. The model employs a self-supervised pre-training approach where it predicts visual tokens from a discrete VAE encoder based on masked patches.
- Processes images as 16x16 fixed-size patches
- Employs linear embedding with absolute position encodings
- Supports 16 document classification classes
- Compatible with the Hugging Face transformers library
Core Capabilities
- Document image classification across 16 categories
- Feature extraction for downstream tasks
- Document layout analysis
- Table detection capabilities
- Vector space encoding of document images
Frequently Asked Questions
Q: What makes this model unique?
This model combines the power of transformer architecture with specialized document understanding capabilities, pre-trained on an extensive dataset of 42 million documents and fine-tuned for specific document classification tasks.
Q: What are the recommended use cases?
The model is ideal for document classification tasks, layout analysis, and feature extraction for downstream document processing tasks. It's particularly effective for organizations dealing with large volumes of varied document types that need automated classification and analysis.
