# Document Image Classification

This model is a fine-tuned version of microsoft/dit-base-finetuned-rvlcdip on an image folder dataset, primarily used for image classification tasks.

An image classification model based on the ViT architecture, designed to identify whether an image contains text and the type of text (Latin, Chinese, Arabic)

An image classification model fine-tuned based on microsoft/dit-base, performing well on the image folder dataset

Donut is an OCR-free document understanding Transformer model that combines a visual encoder and text decoder to process document images.

Document image classification model pretrained on IIT-CDIP and fine-tuned on RVL-CDIP, using Transformer architecture

DiT is a Transformer-based document image classification model, pretrained on the IIT-CDIP dataset and fine-tuned on the RVL-CDIP dataset