Dit Base Finetuned Rvlcdip

🚀 Document Image Transformer (base-sized model)

The Document Image Transformer (DiT) is pre - trained on a large document image dataset and fine - tuned on RVL - CDIP, which can be used for various document - related downstream tasks.

🚀 Quick Start

The Document Image Transformer (DiT) model is pre - trained on IIT - CDIP (Lewis et al., 2006), a dataset with 42 million document images. It's then fine - tuned on RVL - CDIP, a dataset of 400,000 grayscale images divided into 16 classes (25,000 images per class). It was introduced in the paper DiT: Self - supervised Pre - training for Document Image Transformer by Li et al. and first released in this repository. Note that DiT has the same architecture as BEiT.

Disclaimer: The team releasing DiT did not write a model card for this model, so this model card has been written by the Hugging Face team.

✨ Features

• Tags: dit, vision, image - classification
• Datasets: rvl_cdip
• Widget examples:
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  • Scientific publication

📚 Documentation

Model description

The Document Image Transformer (DiT) is a transformer encoder model (BERT - like) pre - trained on a large collection of images in a self - supervised fashion. The pre - training objective for the model is to predict visual tokens from the encoder of a discrete VAE (dVAE), based on masked patches.

Images are presented to the model as a sequence of fixed - size patches (resolution 16x16), which are linearly embedded. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.

By pre - training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled document images for instance, you can train a standard classifier by placing a linear layer on top of the pre - trained encoder.

Intended uses & limitations

You can use the raw model for encoding document images into a vector space, but it's mostly meant to be fine - tuned on tasks like document image classification, table detection or document layout analysis. See the model hub to look for fine - tuned versions on a task that interests you.

💻 Usage Examples

Basic Usage

from transformers import AutoImageProcessor, AutoModelForImageClassification
import torch
from PIL import Image

image = Image.open('path_to_your_document_image').convert('RGB')

processor = AutoImageProcessor.from_pretrained("microsoft/dit-base-finetuned-rvlcdip")
model = AutoModelForImageClassification.from_pretrained("microsoft/dit-base-finetuned-rvlcdip")

inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits

# model predicts one of the 16 RVL-CDIP classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])

BibTeX entry and citation info

@article{Lewis2006BuildingAT,
  title={Building a test collection for complex document information processing},
  author={David D. Lewis and Gady Agam and Shlomo Engelson Argamon and Ophir Frieder and David A. Grossman and Jefferson Heard},
  journal={Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval},
  year={2006}
}