Document Image Transformer (large-sized model)

Document Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 2006), a dataset that includes 42 million document images and fine-tuned on RVL-CDIP , a dataset consisting of 400,000 grayscale images in 16 classes, with 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 is identical to the architecture of 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.

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.

How to use

Here is how to use this model in PyTorch:

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-large-finetuned-rvlcdip")
model = AutoModelForImageClassification.from_pretrained("microsoft/dit-large-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}
}

microsoft/dit-large-finetuned-rvlcdip powered by Hugging Face Inference Toolkit

Send Request

You can use cURL or any REST Client to send a request to the AzureML endpoint with your AzureML token.

curl <AZUREML_ENDPOINT_URL> \
    -X POST \
    -H "Authorization: Bearer <AZUREML_TOKEN>" \
    -H "Content-Type: image/jpeg" \
    --data-binary @"image.jpg"

Supported Parameters

inputs (string): The input image data as a base64-encoded string. If no parameters are provided, you can also provide the image data as a raw bytes payload.
parameters (object):
function_to_apply (enum): Possible values: sigmoid, softmax, none.
top_k (integer): When specified, limits the output to the top K most probable classes.

Check the full API Specification at the Hugging Face Inference documentation .

Model Specifications

LicenseUnknown

Last UpdatedAugust 2025

ProviderHugging Face

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