readme: ---
  tags:
  - dit
  inference: false
  ---
  
  # Document Image Transformer (base-sized model) 
  
  Document Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 2006), a dataset that includes 42 million document images. It was introduced in the paper [DiT: Self-supervised Pre-training for Document Image Transformer](https://arxiv.org/abs/2203.02378) by Li et al. and first released in [this repository](https://github.com/microsoft/unilm/tree/master/dit). Note that DiT is identical to the architecture of [BEiT](https://huggingface.co/docs/transformers/model_doc/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](https://huggingface.co/models?search=microsoft/dit) to look for fine-tuned versions on a task that interests you.
  
  ### How to use
  
  Here is how to use this model in PyTorch:
  
  ```python
  from transformers import BeitFeatureExtractor, BeitForMaskedImageModeling
  import torch
  from PIL import Image
  
  image = Image.open('path_to_your_document_image').convert('RGB')
  
  feature_extractor = BeitFeatureExtractor.from_pretrained("microsoft/dit-base")
  model = BeitForMaskedImageModeling.from_pretrained("microsoft/dit-base")
  
  num_patches = (model.config.image_size // model.config.patch_size) ** 2
  pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
  # create random boolean mask of shape (batch_size, num_patches)
  bool_masked_pos = torch.randint(low=0, high=2, size=(1, num_patches)).bool()
  
  outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)
  loss, logits = outputs.loss, outputs.logits
  ```
  
  ### BibTeX entry and citation info
  
  ```bibtex
  @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}
  }
  ```