huggingface--transformers/docs/source/en/model_doc/dit.md

This model was published in HF papers on 2022-03-04 and contributed to Hugging Face Transformers on 2022-03-10.

DiT

DiT is an image transformer pretrained on large-scale unlabeled document images. It learns to predict the missing visual tokens from a corrupted input image. The pretrained DiT model can be used as a backbone in other models for visual document tasks like document image classification and table detection.

You can find all the original DiT checkpoints under the Microsoft organization.

Tip

Refer to the BEiT docs for more examples of how to apply DiT to different vision tasks.

The example below demonstrates how to classify an image with [Pipeline] or the [AutoModel] class.

from transformers import pipeline


pipeline = pipeline(
    task="image-classification",
    model="microsoft/dit-base-finetuned-rvlcdip",
    device=0
)
pipeline("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/dit-example.jpg")

import requests
import torch
from PIL import Image

from transformers import AutoImageProcessor, AutoModelForImageClassification


image_processor = AutoImageProcessor.from_pretrained(
    "microsoft/dit-base-finetuned-rvlcdip",
    use_fast=True,
)
model = AutoModelForImageClassification.from_pretrained(
    "microsoft/dit-base-finetuned-rvlcdip",
    device_map="auto",
)
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/dit-example.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = image_processor(image, return_tensors="pt").to(model.device)

with torch.no_grad():
  logits = model(**inputs).logits
predicted_class_id = logits.argmax(dim=-1).item()

class_labels = model.config.id2label
predicted_class_label = class_labels[predicted_class_id]
print(f"The predicted class label is: {predicted_class_label}")

Notes

• The pretrained DiT weights can be loaded in a [BEiT] model with a modeling head to predict visual tokens.

  from transformers import BeitForMaskedImageModeling
  
  model = BeitForMaskedImageModeling.from_pretraining("microsoft/dit-base")
  

Resources

• Refer to this notebook for a document image classification inference example.