enginex-mlu370-any2any/transformers/docs/source/en/model_doc/dit.md

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*This model was released on 2022-03-04 and added to Hugging Face Transformers on 2022-03-10.*
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# DiT

[DiT](https://huggingface.co/papers/2203.02378) 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.

<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/dit_architecture.jpg"/>

You can find all the original DiT checkpoints under the [Microsoft](https://huggingface.co/microsoft?search_models=dit) organization.

> [!TIP]
> Refer to the [BEiT](./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.

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
import torch
from transformers import pipeline

pipeline = pipeline(
    task="image-classification",
    model="microsoft/dit-base-finetuned-rvlcdip",
    dtype=torch.float16,
    device=0
)
pipeline("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/dit-example.jpg")
```

</hfoption>
<hfoption id="AutoModel">

```py
import torch
import requests
from PIL import Image
from transformers import AutoModelForImageClassification, AutoImageProcessor

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}")
```

</hfoption>
</hfoptions>

## Notes

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

   ```py
   from transformers import BeitForMaskedImageModeling

   model = BeitForMaskedImageModeling.from_pretraining("microsoft/dit-base")
   ```

## Resources

- Refer to this [notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/DiT/Inference_with_DiT_(Document_Image_Transformer)_for_document_image_classification.ipynb) for a document image classification inference example.