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+*This model was published in HF papers on 2022-03-04 and contributed to Hugging Face Transformers on 2022-03-10.*
+
+# 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">
+
+```python
+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")
+```
+
+</hfoption>
+<hfoption id="AutoModel">
+
+```python
+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}")
+```
+
+</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.