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* documenation finished * Update dit.md --------- Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
97 lines
6.6 KiB
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97 lines
6.6 KiB
Markdown
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">
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</div>
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# DiT
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[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.
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/dit_architecture.jpg"/>
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You can find all the original DiT checkpoints under the [Microsoft](https://huggingface.co/microsoft?search_models=dit) organization.
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> [!TIP]
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> Refer to the [BEiT](./beit) docs for more examples of how to apply DiT to different vision tasks.
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The example below demonstrates how to classify an image with [`Pipeline`] or the [`AutoModel`] class.
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<hfoptions id="usage">
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<hfoption id="Pipeline">
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```py
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import torch
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from transformers import pipeline
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pipeline = pipeline(
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task="image-classification",
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model="microsoft/dit-base-finetuned-rvlcdip",
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torch_dtype=torch.float16,
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device=0
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)
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pipeline(images="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/dit-example.jpg")
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```
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</hfoption>
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<hfoption id="AutoModel">
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```py
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import torch
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import requests
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from PIL import Image
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from transformers import AutoModelForImageClassification, AutoImageProcessor
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image_processor = AutoImageProcessor.from_pretrained(
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"microsoft/dit-base-finetuned-rvlcdip",
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use_fast=True,
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)
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model = AutoModelForImageClassification.from_pretrained(
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"microsoft/dit-base-finetuned-rvlcdip",
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device_map="auto",
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)
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url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/dit-example.jpg"
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image = Image.open(requests.get(url, stream=True).raw)
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inputs = image_processor(image, return_tensors="pt").to("cuda")
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with torch.no_grad():
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logits = model(**inputs).logits
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predicted_class_id = logits.argmax(dim=-1).item()
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class_labels = model.config.id2label
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predicted_class_label = class_labels[predicted_class_id]
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print(f"The predicted class label is: {predicted_class_label}")
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```
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</hfoption>
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## Notes
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- The pretrained DiT weights can be loaded in a [BEiT] model with a modeling head to predict visual tokens.
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```py
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from transformers import BeitForMaskedImageModeling
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model = BeitForMaskedImageModeling.from_pretraining("microsoft/dit-base")
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```
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## Resources
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- 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.
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