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--- |
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tags: |
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- dit |
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datasets: |
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- rvl_cdip |
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inference: false |
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--- |
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# Document Image Transformer (large-sized model) |
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Document Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 2006), a dataset that includes 42 million document images and fine-tuned on [RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/), a dataset consisting of 400,000 grayscale images in 16 classes, with 25,000 images per class. 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). |
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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. |
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## Model description |
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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. |
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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. |
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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. |
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## Intended uses & limitations |
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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. |
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### How to use |
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Here is how to use this model in PyTorch: |
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```python |
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from transformers import AutoImageProcessor, AutoModelForImageClassification |
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import torch |
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from PIL import Image |
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image = Image.open('path_to_your_document_image').convert('RGB') |
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processor = AutoImageProcessor.from_pretrained("microsoft/dit-large-finetuned-rvlcdip") |
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model = AutoModelForImageClassification.from_pretrained("microsoft/dit-large-finetuned-rvlcdip") |
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inputs = processor(images=image, return_tensors="pt") |
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outputs = model(**inputs) |
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logits = outputs.logits |
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# model predicts one of the 16 RVL-CDIP classes |
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predicted_class_idx = logits.argmax(-1).item() |
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print("Predicted class:", model.config.id2label[predicted_class_idx]) |
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``` |
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### BibTeX entry and citation info |
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```bibtex |
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@article{Lewis2006BuildingAT, |
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title={Building a test collection for complex document information processing}, |
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author={David D. Lewis and Gady Agam and Shlomo Engelson Argamon and Ophir Frieder and David A. Grossman and Jefferson Heard}, |
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journal={Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval}, |
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year={2006} |
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} |
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``` |
