microsoft/dit-base-finetuned-rvlcdip
image-classification model by microsoft
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Model card & documentation
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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, 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 by Li et al. and first released in this repository. Note that DiT is identical to the architecture of BEiT. 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. 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. Images are presented to the model as…
Read the full model card ↗ · Preview checked 2026-10-31T23:06:28.437Z
Inside the original model card — Document outline
- Document Image Transformer (base-sized model)
- Model description
- Intended uses & limitations
- How to use
- BibTeX entry and citation info
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Model overview
microsoft/dit-base-finetuned-rvlcdip is a image-classification model repository published by microsoft on Hugging Face. The source reports the transformers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- image-classification
- Library
- transformers
- Recent downloads (30 days)
- 69,736
- Cumulative likes
- 47
- Hugging Face trending score
- 0
- Reported safetensors parameters
- Not reported
- Architecture
- BeitForImageClassification
- License
- mit
- Access
- Not gated by Hugging Face
- Created
- 2022-03-07T20:48:42.000Z
- Last modified
- 2026-10-10T14:31:30.000Z
Compatibility and lineage
Language coverage was not reported.
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-11-01T06:54:32.747Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.
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