Image Classification
Transformers
Safetensors
deit
vision
document-layout-analysis
document-classification
doclaynet
Instructions to use kaixkhazaki/deit_doclaynet_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kaixkhazaki/deit_doclaynet_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="kaixkhazaki/deit_doclaynet_base") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("kaixkhazaki/deit_doclaynet_base") model = AutoModelForImageClassification.from_pretrained("kaixkhazaki/deit_doclaynet_base") - Notebooks
- Google Colab
- Kaggle
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# Data-efficient Image Transformer(DeiT) for Document Classification(DocLayNet)
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This model is a fine-tuned Data-efficient Image Transformer(DeiT) for document
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Trained on images of the document categories from DocLayNet dataset where the categories namely(with their indexes) are :
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# Data-efficient Image Transformer(DeiT) for Document Classification(DocLayNet)
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This model is a fine-tuned Data-efficient Image Transformer(DeiT) for document image classification based on the DocLayNet dataset.
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Trained on images of the document categories from DocLayNet dataset where the categories namely(with their indexes) are :
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