Instructions to use bdpc/vit-base_rvl_cdip_ce with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bdpc/vit-base_rvl_cdip_ce with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="bdpc/vit-base_rvl_cdip_ce") 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("bdpc/vit-base_rvl_cdip_ce") model = AutoModelForImageClassification.from_pretrained("bdpc/vit-base_rvl_cdip_ce") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
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by SFconvertbot - opened
- model.safetensors +3 -0
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version https://git-lfs.github.com/spec/v1
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oid sha256:80f4a80d0d25356f78dbff58284266b861742f2463b5a3747ec1dd4c826da68d
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size 343267040
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