Instructions to use DataScienceProject/Vit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DataScienceProject/Vit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DataScienceProject/Vit") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DataScienceProject/Vit", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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base_model: google/vit-base-patch16-224
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datasets:
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- DataScienceProject/Art_Images_Ai_And_Real_
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---
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# Model Card for Model ID
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base_model: google/vit-base-patch16-224
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datasets:
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- DataScienceProject/Art_Images_Ai_And_Real_
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pipeline_tag: image-classification
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library_name: transformers
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---
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# Model Card for Model ID
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