Instructions to use pytholic/vit_classification_huggingface with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pytholic/vit_classification_huggingface with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="pytholic/vit_classification_huggingface") 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("pytholic/vit_classification_huggingface") model = AutoModelForImageClassification.from_pretrained("pytholic/vit_classification_huggingface") - Notebooks
- Google Colab
- Kaggle
commit files to HF hub
Browse files
README.md
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- task:
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name: Image Classification
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type: image-classification
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metrics:
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- name: Accuracy
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type: accuracy
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# vit_classification_huggingface
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- task:
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name: Image Classification
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type: image-classification
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# vit_classification_huggingface
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