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
- Xet hash:
- 95bad923507acb75ef172edcf5eaf8210585005ea1291ac37cddc500322090fe
- Size of remote file:
- 343 MB
- SHA256:
- f124efeaf7678696a010392205afa83fc66d28f2ae661d3a662937e686cf596e
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