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:
- b00c25fe7d2a2d2cbd08861d91dd3c917e0f9bf2c1c00216cc45794ba3786797
- Size of remote file:
- 14.9 kB
- SHA256:
- 480560e778949543ece85279be8e3f966cbdadd42eb1d96c595f445d20372dae
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