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:
- 3843a8b660dc66c8d9a093e4fe5c7214f0474ea82e941ca78dfdc44adc9081a0
- Size of remote file:
- 16.3 kB
- SHA256:
- 9afc66a291041039c3024c84e30f3efad1365662dcb6384e14e60251006b53aa
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