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", device_map="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- d3cacc9e35e7d8625c8b43524e28f22b6cf0dd5fc5c168a554326421c209c5fb
- Size of remote file:
- 9.25 kB
- SHA256:
- c326cef2253467758437fa37eff4c34fa9fc4c5e8256b024a9d890bf0068e66d
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