Instructions to use google/vit-base-patch16-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/vit-base-patch16-384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="google/vit-base-patch16-384") 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("google/vit-base-patch16-384") model = AutoModelForImageClassification.from_pretrained("google/vit-base-patch16-384") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 55dca37799e078d7d554cd2a9c3b36fa0aa0d5ed8656300330751fbe229597d2
- Size of remote file:
- 347 MB
- SHA256:
- e060ccd0918587a3142d40f600df2a9552b4d4b57ba7b141fbc6ebab8757e616
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