Instructions to use google/vit-base-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/vit-base-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="google/vit-base-patch16-224") 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-patch16-224") model = AutoModelForImageClassification.from_pretrained("google/vit-base-patch16-224", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
docs: cite the original Vision Transformer paper in the model card
#25
by unit27research - opened
The card describes this checkpoint as ViT from Dosovitskiy et al. but its first BibTeX entry currently points to a different paper. This swaps that entry for the citation published by the official Vision Transformer repository. The ImageNet citation remains unchanged.