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") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 47dd97211485d010d6db7ceeec052b98bbb84b2e4a4e4480c186f2b21f6af838
- Size of remote file:
- 347 MB
- SHA256:
- 30f9125423060139b80ddae09daa8a1b612eb1eda8fc34a0b58cdfe920cbbc0f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.