Instructions to use pk3388/vit-base-patch16-224-Rado_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pk3388/vit-base-patch16-224-Rado_5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="pk3388/vit-base-patch16-224-Rado_5") 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("pk3388/vit-base-patch16-224-Rado_5") model = AutoModelForImageClassification.from_pretrained("pk3388/vit-base-patch16-224-Rado_5") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8c0421d054bf0e82fb9fc1bc0c91afa153e3fc610a96336e1c834cca7961bf5b
- Size of remote file:
- 5.18 kB
- SHA256:
- d50ac9f38bbf14a987f5f6e9e08d69c6d53d0e6b4bdfba0d71c822000b5ea40a
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