Instructions to use jayanta/vit-base-patch16-224-in21k-harm-C with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jayanta/vit-base-patch16-224-in21k-harm-C with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jayanta/vit-base-patch16-224-in21k-harm-C") 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("jayanta/vit-base-patch16-224-in21k-harm-C") model = AutoModelForImageClassification.from_pretrained("jayanta/vit-base-patch16-224-in21k-harm-C", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2787e1933e1801d1387e07181580a2f23b3f68108eb353e19204d99cea6a53d4
- Size of remote file:
- 343 MB
- SHA256:
- 5d0376afdea20cd8307f43e1090eba6fa313b743ac2f01678ca9f333ad8f5796
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