Instructions to use ericakcc/vit-base-beans-demo-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ericakcc/vit-base-beans-demo-v5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ericakcc/vit-base-beans-demo-v5") 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("ericakcc/vit-base-beans-demo-v5") model = AutoModelForImageClassification.from_pretrained("ericakcc/vit-base-beans-demo-v5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 123b445ec3e982128058b26f20488b583908a04f78918792e686135a3b78290a
- Size of remote file:
- 5.3 kB
- SHA256:
- f7c147628a41c43ca75db584d4f17e5ccdf56857e8425da3937fc20119982557
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