Instructions to use superlazycoder/vit-base-beans-demo-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use superlazycoder/vit-base-beans-demo-v5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="superlazycoder/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("superlazycoder/vit-base-beans-demo-v5") model = AutoModelForImageClassification.from_pretrained("superlazycoder/vit-base-beans-demo-v5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 084266c63bee6394e8ade5f9d3a3b2880d4de07c5000998b172af56fdaa74789
- Size of remote file:
- 4.6 kB
- SHA256:
- 5ba1ed934a4444cf41b53723e21fc1de561d3d53fb8f2d9227fa8d2c1b8e7426
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