Instructions to use mmervecerit/vit-base-beans-tutorial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mmervecerit/vit-base-beans-tutorial with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mmervecerit/vit-base-beans-tutorial", device_map="auto") 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("mmervecerit/vit-base-beans-tutorial") model = AutoModelForImageClassification.from_pretrained("mmervecerit/vit-base-beans-tutorial", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 660299af2e9b6c9afb4b0656c21b29c1fd5bdbfad41c5aad92686a2d2e70c2b1
- Size of remote file:
- 343 MB
- SHA256:
- 3248b4ee810091ece3e016626da3464ceaa30b3ef61490381a5f985e5defffcd
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