Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use sjdata/vit-base-beans with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sjdata/vit-base-beans with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sjdata/vit-base-beans") 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("sjdata/vit-base-beans") model = AutoModelForImageClassification.from_pretrained("sjdata/vit-base-beans") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b42b9ae949f2bf61e4445a308daae0e486f1f6c432502be77179ec61537252f8
- Size of remote file:
- 3.9 kB
- SHA256:
- ffbca0a496ea05fadc63f3dd9c0e469971fffc8185cef3f1d8b393eb9088295f
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