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:
- 7078ebae019f2fe446bc3d54f78431e58eeb0654128135a580ace22bba58eb15
- Size of remote file:
- 343 MB
- SHA256:
- 694c68384155c2cc27f8d5191b810c85e460d6d9c089ac52ae3a6337c88f37d1
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