Image Classification
Transformers
PyTorch
TensorBoard
English
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use nateraw/vit-base-beans with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nateraw/vit-base-beans with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nateraw/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("nateraw/vit-base-beans") model = AutoModelForImageClassification.from_pretrained("nateraw/vit-base-beans") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 59f68a04c72aee5d26421be2f4f0fc5256362fe87c3b89735d9d153d497a6a54
- Size of remote file:
- 343 MB
- SHA256:
- fc443b145fcc3a09cf07eb28b94e9a989ec3f0e8f7255e1fa08c0953ed4bae91
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