Instructions to use bazudde/potato_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bazudde/potato_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="bazudde/potato_model") 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("bazudde/potato_model") model = AutoModelForImageClassification.from_pretrained("bazudde/potato_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 735a14f61910082206244cf3d957acfb53e6a34fc317bea64dc24960d85ef372
- Size of remote file:
- 347 MB
- SHA256:
- 5dd4709bc4830c1f06f9195ab1eb7f8708d0578cd33a86185419e95a46e48aec
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