Instructions to use Suru/FruitModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Suru/FruitModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Suru/FruitModel", 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("Suru/FruitModel") model = AutoModelForImageClassification.from_pretrained("Suru/FruitModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 080eb1d06534a754703fd7340cd69d028a4f35758e1d7cb05afaa301ab67d009
- Size of remote file:
- 343 MB
- SHA256:
- 38e1e0d5cb6775ed3b15639504221c65890813c8c8ceebe20fe6f3ecca1f938c
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