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") 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:
- e89e4ddd3f23f23b68b98decb7c8c6c92318f042b850e794599219995444b87f
- Size of remote file:
- 343 MB
- SHA256:
- 46c39faf5de4ab33a59960a6a07e3c50138aa74317fb7146597b04bb0403c360
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