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:
- 79271d19dca55c5aa6a4a18badb908ee5a12fc4cc17a0da8c491bf9bd2fdecaf
- Size of remote file:
- 3.52 kB
- SHA256:
- 9ba37fcd1acea0b45f51a95d7f2fad46fb559fdcb0ae53b50ff96273b810b552
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