Instructions to use flatmoon102/fruits_and_vegetables_image_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flatmoon102/fruits_and_vegetables_image_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="flatmoon102/fruits_and_vegetables_image_classification") 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("flatmoon102/fruits_and_vegetables_image_classification") model = AutoModelForImageClassification.from_pretrained("flatmoon102/fruits_and_vegetables_image_classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 948b48720fc74a20834461b584cc0ddac93d6702b1169eb33b690662591254db
- Size of remote file:
- 4.16 kB
- SHA256:
- 650089d8c50f0afc0ca3fa0b4493a0e1099bac86e6ca2415caf1d6198d65f4f5
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