Instructions to use Mirkat/Plant_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mirkat/Plant_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Mirkat/Plant_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("Mirkat/Plant_Classification") model = AutoModelForImageClassification.from_pretrained("Mirkat/Plant_Classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ce3734e001c0bf20d33d657f1f1eb6a5361b4658bfd39cc5a975c8684fd16bcd
- Size of remote file:
- 627 Bytes
- SHA256:
- 2ed8a156a22c309bb91c33d910110b63f1c190e1e24dadfe22b621d95df2fa8a
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