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:
- ff49adfd9250e53d43898b3257d6d9299074643565dcda1835473efec4430378
- Size of remote file:
- 343 MB
- SHA256:
- 64b6d2b1634d4e5818b3b03ba56b6ff5ea6a172e5705aad392ee633558360106
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