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:
- fc10bdd389482d03b10e73c6c135c8c34792d6cb7b2376dae2d9f2ecb96127b3
- Size of remote file:
- 687 MB
- SHA256:
- 3872c3bca0bcaada8b7c91afab197a88f39c328e8aaded23ce282e996b32aac0
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