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:
- c4fda31e09a555ae4bd3320737a20be86e7e592d3b525bba306c6ba2fadcd4d2
- Size of remote file:
- 343 MB
- SHA256:
- 97b6c3d88e8d3085aa2549ce51f09dd729afea61390640084e55c89de803cbe1
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