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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 99f6f414f051dec5b09d7a743b2a909ec918331e9ff03d849964585b8ba2dbde
- Size of remote file:
- 627 Bytes
- SHA256:
- 9e9d1478932d95f604c248a1fe1362a1d07bf90d5ca17e1579a67752b11f2fc2
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