Instructions to use n1hal/Weeds_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use n1hal/Weeds_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="n1hal/Weeds_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("n1hal/Weeds_Classification") model = AutoModelForImageClassification.from_pretrained("n1hal/Weeds_Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 425f8b6ae4e99dca865c1be6b799c999055863372b7f8e694af5d24517530d31
- Size of remote file:
- 344 MB
- SHA256:
- 442c49dff95893d59d30dd89c3741b61d17d1e74203fa627ad39ba3ec5a42ad2
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