Instructions to use BeckerAnas/hardy-bee-220 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BeckerAnas/hardy-bee-220 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="BeckerAnas/hardy-bee-220") 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("BeckerAnas/hardy-bee-220") model = AutoModelForImageClassification.from_pretrained("BeckerAnas/hardy-bee-220", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2cf231e55401722c4abdcec7f4d7e2224c690530769768fe78bd28ab7b9912da
- Size of remote file:
- 5.71 kB
- SHA256:
- dc67440ede84bd094e9deae23498fd5ad550522e8a64a84ec69bd5c9fd688812
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