Instructions to use HedronCreeper/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HedronCreeper/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="HedronCreeper/results") 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("HedronCreeper/results") model = AutoModelForImageClassification.from_pretrained("HedronCreeper/results") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 530b18f00533665c3a6db3c5c9e18563f0e8f0f58693cea7600c8e698a19df1e
- Size of remote file:
- 5.14 kB
- SHA256:
- 1c0ba807516f9b45dd09fdf6487a107162b29f0debf6e58e62dc3373ef1b4617
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