Instructions to use Prahas10/shingles with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Prahas10/shingles with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Prahas10/shingles") 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("Prahas10/shingles") model = AutoModelForImageClassification.from_pretrained("Prahas10/shingles", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 43004c1debe320effab15eae84122d28c160a5d8b4405119aed6af0a59fa4ed4
- Size of remote file:
- 345 MB
- SHA256:
- f8178b980e72f1f38e623f8758e30778dc513e298b17e7dd323da60455bf4eca
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