Instructions to use prithivMLmods/NailbitingNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/NailbitingNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/NailbitingNet") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/NailbitingNet") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/NailbitingNet") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1b1ab3c9da572db8a9793289668beba1f460517071f6063cfedec1e52ac7b9e8
- Size of remote file:
- 372 MB
- SHA256:
- 9e60b72a9ca548a7b2207a5618b15766b5533715e777938dbedbfd571dff8400
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