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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 58838d29db11c70946637ca204e480cd021e05c7e2af9c9d263c549abb057e8c
- Size of remote file:
- 687 MB
- SHA256:
- 0e1477880d5768ebc9a43cd4d2feae1d3d52df7b3baea3632d621c23ae4f902f
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