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:
- f9ff0683f5a42634fbf11a471fe99db1c02c73a3858c1284128a5f20b7dd047f
- Size of remote file:
- 687 MB
- SHA256:
- b75992c64e9911fe9db9fcaaa016594acd72d5e5b41317cc961e27b87e4bdd7f
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