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", device_map="auto") 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
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license: apache-2.0
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datasets:
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- alecsharpie/nailbiting_classification
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license: apache-2.0
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datasets:
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- alecsharpie/nailbiting_classification
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---
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```py
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Classification Report:
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precision recall f1-score support
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biting 0.8412 0.9076 0.8731 2824
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no biting 0.9271 0.8728 0.8991 3805
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accuracy 0.8876 6629
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macro avg 0.8841 0.8902 0.8861 6629
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weighted avg 0.8905 0.8876 0.8881 6629
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```
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