Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use chbh7051/driver-drowsiness-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chbh7051/driver-drowsiness-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="chbh7051/driver-drowsiness-detection") 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("chbh7051/driver-drowsiness-detection") model = AutoModelForImageClassification.from_pretrained("chbh7051/driver-drowsiness-detection") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d838801826f2e4965971cb9e9f563120de6e5f81a6d01d4d9e792fdf5612a471
- Size of remote file:
- 3.58 kB
- SHA256:
- 1cad04b0c6a96cd08baab81982776f9dce70a4a1cc0b1d7c57cee1cb70397666
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