Instructions to use chandra1976/vit-facial-expression-fatigue with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chandra1976/vit-facial-expression-fatigue with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="chandra1976/vit-facial-expression-fatigue") 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("chandra1976/vit-facial-expression-fatigue") model = AutoModelForImageClassification.from_pretrained("chandra1976/vit-facial-expression-fatigue", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4cf433e2d7494f41ed4a5420f64ee390af687937128d07ad5a7c3560de829cd7
- Size of remote file:
- 343 MB
- SHA256:
- 7537d7520a98601349bd1220b031016985031fa495587a7c580253c3d23cfb9a
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