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:
- d838d44f421197f147bcef05c501ee1e3da5557bab657d19f5f0c28c5d85a3e2
- Size of remote file:
- 5.84 kB
- SHA256:
- 5fecb47c4aaeda5d85991e40a0314eb16c5abd59a34182c123d9686753034dcf
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