Instructions to use trpakov/vit-face-expression with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use trpakov/vit-face-expression with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="trpakov/vit-face-expression") 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("trpakov/vit-face-expression") model = AutoModelForImageClassification.from_pretrained("trpakov/vit-face-expression") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 727cac6dd6d71809ee4ae456a239441e9706c9103ab8971bfeb01b636afd9eca
- Size of remote file:
- 343 MB
- SHA256:
- 17268e15dabb64cd6751b63abb51ac860231374b747f68fbf12a2815c80dd9ef
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