Instructions to use HardlyHumans/Facial-expression-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HardlyHumans/Facial-expression-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="HardlyHumans/Facial-expression-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("HardlyHumans/Facial-expression-detection") model = AutoModelForImageClassification.from_pretrained("HardlyHumans/Facial-expression-detection") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6cde6706625fa408f15047cf4a7de0151103d24bbba0c45012c1143353c76164
- Size of remote file:
- 343 MB
- SHA256:
- 95763e110ed7cadd1e63dc3d10a9408fda56910b9b4248c6f745687558e930fe
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