Instructions to use dima806/faces_age_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/faces_age_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dima806/faces_age_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("dima806/faces_age_detection") model = AutoModelForImageClassification.from_pretrained("dima806/faces_age_detection") - Notebooks
- Google Colab
- Kaggle
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metrics:
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- accuracy
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- f1
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Returns age group with about 91% accuracy based on facial image.
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metrics:
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- accuracy
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- f1
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base_model:
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- google/vit-base-patch16-224-in21k
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Returns age group with about 91% accuracy based on facial image.
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