Instructions to use Muqtadar08/skin_type_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Muqtadar08/skin_type_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Muqtadar08/skin_type_classifier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Muqtadar08/skin_type_classifier") model = AutoModelForImageClassification.from_pretrained("Muqtadar08/skin_type_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Muqtadar08/skin_type_classifier: direct link, hf CLI and curl.
- Browser
- Download file 737 Bytes
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https://huggingface.co/Muqtadar08/skin_type_classifier/resolve/main/README.md
- Command line
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hf download hf://Muqtadar08/skin_type_classifier/README.md
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curl -L -o README.md https://huggingface.co/Muqtadar08/skin_type_classifier/resolve/main/README.md
737 Bytes
metadata
license: apache-2.0
metrics:
- accuracy
- f1
Detects the skin type (dry, normal, oily) based on facial image.
See https://www.kaggle.com/code/dima806/skin-types-image-detection-vit for details.
Classification report:
precision recall f1-score support
dry 0.6471 0.5402 0.5888 224
normal 0.6031 0.7054 0.6502 224
oily 0.6054 0.6027 0.6040 224
accuracy 0.6161 672
macro avg 0.6185 0.6161 0.6143 672
weighted avg 0.6185 0.6161 0.6143 672
