Instructions to use dima806/skin_types_image_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/skin_types_image_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dima806/skin_types_image_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/skin_types_image_detection") model = AutoModelForImageClassification.from_pretrained("dima806/skin_types_image_detection") - Notebooks
- Google Colab
- Kaggle
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 0.6829 0.6346 0.6578 509
normal 0.6414 0.6314 0.6364 510
oily 0.6390 0.6941 0.6654 510
accuracy 0.6534 1529
macro avg 0.6544 0.6534 0.6532 1529
weighted avg 0.6544 0.6534 0.6532 1529
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Model tree for dima806/skin_types_image_detection
Base model
google/vit-base-patch16-224-in21k