Instructions to use driboune/skin_type with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use driboune/skin_type with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="driboune/skin_type") 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("driboune/skin_type") model = AutoModelForImageClassification.from_pretrained("driboune/skin_type", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from driboune/skin_type: direct link, hf CLI and curl.
- Browser
- Download file 912 Bytes
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https://huggingface.co/driboune/skin_type/resolve/main/README.md
- Command line
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hf download hf://driboune/skin_type/README.md
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curl -L -o README.md https://huggingface.co/driboune/skin_type/resolve/main/README.md
912 Bytes
metadata
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: skin_type
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8222222328186035
skin_type
Aiming for fairness in image classification for humans, knowing the skin type of subjects is relevant to make sure the model performs correctly on all skin types.
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.

