Instructions to use NatSquared/extended-gender-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NatSquared/extended-gender-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="NatSquared/extended-gender-classifier") 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("NatSquared/extended-gender-classifier") model = AutoModelForImageClassification.from_pretrained("NatSquared/extended-gender-classifier") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("NatSquared/extended-gender-classifier")
model = AutoModelForImageClassification.from_pretrained("NatSquared/extended-gender-classifier")Quick Links
extended-gender-classifier
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.
Example Images
female
male
non-binary person
- Downloads last month
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Evaluation results
- Accuracyself-reported0.896



# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="NatSquared/extended-gender-classifier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")