Image Classification
Transformers
Safetensors
English
siglip
Gender
Classification
art
realism
portrait
Male
Female
SigLIP2
Instructions to use prithivMLmods/Realistic-Gender-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Realistic-Gender-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Realistic-Gender-Classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/Realistic-Gender-Classification") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Realistic-Gender-Classification") - Notebooks
- Google Colab
- Kaggle
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- SigLIP2
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- SigLIP2
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---
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```py
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Classification Report:
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precision recall f1-score support
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female portrait 0.9754 0.9656 0.9705 1600
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male portrait 0.9660 0.9756 0.9708 1600
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accuracy 0.9706 3200
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macro avg 0.9707 0.9706 0.9706 3200
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weighted avg 0.9707 0.9706 0.9706 3200
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```
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