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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# **Realistic-Gender-Classification**
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> **Realistic-Gender-Classification** is a binary image classification model based on `google/siglip2-base-patch16-224`, designed to classify **gender** from realistic human portrait images. It can be used in **demographic analysis**, **personalization systems**, and **automated tagging** in large-scale image datasets.
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* **Demographic Insights in Visual Data**
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* **Dataset Curation & Tagging**
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* **Media Analytics**
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* **Audience Profiling for Marketing**
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- Female
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# **Realistic-Gender-Classification**
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> **Realistic-Gender-Classification** is a binary image classification model based on `google/siglip2-base-patch16-224`, designed to classify **gender** from realistic human portrait images. It can be used in **demographic analysis**, **personalization systems**, and **automated tagging** in large-scale image datasets.
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* **Demographic Insights in Visual Data**
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* **Dataset Curation & Tagging**
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* **Media Analytics**
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* **Audience Profiling for Marketing**
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