prithivMLmods / Realistic-Gender-Classification

Realistic-Gender-Classification

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.

SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features https://arxiv.org/pdf/2502.14786

Classification Report:
                 precision    recall  f1-score   support

female portrait     0.9754    0.9656    0.9705      1600
  male portrait     0.9660    0.9756    0.9708      1600

       accuracy                         0.9706      3200
      macro avg     0.9707    0.9706    0.9706      3200
   weighted avg     0.9707    0.9706    0.9706      3200

Label Classes

The model distinguishes between the following portrait gender categories:

0: female portrait  
1: male portrait

Installation

pip install transformers torch pillow gradio

Example Inference Code

import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/Realistic-Gender-Classification"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

# ID to label mapping
id2label = {
    "0": "female portrait",
    "1": "male portrait"
}

def classify_gender(image):
    image = Image.fromarray(image).convert("RGB")
    inputs = processor(images=image, return_tensors="pt")

    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()

    prediction = {id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))}
    return prediction

# Gradio Interface
iface = gr.Interface(
    fn=classify_gender,
    inputs=gr.Image(type="numpy"),
    outputs=gr.Label(num_top_classes=2, label="Gender Classification"),
    title="Realistic-Gender-Classification",
    description="Upload a realistic portrait image to classify it as 'female portrait' or 'male portrait'."
)

if __name__ == "__main__":
    iface.launch()

Demo Inference

female portrait

male portrait

Applications

• Demographic Insights in Visual Data
• Dataset Curation & Tagging
• Media Analytics
• Audience Profiling for Marketing