👁 WrD.png

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

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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

👁 Screenshot 2025-05-10 at 17-09-35 Realistic-Gender-Classification.png
👁 Screenshot 2025-05-10 at 17-10-09 Realistic-Gender-Classification.png

male portrait

👁 Screenshot 2025-05-10 at 17-10-48 Realistic-Gender-Classification.png
👁 Screenshot 2025-05-10 at 17-11-39 Realistic-Gender-Classification.png

Applications

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

Base model