README.md

metadata

license: apache-2.0
datasets:
  - prithivMLmods/Realistic-Portrait-Gender-1024px
language:
  - en
base_model:
  - google/siglip2-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
  - Gender
  - Classification
  - art
  - realism
  - portrait
  - Male
  - Female
  - SigLIP2

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