Gender-Classifier-Mini: Model Card

Gender-Classifier-Mini is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for a single-label classification task. It is designed to classify images based on gender using the SiglipForImageClassification architecture.

Accuracy: 0.9720
F1 Score: 0.9720

Classification Report:
precision recall f1-score support

Female 0.9660 0.9796 0.9727 2549
Male 0.9785 0.9641 0.9712 2451

accuracy 0.9720 5000
macro avg 0.9722 0.9718 0.9720 5000
weighted avg 0.9721 0.9720 0.9720 5000

The model categorizes images into two classes: - Class 0:"Female " -Class 1:"Male "

Run with Transformers

!pip install -q transformers torch pillow gradio

import gradio as gr
from transformers import AutoImageProcessor
from transformers import SiglipForImageClassification
from transformers.image_utils import load_image
from PIL import Image
import torch

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

def gender_classification(image):
"""Predicts gender category for an 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()

labels = {"0": "Female ", "1": "Male "}
predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}

return predictions

# Create Gradio interface
iface = gr.Interface(
fn=gender_classification,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(label="Prediction Scores"),
title="Gender Classification",
description="Upload an image to classify its gender."
)

# Launch the app
if __name__ == "__main__":
iface.launch()

Intended Use:

The Gender-Classifier-Mini model is designed to classify images into gender categories. Potential use cases include:

• Demographic Analysis: Assisting in understanding gender distribution in datasets.
• Face Recognition Systems: Enhancing identity verification processes.
• Marketing & Advertising: Personalizing content based on demographic insights.
• Healthcare & Research: Supporting gender-based analysis in medical imaging.