Gender-Classifier-Mini
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.