👁 2.png

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

👁 Untitled.png

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.

Base model

google/siglip2-base-patch16-224

(119)

vit, siglip • 7 items • Updated 1 day ago • 1

Age, Gender, Race and More .. • 10 items • Updated 1 day ago • 2