MetaCLIP-2-Gender-Identifier is an image classification vision-language encoder model fine-tuned from facebook/metaclip-2-worldwide-s16 for a single-label classification task. It is designed to predict the gender of a person from an image using the MetaClip2ForImageClassification architecture.

[!note] MetaCLIP 2: A Worldwide Scaling Recipe : https://huggingface.co/papers/2507.22062

Classification Report:
              precision    recall  f1-score   support

      female     0.9815    0.9631    0.9722      1600
        male     0.9638    0.9819    0.9728      1600

    accuracy                         0.9725      3200
   macro avg     0.9727    0.9725    0.9725      3200
weighted avg     0.9727    0.9725    0.9725      3200

The model categorizes images into two gender classes:

Class 0: "female"
Class 1: "male"

Run with Transformers

!pip install -q transformers torch pillow gradio

python

import gradio as gr
import torch
from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image

# Model name from Hugging Face Hub
model_name = "prithivMLmods/MetaCLIP-2-Gender-Identifier"

# Load processor and model
processor = AutoImageProcessor.from_pretrained(model_name)
model = AutoModelForImageClassification.from_pretrained(model_name)
model.eval()

# Define labels
LABELS = {
    0: "female",
    1: "male"
}

def age_classification(image):
    """Predict the age group of a person from 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()

    predictions = {LABELS[i]: round(probs[i], 3) for i in range(len(probs))}
    return predictions

# Build Gradio interface
iface = gr.Interface(
    fn=age_classification,
    inputs=gr.Image(type="numpy", label="Upload Image"),
    outputs=gr.Label(label="Predicted Gender"),
    title="MetaCLIP-2-Gender-Identifier",
    description="Upload an image to predict the person's gender."
)

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

Sample Inference:

Screenshot 2025-11-13 at 14-09-26 MetaCLIP-2-Geneder-Identifier

Screenshot 2025-11-13 at 14-06-43 MetaCLIP-2-Geneder-Identifier

Screenshot 2025-11-13 at 14-08-03 MetaCLIP-2-Geneder-Identifier

Screenshot 2025-11-13 at 14-08-52 MetaCLIP-2-Geneder-Identifier

Intended Use:

The MetaCLIP-2-Gender-Identifier model is designed to classify images into gender categories. Potential use cases include:

Demographic Analysis: Supporting research and business insights into gender-based distribution.
Health and Fitness Applications: Assisting in gender-specific analytics and recommendations.
Security and Access Control: Supporting gender-based identity verification systems.
Retail and Marketing: Enabling improved personalization and customer segmentation.
Forensics and Surveillance: Assisting in identity estimation for investigative purposes.