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MetaCLIP-2-Gender-Identifier

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.

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

👁 download

The model categorizes images into two gender classes:

• Class 0: "female"
• Class 1: "male"

Run with Transformers

!pip install -q transformers torch pillow gradio

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.

Base model