README.md

---
license: cc-by-nc-4.0
language:
- en
base_model:
- facebook/metaclip-2-worldwide-s16
pipeline_tag: image-classification
library_name: transformers
tags:
- text-generation-inference
- gender-identifier
---

![1](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Abs7c5gHKnPjZg28GSZGy.png)

# **MetaCLIP-2-Gender-Identifier**

> **MetaCLIP-2-Gender-Identifier** is an image classification vision-language encoder model fine-tuned from **[facebook/metaclip-2-worldwide-s16](https://huggingface.co/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
```

![download](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/SWDn8PT5FxrZixb-Jq0pq.png)

---

The model categorizes images into two gender classes:

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

# **Run with Transformers**

```python
!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](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/eTLAyS2ouGnZDxSwi6HnT.png)
![Screenshot 2025-11-13 at 14-06-43 MetaCLIP-2-Geneder-Identifier](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/ilq2pBgONk4WP3U7dX2YI.png)
![Screenshot 2025-11-13 at 14-08-03 MetaCLIP-2-Geneder-Identifier](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/EXJdYEPJMh7LMPJEoNonT.png)
![Screenshot 2025-11-13 at 14-08-52 MetaCLIP-2-Geneder-Identifier](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/jvYUEQHX_-Eq2JI1pKLcr.png)

# **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.