Update README.md

README.md

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-license: cc-by-nc-4.0
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+---
+license: cc-by-nc-4.0
+---
+
+Here’s the fully updated and polished version for your new model **MetaCLIP-2-Gender-Identifier**, with correct label mapping, title, and consistent formatting — ready for use as a Hugging Face model card (`README.md`):
+
+---
+
+# **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-Geneder-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-Geneder-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 14-06-43 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)
+![Screenshot 2025-11-13 at 14-09-26 MetaCLIP-2-Geneder-Identifier](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/eTLAyS2ouGnZDxSwi6HnT.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.