Update README.md

README.md

@@ -11,6 +11,16 @@ tags:
 - Age-Range-Estimator
 ---
 
+![1](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/3lZzKyjG6fz-ArZwSh__B.png)
+
+# **MetaCLIP-2-Age-Range-Estimator**
+
+> **MetaCLIP-2-Age-Range-Estimator** 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 age range 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
@@ -26,4 +36,91 @@ Middle Age 45-64     0.9458    0.9450    0.9454      3785
     weighted avg     0.9557    0.9559    0.9556     19016
 ```
 
-![download](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Qm87Eex4rqSFoTw2H_Nog.png)
+![download](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Qm87Eex4rqSFoTw2H_Nog.png)
+
+---
+
+The model categorizes images into five age ranges:
+
+* **Class 0:** "Child 0-12"
+* **Class 1:** "Teenager 13-20"
+* **Class 2:** "Adult 21-44"
+* **Class 3:** "Middle Age 45-64"
+* **Class 4:** "Aged 65+"
+
+---
+
+# **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-Age-Range-Estimator"
+
+# Load processor and model
+processor = AutoImageProcessor.from_pretrained(model_name)
+model = AutoModelForImageClassification.from_pretrained(model_name)
+model.eval()
+
+# Define labels
+LABELS = {
+    0: "Child (0–12)",
+    1: "Teenager (13–20)",
+    2: "Adult (21–44)",
+    3: "Middle Age (45–64)",
+    4: "Aged (65+)"
+}
+
+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 Age Group Probabilities"),
+    title="MetaCLIP-2 Age Range Estimator",
+    description="Upload a face image to estimate the person's age group using MetaCLIP-2."
+)
+
+# Launch app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+# **Sample Inference:**
+
+![Screenshot 2025-11-13 at 01-14-28 MetaCLIP-2 Age Range Estimator](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/5SUHT4ZeKlWEM2smB1dd0.png)
+![Screenshot 2025-11-13 at 01-15-41 MetaCLIP-2 Age Range Estimator](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/cQT5GtchFCDnlu79AG0BR.png)
+![Screenshot 2025-11-13 at 01-17-31 MetaCLIP-2 Age Range Estimator](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/qxoEmFliB1KCDjXhhW25H.png)
+![Screenshot 2025-11-13 at 01-18-15 MetaCLIP-2 Age Range Estimator](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Xnsa49OVCqm600S2ifFFy.png)
+![Screenshot 2025-11-13 at 01-18-52 MetaCLIP-2 Age Range Estimator](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/JHUnt0UP1uYKJdUpjJAGE.png)
+
+# **Intended Use:**
+
+The **MetaCLIP-2-Age-Range-Estimator** model is designed to classify images into five age categories.
+Potential use cases include:
+
+* **Demographic Analysis:** Supporting research and business insights into age distribution.
+* **Health and Fitness Applications:** Assisting in age-based health recommendations.
+* **Security and Access Control:** Enabling age verification systems.
+* **Retail and Marketing:** Enhancing personalization and customer profiling.
+* **Forensics and Surveillance:** Supporting age estimation in investigative and security contexts.