Update README.md

README.md

@@ -1,7 +1,25 @@
 ---
 license: apache-2.0
+datasets:
+- prithivMLmods/Age-Classification-Set
+language:
+- en
+base_model:
+- google/siglip2-base-patch16-512
+library_name: transformers
 ---
 
+![3.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/oN-EYfwR1vvdgHkVKAOKP.png)
+
+# open-age-detection
+
+> `open-age-detection` is a vision-language encoder model fine-tuned from `google/siglip2-base-patch16-512` for **multi-class image classification**. It is trained to classify the estimated age group of a person from an image. The model uses the `SiglipForImageClassification` architecture.
+
+> \[!note]
+> *SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features*
+> [https://arxiv.org/pdf/2502.14786](https://arxiv.org/pdf/2502.14786)
+
+
 ```py
 Classification Report:
                   precision    recall  f1-score   support
@@ -17,4 +35,88 @@ Middle Age 45-64     0.8532    0.7400    0.7926      3785
     weighted avg     0.8545    0.8583    0.8510     19016
 ```
 
-![download (1).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/W26dIY52YctE7r02CQL90.png)
+![download (1).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/W26dIY52YctE7r02CQL90.png)
+
+---
+
+## Label Space: 5 Age Groups
+
+```
+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+
+```
+
+---
+
+## Install Dependencies
+
+```bash
+pip install -q transformers torch pillow gradio hf_xet
+```
+
+---
+
+## Inference Code
+
+```python
+import gradio as gr
+from transformers import AutoImageProcessor, SiglipForImageClassification
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/open-age-detection"  # Updated model name
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Updated label mapping
+id2label = {
+    "0": "Child 0-12",
+    "1": "Teenager 13-20",
+    "2": "Adult 21-44",
+    "3": "Middle Age 45-64",
+    "4": "Aged 65+"
+}
+
+def classify_image(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()
+
+    prediction = {
+        id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
+    }
+
+    return prediction
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(num_top_classes=5, label="Age Group Detection"),
+    title="open-age-detection",
+    description="Upload a facial image to estimate the age group: Child, Teenager, Adult, Middle Age, or Aged."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+## Intended Use
+
+`open-age-detection` is designed for:
+
+* **Demographic Analysis** – Estimate age groups for statistical or analytical applications.
+* **Smart Personalization** – Age-based content or product recommendation.
+* **Access Control** – Assist systems requiring age verification.
+* **Social Research** – Study age-related trends in image datasets.
+* **Surveillance and Security** – Profile age ranges in monitored environments.