Update README.md

README.md

@@ -18,6 +18,9 @@ tags:
 - Female
 - SigLIP2
 ---
+# **Realistic-Gender-Classification**
+
+> **Realistic-Gender-Classification** is a binary image classification model based on `google/siglip2-base-patch16-224`, designed to classify **gender** from realistic human portrait images. It can be used in **demographic analysis**, **personalization systems**, and **automated tagging** in large-scale image datasets.
 
 ```py
 Classification Report:
@@ -32,3 +35,77 @@ female portrait     0.9754    0.9656    0.9705      1600
 ```
 
 ![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Hl1qDGrIIZyiSOzOX8K8t.png)
+
+---
+
+## **Label Classes**
+
+The model distinguishes between the following portrait gender categories:
+
+```
+0: female portrait  
+1: male portrait
+```
+
+---
+
+## **Installation**
+
+```bash
+pip install transformers torch pillow gradio
+```
+
+---
+
+## **Example 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/Realistic-Gender-Classification"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# ID to label mapping
+id2label = {
+    "0": "female portrait",
+    "1": "male portrait"
+}
+
+def classify_gender(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_gender,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(num_top_classes=2, label="Gender Classification"),
+    title="Realistic-Gender-Classification",
+    description="Upload a realistic portrait image to classify it as 'female portrait' or 'male portrait'."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+## **Applications**
+
+* **Demographic Insights in Visual Data**
+* **Dataset Curation & Tagging**
+* **Media Analytics**
+* **Audience Profiling for Marketing**