Update README.md

README.md

@@ -3,6 +3,9 @@ license: apache-2.0
 datasets:
 - prithivMLmods/Human-vs-NonHuman
 ---
+# **Human-vs-NonHuman-Detection**  
+
+> **Human-vs-NonHuman-Detection** is an image classification vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for a single-label classification task. It is designed to classify images as either human or non-human using the **SiglipForImageClassification** architecture.  
 
 ```py
 Classification Report:
@@ -16,4 +19,68 @@ Classification Report:
 weighted avg     0.9863    0.9862    0.9862     15635
 ```
 
-![download (1).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/ToGf2iWUKacTCQQn9hRPD.png)
+![download (1).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/ToGf2iWUKacTCQQn9hRPD.png) 
+
+The model categorizes images into two classes:  
+- **Class 0:** "Human 𖨆"  
+- **Class 1:** "Non Human メ"  
+
+# **Run with Transformers🤗**  
+
+```python
+!pip install -q transformers torch pillow gradio
+```
+
+```python
+import gradio as gr
+from transformers import AutoImageProcessor
+from transformers import SiglipForImageClassification
+from transformers.image_utils import load_image
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/Human-vs-NonHuman-Detection"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def human_detection(image):
+    """Predicts whether the image contains a human or non-human entity."""
+    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()
+    
+    labels = {
+        "0": "Human 𖨆", 
+        "1": "Non Human メ"
+    }
+    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=human_detection,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="Human vs Non-Human Detection",
+    description="Upload an image to classify whether it contains a human or non-human entity."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```  
+
+# **Intended Use:**  
+
+The **Human-vs-NonHuman-Detection** model is designed to distinguish between human and non-human entities. Potential use cases include:  
+
+- **Surveillance & Security:** Enhancing monitoring systems to detect human presence.  
+- **Autonomous Systems:** Helping robots and AI systems identify humans.  
+- **Image Filtering:** Automatically categorizing human vs. non-human images.  
+- **Smart Access Control:** Identifying human presence for secure authentication.