Update README.md

README.md

@@ -3,7 +3,20 @@ license: apache-2.0
 datasets:
 - prithivMLmods/IndoorOutdoorNet-20K
 library_name: transformers
+language:
+- en
+base_model:
+- google/siglip2-base-patch16-224
+pipeline_tag: image-classification
+tags:
+- Indoor
+- Outdoor
+- Classification
+- SigLIP2
 ---
+# **IndoorOutdoorNet**
+
+> **IndoorOutdoorNet** 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 **Indoor** or **Outdoor** using the **SiglipForImageClassification** architecture.
 
 ```py
 Classification Report:
@@ -17,4 +30,78 @@ Classification Report:
 weighted avg     0.9610    0.9609    0.9609     19998
 ```
 
-![download (1).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/wLvX04YPoU2OsDjKBDKXU.png)
+![download (1).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/wLvX04YPoU2OsDjKBDKXU.png)
+
+
+---
+
+The model categorizes images into 2 environment-related classes:
+
+```
+    Class 0: "Indoor"
+    Class 1: "Outdoor"
+```
+
+---
+
+## **Install dependencies**
+
+```python
+!pip install -q transformers torch pillow gradio
+```
+
+---
+
+## **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/IndoorOutdoorNet"  # Updated model name
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def classify_environment_image(image):
+    """Predicts whether an image is Indoor or Outdoor."""
+    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": "Indoor", "1": "Outdoor"
+    }
+    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=classify_environment_image,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="IndoorOutdoorNet",
+    description="Upload an image to classify it as Indoor or Outdoor."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+## **Intended Use:**
+
+The **IndoorOutdoorNet** model is designed to classify images into indoor or outdoor environments. Potential use cases include:
+
+- **Smart Cameras:** Detect indoor/outdoor context to adjust settings.
+- **Dataset Curation:** Automatically filter image datasets by setting.
+- **Robotics & Drones:** Environment-aware navigation logic.
+- **Content Filtering:** Moderate or tag environment context in image platforms.