Update README.md

README.md

@@ -19,6 +19,10 @@ tags:
 - street
 ---
 
+# open-scene-detection
+
+> open-scene-detection is a vision-language encoder model fine-tuned from [`siglip2-base-patch16-512`](https://huggingface.co/google/siglip-base-patch16-512) for multi-class scene classification. It is trained to recognize and categorize natural and urban scenes using a curated visual dataset. The model uses the `SiglipForImageClassification` architecture.
+
 ```py
 Classification Report:
               precision    recall  f1-score   support
@@ -36,3 +40,91 @@ weighted avg     0.9729    0.9728    0.9728     16345
 ```
 
 ![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/oqlb8a1p6zJuNZSI9PgZO.png)
+
+---
+
+## Label Space: 6 Classes
+
+The model classifies an image into one of the following scenes:
+
+```
+Class 0: Buildings  
+Class 1: Forest  
+Class 2: Glacier  
+Class 3: Mountain  
+Class 4: Sea  
+Class 5: Street
+```
+
+---
+
+## 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-scene-detection"  # Updated model name
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Updated label mapping
+id2label = {
+    "0": "Buildings",
+    "1": "Forest",
+    "2": "Glacier",
+    "3": "Mountain",
+    "4": "Sea",
+    "5": "Street"
+}
+
+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=6, label="Scene Classification"),
+    title="open-scene-detection",
+    description="Upload an image to classify the scene into one of six categories: Buildings, Forest, Glacier, Mountain, Sea, or Street."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+## Intended Use
+
+`open-scene-detection` is designed for:
+
+* **Scene Recognition** – Automatically classify natural and urban scenes.
+* **Environmental Mapping** – Support geographic and ecological analysis from visual data.
+* **Dataset Annotation** – Efficiently label large-scale image datasets by scene.
+* **Visual Search and Organization** – Enable smart scene-based filtering or retrieval.
+* **Autonomous Systems** – Assist navigation and perception modules with scene understanding.