Update README.md

README.md

@@ -2,8 +2,24 @@
 license: apache-2.0
 datasets:
 - jonathan-roberts1/RSI-CB256
+language:
+- en
+base_model:
+- google/siglip-base-patch16-224
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- Remote Sensing Instruments
+- RSI
+- Location
 ---
 
+![4.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/6wc37VzmAH_l6nJ2YmV_D.png)
+
+# **RSI-CB256-07**
+
+> **RSI-CB256-07** is a SigLIP2-based model fine-tuned for **coarse-grained remote sensing land-cover classification**. It distinguishes among 7 essential categories commonly used in environmental, urban planning, and geospatial analysis applications. The model is built on `google/siglip2-base-patch16-256` using the `SiglipForImageClassification` architecture.
+
 ```py
 Classification Report:
                    precision    recall  f1-score   support
@@ -22,3 +38,90 @@ construction land     0.9945    0.9963    0.9954      3791
 ```
 
 ![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/TK92T1T135eFWucPmTGqw.png)
+
+---
+
+## **Label Space: 7 Remote Sensing Classes**
+
+This model predicts one of the following categories for a given satellite or aerial image:
+
+```
+Class 0:  "transportation"  
+Class 1:  "other objects"  
+Class 2:  "woodland"  
+Class 3:  "water area"  
+Class 4:  "other land"  
+Class 5:  "cultivated land"  
+Class 6:  "construction land"
+```
+
+---
+
+## **Install Dependencies**
+
+```bash
+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/RSI-CB256-07"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# ID to label mapping
+id2label = {
+    "0": "transportation",
+    "1": "other objects",
+    "2": "woodland",
+    "3": "water area",
+    "4": "other land",
+    "5": "cultivated land",
+    "6": "construction land"
+}
+
+def classify_rsi_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_rsi_image,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(num_top_classes=7, label="Predicted Land-Cover Category"),
+    title="RSI-CB256-07",
+    description="Upload a satellite or aerial image to classify it into one of seven coarse land-cover classes using SigLIP2."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+## **Applications**
+
+* **Urban vs Rural Segmentation**
+* **Land-Use Classification**
+* **National/Regional Land Cover Monitoring**
+* **Environmental Impact Assessment**