Update README.md

README.md

@@ -2,7 +2,20 @@
 license: apache-2.0
 datasets:
 - jonathan-roberts1/GID
+language:
+- en
+base_model:
+- google/siglip2-base-patch16-224
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- Gaofen-Image-Dataset
+- Land-Cover-Classification
+- Remote-Sensing-Images
 ---
+# **GiD-Land-Cover-Classification**
+
+> **GiD-Land-Cover-Classification** is a multi-class image classification model based on `google/siglip2-base-patch16-224`, trained to detect **land cover types** in geographical or environmental imagery. This model can be used for **urban planning**, **agriculture monitoring**, and **environmental analysis**.
 
 ```py
 Classification Report:
@@ -31,3 +44,103 @@ artificial grassland     0.9173    0.9425    0.9297      2000
 
 ![Untitled.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/09-cU54xSrM97DKD66LeU.png)
 
+
+---
+
+## **Label Classes**
+
+The model distinguishes between the following land cover types:
+
+```
+0: arbor woodland  
+1: artificial grassland  
+2: dry cropland  
+3: garden plot  
+4: industrial land  
+5: irrigated land  
+6: lake  
+7: natural grassland  
+8: paddy field  
+9: pond  
+10: river  
+11: rural residential  
+12: shrub land  
+13: traffic land  
+14: urban residential
+```
+
+---
+
+## **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/GiD-Land-Cover-Classification"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# ID to label mapping
+id2label = {
+    "0": "arbor woodland",
+    "1": "artificial grassland",
+    "2": "dry cropland",
+    "3": "garden plot",
+    "4": "industrial land",
+    "5": "irrigated land",
+    "6": "lake",
+    "7": "natural grassland",
+    "8": "paddy field",
+    "9": "pond",
+    "10": "river",
+    "11": "rural residential",
+    "12": "shrub land",
+    "13": "traffic land",
+    "14": "urban residential"
+}
+
+def detect_land_cover(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=detect_land_cover,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(num_top_classes=5, label="Land Cover Type"),
+    title="GiD-Land-Cover-Classification",
+    description="Upload an image to classify its land cover type: arbor woodland, dry cropland, lake, river, traffic land, etc."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+## **Applications**
+
+* **Urban Development Planning**
+* **Agricultural Monitoring**
+* **Land Use and Land Cover (LULC) Mapping**
+* **Disaster Management and Flood Risk Analysis**