Update README.md

README.md

@@ -1,6 +1,10 @@
 ---
 license: apache-2.0
 ---
+# **SAT-Landforms-Classifier**
+
+> **SAT-Landforms-Classifier** 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 satellite images into different landform categories using the **SiglipForImageClassification** architecture.  
+
 ```py
 Accuracy: 0.9863
 F1 Score: 0.9858
@@ -25,3 +29,76 @@ Herbaceous Vegetation     0.9697    0.9800    0.9748      3000
 ```
 
 ![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Vt95rKi7pcP_6mV9fkIkS.png)
+
+The model categorizes images into ten classes:
+- **Class 0:** "Annual Crop"
+- **Class 1:** "Forest"
+- **Class 2:** "Herbaceous Vegetation"
+- **Class 3:** "Highway"
+- **Class 4:** "Industrial"
+- **Class 5:** "Pasture"
+- **Class 6:** "Permanent Crop"
+- **Class 7:** "Residential"
+- **Class 8:** "River"
+- **Class 9:** "Sea Lake"
+
+# **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/SAT-Landforms-Classifier"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def landform_classification(image):
+    """Predicts landform category for a satellite 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()
+    
+    labels = {
+        "0": "Annual Crop", "1": "Forest", "2": "Herbaceous Vegetation", "3": "Highway", "4": "Industrial", 
+        "5": "Pasture", "6": "Permanent Crop", "7": "Residential", "8": "River", "9": "Sea Lake"
+    }
+    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=landform_classification,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="SAT Landforms Classification",
+    description="Upload a satellite image to classify its landform type."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+# **Intended Use:**  
+
+The **SAT-Landforms-Classifier** model is designed to classify satellite images into various landform types. Potential use cases include:  
+
+- **Land Use Monitoring:** Identifying different land use patterns from satellite imagery.
+- **Environmental Studies:** Supporting researchers in tracking changes in vegetation and water bodies.
+- **Urban Planning:** Assisting planners in analyzing residential, industrial, and infrastructure distributions.
+- **Agricultural Analysis:** Helping assess crop distribution and pastureland areas.
+- **Disaster Management:** Providing insights into land coverage for emergency response and recovery planning.