Update README.md

README.md

@@ -1,8 +1,27 @@
 ---
 datasets:
 - marcelomoreno26/geoguessr
+license: apache-2.0
+language:
+- en
+base_model:
+- google/siglip2-base-patch16-224
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- GeoGuessr
+- '55'
+- Loaction
+- RSI
+- Remote Sensing Instruments
 ---
 
+![Geo.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/v_A_Yi9DKdol7cHmX-G5a.png)
+
+# **GeoGuessr-55**
+
+> **GeoGuessr-55** is a visual geolocation classification model that predicts the **country** from a single image. Based on the `SigLIP2` architecture, this model can classify images into one of **55 countries** using visual features such as landscapes, signs, vegetation, and architecture. It is useful for location-based games, geographic AI research, and image-based country inference.
+
 ```py
 Classification Report:
                 precision    recall  f1-score   support
@@ -66,4 +85,99 @@ United Kingdom     0.6792    0.8746    0.7646      1738
       accuracy                         0.6485     25160
      macro avg     0.4944    0.3713    0.3836     25160
   weighted avg     0.6147    0.6485    0.6106     25160
-```
+```
+
+---
+
+## **Label Classes**
+
+The model classifies an image into one of the following 55 countries:
+
+```
+0: Argentina         1: Australia         2: Austria           3: Bangladesh  
+4: Belgium           5: Bolivia           6: Botswana          7: Brazil  
+8: Bulgaria          9: Cambodia         10: Canada           11: Chile  
+12: Colombia        13: Croatia          14: Czechia          15: Denmark  
+16: Finland         17: France           18: Germany          19: Ghana  
+20: Greece          21: Hungary          22: India            23: Indonesia  
+24: Ireland         25: Israel           26: Italy            27: Japan  
+28: Kenya           29: Latvia           30: Lithuania        31: Malaysia  
+32: Mexico          33: Netherlands      34: New Zealand      35: Nigeria  
+36: Norway          37: Peru             38: Philippines      39: Poland  
+40: Portugal        41: Romania          42: Russia           43: Singapore  
+44: Slovakia        45: South Africa     46: South Korea      47: Spain  
+48: Sweden          49: Switzerland      50: Taiwan           51: Thailand  
+52: Turkey          53: Ukraine          54: United Kingdom
+```
+
+---
+
+## **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/GeoGuessr-55"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# ID to label mapping
+id2label = {
+    "0": "Argentina", "1": "Australia", "2": "Austria", "3": "Bangladesh", "4": "Belgium",
+    "5": "Bolivia", "6": "Botswana", "7": "Brazil", "8": "Bulgaria", "9": "Cambodia",
+    "10": "Canada", "11": "Chile", "12": "Colombia", "13": "Croatia", "14": "Czechia",
+    "15": "Denmark", "16": "Finland", "17": "France", "18": "Germany", "19": "Ghana",
+    "20": "Greece", "21": "Hungary", "22": "India", "23": "Indonesia", "24": "Ireland",
+    "25": "Israel", "26": "Italy", "27": "Japan", "28": "Kenya", "29": "Latvia",
+    "30": "Lithuania", "31": "Malaysia", "32": "Mexico", "33": "Netherlands",
+    "34": "New Zealand", "35": "Nigeria", "36": "Norway", "37": "Peru", "38": "Philippines",
+    "39": "Poland", "40": "Portugal", "41": "Romania", "42": "Russia", "43": "Singapore",
+    "44": "Slovakia", "45": "South Africa", "46": "South Korea", "47": "Spain", "48": "Sweden",
+    "49": "Switzerland", "50": "Taiwan", "51": "Thailand", "52": "Turkey", "53": "Ukraine",
+    "54": "United Kingdom"
+}
+
+def classify_country(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()
+
+    return {id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+
+# Launch Gradio demo
+iface = gr.Interface(
+    fn=classify_country,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(num_top_classes=5, label="Top Predicted Countries"),
+    title="GeoGuessr-55",
+    description="Upload an image to predict which country it's from. The model uses SigLIP2 to classify among 55 countries."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+## **Applications**
+
+* **GeoGuessr-style games and challenges**
+* **Geographical tagging of unlabeled datasets**
+* **Tourism photo origin prediction**
+* **Education and training for human geographers or ML enthusiasts**