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---
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
---
# **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
Argentina 0.5292 0.5083 0.5185 482
Australia 0.7850 0.8146 0.7995 1192
Austria 0.6199 0.4380 0.5133 242
Bangladesh 0.4583 0.1486 0.2245 74
Belgium 0.2500 0.0065 0.0127 153
Bolivia 0.0000 0.0000 0.0000 81
Botswana 0.5263 0.2000 0.2899 100
Brazil 0.6562 0.8356 0.7351 1624
Bulgaria 0.5091 0.3709 0.4291 151
Cambodia 0.0000 0.0000 0.0000 82
Canada 0.7464 0.7973 0.7710 967
Chile 0.5000 0.1360 0.2138 228
Colombia 0.3191 0.0857 0.1351 175
Croatia 0.6667 0.0222 0.0430 90
Czechia 0.5000 0.0335 0.0628 179
Denmark 0.0000 0.0000 0.0000 138
Finland 0.6609 0.8338 0.7373 734
France 0.6129 0.7913 0.6908 2501
Germany 0.7943 0.8627 0.8271 488
Ghana 0.4706 0.1081 0.1758 74
Greece 0.3684 0.0809 0.1327 173
Hungary 0.5000 0.0342 0.0640 117
India 0.8261 0.5089 0.6298 112
Indonesia 0.6211 0.2935 0.3986 201
Ireland 0.6316 0.0591 0.1081 203
Israel 0.5427 0.5570 0.5498 228
Italy 0.4092 0.2736 0.3279 552
Japan 0.7996 0.9632 0.8738 2688
Kenya 0.4359 0.1868 0.2615 91
Latvia 0.0000 0.0000 0.0000 81
Lithuania 0.0000 0.0000 0.0000 98
Malaysia 0.5413 0.3986 0.4591 296
Mexico 0.4721 0.4571 0.4645 630
Netherlands 0.5101 0.3753 0.4324 405
New Zealand 0.6910 0.5116 0.5879 389
Nigeria 0.4000 0.3488 0.3727 86
Norway 0.7384 0.7055 0.7216 472
Peru 0.5000 0.3016 0.3762 189
Philippines 0.5217 0.1569 0.2412 153
Poland 0.5122 0.6275 0.5640 604
Portugal 0.2000 0.0059 0.0115 169
Romania 0.4167 0.3512 0.3812 242
Russia 0.6232 0.7946 0.6985 1232
Singapore 0.7339 0.9211 0.8169 494
Slovakia 0.0000 0.0000 0.0000 75
South Africa 0.7535 0.7717 0.7625 828
South Korea 0.5478 0.5059 0.5260 170
Spain 0.4589 0.5492 0.5000 752
Sweden 0.5311 0.3701 0.4362 508
Switzerland 1.0000 0.0165 0.0325 121
Taiwan 0.6029 0.4293 0.5015 382
Thailand 0.5309 0.7939 0.6363 660
Turkey 0.4872 0.2032 0.2868 187
Ukraine 0.0000 0.0000 0.0000 79
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** |