Simplified GeoGuessr: Visual Place Recognition from Google Street View Images

A deep learning project for classifying cities from street view images using multiple model architectures. This project compares three different approaches to visual place recognition: a GSV-based model adapted from an existing open-source implementation, a vision-based transformer model, and our hybrid model, GeoSceneNet, which fuses computer-vision based scene descriptors with CNN image features.

Demo (linked)

You can also try the web application live on the same GeoGuessr map here.

Models

GSV

Based on the GSV-Cities framework:

• Backbone: ResNet50
• Aggregation: ConvAP (Convolutional Aggregation Pooling)
• Fine-tuned with a classification head for city prediction

VLM

Uses OpenAI's CLIP model:

• Base Model: openai/clip-vit-base-patch32
• Architecture: CLIP vision encoder with a linear classification head
• Leverages pre-trained vision-language representations

GeoSceneNet

Our own custom model:

• Model: Fusion of CV scene descriptors and CNN image features (ResNet18)
• Classification head predicts off of these features

Installation

1. Clone the repository:

git clone https://github.com/onoderamia/CS549.git
cd CS549

2. Prepare the GSV-cities repository:

git submodule init
git submodule update

Then comment out line 7 in gsv/gsv-cities/main.py:

# from dataloaders.GSVCitiesDataloader import GSVCitiesDataModule

3. Install dependencies:

pip install -r requirements.txt

4. Scrape your own data OR download our data from here

cd scraper
echo "API_KEY=[YOUR_GOOGLE_API_KEY]" > .env
python scraper.py

Usage

Training

Train the GSV model:

cd gsv
python train.py                      # Train from scratch
python train.py ../models/gsv.pth    # Resume from checkpoint

Train the VLM model:

cd vlm
python train.py                      # Train from scratch
python train.py ../models/vlm.pth    # Resume from checkpoint

Train the custom model:

cd custom
python train.py

All training scripts save the model to the models/ directory. You can also use our pretrained models available here

Web Application

Run the web server:

cd webapp
python app.py