Canada Guesser

Welcome to Canada Guesser, a collection of resources for building and exploring Canadian city classification models using street-view imagery. This organization contains a dataset, models, and an interactive demo designed to work together, enabling end-to-end experimentation and deployment.

Dataset

Canadian Street View Cities

• Purpose: Train and evaluate models that classify street-view images by Canadian city.
• Content: 135,000 training images, 15,000 test images, covering 15 major Canadian cities with 10,000 images each.
• Features: image, label (0–14), city (city name).
• Known Limitations: Some cities (Saskatoon, Halifax) contain dashcam-style images including dashboards, which may affect model behavior.
• License: CC-BY-SA-4.0

Model

Canada-Guesser Model

• Purpose: Classifies Canadian street-view images by city.
• Trained on: Canadian Street View Cities dataset.
• Architecture: CNN (ConvNeXt-tiny) and Transformer (SwinV2) variants available.
• Task: Image classification.

Model Performance

Performance was evaluated on the test split of the dataset. Both models achieve high accuracy, with Swin Transformer V2 slightly outperforming ConvNeXt-tiny.

Space

Canada-Guesser Demo

• Interactive app: Upload a street-view image and see which city the model predicts.
• Demonstrates the full pipeline: dataset → model → live inference.

Citation

If you use this dataset or models, please cite:

1. Stephen Rebel, Danial McIntyre, Sharav Bali. Canadian Street View Classifier. Hugging Face, 2025.