Playing Geoguessr in NYC using deep learning superpowers.
The codebase consists of three notebooks:
scraping.ipynb: Construction of the RecogNYCe dataset.training.ipynb: Training of the neural networks.results.ipynb: Model evaluation, webapp log processing and result visualization.
The web application is also contained within this repository and can be found in the ./webapp directory.
In order to keep the repository lightweight, both the dataset and the model weights are distributed through Google Drive:
The complete folder structure for this project looks like.
.
├── data
│ └── all
│ └── ...
├── docker-compose.yml
├── figures
├── models
│ ├── cnn_benchmark.pth
│ ├── priors.npy
│ ├── regnet_5_alone.pth
│ ├── resnet18_20+5_cont.pth
│ ├── resnet18_places_5_-2_alone.pth
│ ├── resnet50_5_alone.pth
│ └── results
│ ├── accuracy.npy
│ ├── y_hat.npy
│ └── y.npy
├── nginx.conf
├── README.md
├── results.ipynb
├── scraping.ipynb
├── training.ipynb
├── utils
│ ├── geojson_utils.py
│ ├── gmaps_utils.py
│ ├── mapillary_utils.py
│ ├── model_utils.py
│ └── plot_utils.py
└── webapp
└── ...