The GitHub repository titled "Statewide Visual Geolocalization in the Wild" is associated with a research project presented at the European Conference on Computer Vision (ECCV) in 2024. The project is led by a team of researchers including Florian Fervers, Sebastian Bullinger, Christoph Bodensteiner, Michael Arens, and Rainer Stiefelhagen. The repository serves as a resource for implementing the methods discussed in their paper, which focuses on visual geolocalization techniques applicable in real-world scenarios. The repository includes essential components such as installation instructions, dataset information, training procedures, and evaluation methods. Users are guided to install Jax with GPU support and clone the repository to set up the environment. The dataset utilized for training and evaluation consists of street-view images sourced from the Mapillary platform, along with aerial imagery from various locations in the United States and Germany. For training, users are instructed to configure dataset paths in a YAML file and execute a training script that leverages available GPUs. The results of the training process are stored in a designated output directory. The evaluation process involves creating a reference database for a specified search region, which includes generating embeddings for different cells and creating a FAISS index for efficient retrieval. The repository also provides a script for localizing query images against the reference database, allowing users to assess the performance of the model through recall metrics at various distances. The results indicate the effectiveness of the model in accurately localizing images based on the pretrained weights. Additionally, the repository encourages users to cite the work if they utilize the code or data, providing a citation format for reference. Users are also invited to report any issues they encounter while using the repository, fostering a collaborative environment for further development and improvement of the geolocalization methods presented.