An image retrieval model for any localization task, which achieves SOTA on most VPR datasets, including indoor and outdoor ones.

Gradio Demo - ArXiv - Paper on ArXiv - Paper on HF - Model on HF.

Try the demo on your own images to see how good MegaLoc is! The demo uses a database of ~5M street-view images from San Francisco, and when you upload one it will find the most similar one from the same place.

You can use the model with torch.hub, as simple as this

import torch
model = torch.hub.load("gmberton/MegaLoc", "get_trained_model")

For more complex uses, like computing results on VPR datasets, visualizing predictions and so on, you can use our VPR-methods-evaluation, which lets you do all this for MegaLoc and multiple other VPR methods on labelled or unlabelled datasets.

Here are some examples of top-1 retrieved images from the SF-XL test set, which has 2.8M images as database.

If you use this repository please cite the following

@misc{berton_2025_megaloc,
      title={MegaLoc: One Retrieval to Place Them All}, 
      author={Gabriele Berton and Carlo Masone},
      year={2025},
      eprint={2502.17237},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2502.17237}, 
}