MegaLoc

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.

Demo

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.

Using the model

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

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

Here is a complete example with the same preprocessing that we use for evaluation (ImageNet normalization, resize to 322x322)

import torchvision.transforms as tfm
from PIL import Image

transform = tfm.Compose([
    tfm.ToTensor(),
    tfm.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    tfm.Resize(size=[322, 322], antialias=True),
])

images = torch.stack([transform(Image.open(path).convert("RGB")) for path in ["im1.jpg", "im2.jpg"]])
with torch.inference_mode():
    descriptors = model(images)  # shape [2, 8448], L2-normalized

similarities = descriptors @ descriptors.T  # cosine similarities

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.

FAQ

How do I reproduce the paper's results? With VPR-methods-evaluation and the datasets from VPR-datasets-downloader, which has the exact splits we used (#13).

Will you release the training code or datasets? No: the datasets come from sources that we are not allowed to redistribute, and we are not planning to release the training code (#5, #9).

Should I fine-tune MegaLoc on my data? Usually not, unless your data is very out-of-distribution, like caves or underwater (#6).

How should I handle 360° images? Crop each panorama into perspective crops and treat them as independent images (#10).

Qualitative examples

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

Acknowledgements / Cite / BibTex

If you use this repository please cite the following

@inproceedings{Berton_2025_MegaLoc,
    author    = {Berton, Gabriele and Masone, Carlo},
    title     = {MegaLoc: One Retrieval to Place Them All},
    booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
    month     = {June},
    year      = {2025},
    pages     = {2886--2892}
}