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---
pipeline_tag: visual-document-retrieval
library_name: pytorch
license: mit
tags:
- visual-place-recognition
- image-retrieval
- arxiv:2502.17237
---
# MegaLoc
MegaLoc is an image retrieval model for visual place recognition (VPR) that achieves state-of-the-art on most VPR datasets, including indoor and outdoor environments.
**Paper:** [MegaLoc: One Retrieval to Place Them All](https://arxiv.org/abs/2502.17237) (CVPR 2025 Workshop)
**GitHub:** [gmberton/MegaLoc](https://github.com/gmberton/MegaLoc)
## Usage
```python
import torch
model = torch.hub.load("gmberton/MegaLoc", "get_trained_model")
model.eval()
# Extract descriptor from an image
image = torch.randn(1, 3, 322, 322) # [B, 3, H, W] - any size works
with torch.no_grad():
descriptor = model(image) # [B, 8448] L2-normalized descriptor
```
For benchmarking on VPR datasets, see [VPR-methods-evaluation](https://github.com/gmberton/VPR-methods-evaluation).
## Qualitative Examples
Top-1 retrieved images from the SF-XL test set (2.8M database images):
## Citation
```bibtex
@InProceedings{Berton_2025_CVPR,
author = {Berton, Gabriele and Masone, Carlo},
title = {MegaLoc: One Retrieval to Place Them All},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2025},
pages = {2861-2867}
}
```
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