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Repository with code and links to datasets for the visual geo-localization project

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Project of Visual Geo-localization

This repository provides a ready-to-use visual geo-localization (VG) pipeline, which you can use to train a model on a given dataset. Specifically, it implements a ResNet-18 followed by an average pooling, which can be trained on VG datasets such as Pitts30k, using negative mining and triplet loss as explained in the NetVLAD paper. You will have to replace the average pooling with a GeM layer and a NetVLAD layer.

Datasets

We provide the datasets of Pitts30k and St Lucia

About the datasets formatting, the adopted convention is that the names of the files with the images are:

@ UTM_easting @ UTM_northing @ UTM_zone_number @ UTM_zone_letter @ latitude @ longitude @ pano_id @ tile_num @ heading @ pitch @ roll @ height @ timestamp @ note @ extension

Note that some of these values can be empty (e.g. the timestamp might be unknown), and the only required values are UTM coordinates (obtained from latitude and longitude).

Getting started

To get started first download the repository

git clone https://github.com/gmberton/project_vg

then download Pitts30k (link), and extract the zip file. Then install the required packages

pip install -r requirements.txt

and finally run

Runs command BRB

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