This repository serves as a starting point to implement a VPR pipeline. It allows you to train a simple ResNet-18 on the GSV dataset. It relies on the pytorch_metric_learning library.
NB: if you are using Colab, skip this section
The following script:
python download_datasets.py
allows you to download GSV_xs, SF_xs, tokyo_xs, which are reduced version of the GSVCities, SF-XL, Tokyo247 datasets respectively.
NB: if you are using Colab, skip this section
You can install the required packages by running
pip install -r requirements.txt
You can choose to validate/test on sf_xs or tokyo_xs.
python main.py --train_path /path/to/datasets/gsv_xs --val_path /path/to/datasets/tokyo_xs/test --test_path /path/to/datasets/tokyo_xs/test
We provide you with the notebook colab_example.ipynb.
It shows you how to attach your GDrive file system to Colab, unzip the datasets, install packages and run your first experiment.
NB: BEFORE running this notebook, you must copy the datasets zip into your GDrive. You can use the link that we provided and simply click 'create a copy'. Make sure that you have enough space (roughly 8 GBs)
NB^2: you can ignore the dataset robotcar_one_every_2m.