VG-SSL

This is the official repository for VG-SSL: Benchmarking Self-supervised Representation Learning Approaches for Visual Geo-localization.

VG-SSL is designed to facilitate research and development in geo-localization by providing a robust framework for evaluating self-supervised learning approaches.

Prerequisites

Before you begin, ensure you have Anaconda installed on your system as it is required for setting up the environment.

Environment setup

To set up your environment for running VG-SSL, follow these steps:

Install Anaconda, if not already installed.

Use the following command to create and activate the VG-SSL environment:

conda env create -f env.yml

Datasets

VG-SSL utilizes four public datasets: Pitts30k, MSLS, Tokyo 24/7, and Nordland. Each dataset offers unique challenges and scenarios for visual geo-localization.

Download and Preparation: Follow the instructions at VPR datasets downloader to download and prepare these datasets. Note that for MSLS, we use the raw format to leverage the official API for performance evaluation.

Directory Structure: Organize the datasets in your local environment as follows:

VG_SSL/datasets
├── tokyo247
│   └── images
│       └── test
├── nordland
│   └── images
│       └── test    
├── msls
│   ├── test
│   └── train_val
└── pitts30k
    └── images
        ├── train
        ├── val
        └── test  

Training

Our training scripts are used with singularity and slurm system. If you are not using slurm, do step 1-2. If you are not using singularity, do step 3. Otherwise, just do step 4.

1. Export the environment variables

You need to export the environment variables. For example, in the training shell script train_msls_byol_final_sbatch.sh, when you see

sbatch --export=ALL,SSL=byol,LR=1e-5,BATCH=64,PROJ=4096,LAY=2,NEG=0,FC=1024,NEGQ=0 ...,

you need to run

export SSL=byol LR=1e-5 BATCH=64 PROJ=4096 LAY=2 NEG=0 FC=1024 NEGQ=0.

2. Remove sbatch and run training shell script

Remove the sbatch in training shell script. For example, change

sbatch --export=ALL,SSL=byol,LR=1e-5,BATCH=64,PROJ=2048,LAY=2,NEG=0,FC=1024,NEGQ=0 ./script/train_msls_ssl_long_byol_neg_random_pair.sbatch

to

./script/train_msls_ssl_long_byol_neg_random_pair.sbatch.

3. Remove singularity part

To remove the dependency on Singularity, extract and run the core command from the script. For example, Change

singularity exec --nv \
                 --overlay /vast/jx1190/mapillary_sls.sqf:ro \
                 /scratch/work/public/singularity/cuda11.6.124-cudnn8.4.0.27-devel-ubuntu20.04.4.sif \
                 /bin/bash -c "source ~/.bashrc; conda activate VG_SSL; python3 -u train_ssl.py --dataset_name msls --backbone resnet50conv5 --aggregation gem --mining partial --ssl_method $SSL --method pair --datasets_folder ./datasets --save_dir global_retrieval --lr $LR --fc_output_dim $FC --train_batch_size $BATCH --infer_batch_size 256 --num_workers 12 --epochs_num 200 --patience 10 --negs_num_per_query $NEGQ --queries_per_epoch 10000 --unfreeze --n_layers $LAY --projection_size $PROJ --neg_samples_num $NEG --pair_negative --random_resized_crop 0.25 --horizontal_flip"

to

source ~/.bashrc; conda activate VG_SSL; python3 -u train_ssl.py --dataset_name msls --backbone resnet50conv5 --aggregation gem --mining partial --ssl_method $SSL --method pair --datasets_folder ./datasets --save_dir global_retrieval --lr $LR --fc_output_dim $FC --train_batch_size $BATCH --infer_batch_size 256 --num_workers 12 --epochs_num 200 --patience 10 --negs_num_per_query $NEGQ --queries_per_epoch 10000 --unfreeze --n_layers $LAY --projection_size $PROJ --neg_samples_num $NEG --pair_negative --random_resized_crop 0.25 --horizontal_flip

4. Run the experiments

• First stage: Training global retrieval part:

To run simclr and mocov2 experiments, run train_msls_simclr_final_sbatch.sh

To run byol and simsiam experiments, run train_msls_byol_final_sbatch.sh

To run barlow twins and vicreg experiments, run train_msls_vicreg_final_sbatch.sh

After training, you can find the model_folder_name in the logs folders and the name conversion is as follows:

$Training_dataset-$datetime-$uuid

• Second stage: Training the reranking part and finetuning:

Use train.sh to train the reranking part and finetune.

Remember to Change the --resume argument in the sbatch scripts to load the global retrieval models you trained in the first stage

Evaluation

For evaluation, use the script located in ./script/eval_ssl_singularity.sbatch. This script is designed to evaluate the recall performance of your trained models using standard metrics.

Acknowledgement

VG-SSL builds upon several existing frameworks and repositories:

https://github.com/gmberton/deep-visual-geo-localization-benchmark
https://github.com/bytedance/R2Former
https://github.com/google-research/simclr
https://github.com/facebookresearch/moco
https://github.com/lucidrains/byol-pytorch
https://github.com/facebookresearch/barlowtwins
https://github.com/facebookresearch/vicreg