TransVLAD for Visual Geo-localization

Introduction

TVLAD is a PyTorch implementation for our wacv-2023 paper "TransVLAD: Multi-Scale Attention-Based Global Descriptors for Visual Geo-Localization". This work was part of a project with SNCF. If you use this code for your research, please cite our paper. For additional questions contact us via yifanxu98@163.com or pshams55@gmail.com.

Installation

We test this repo with Python 3.8, PyTorch 1.9.0, and CUDA 10.2. However, it should be runnable with recent PyTorch versions (Pytorch >=1.1.0).

python setup.py develop

Preparation

Datasets

We test our models on three geo-localization benchmarks, Pittsburgh, Tokyo 24/7 and Tokyo Time Machine datasets. The three datasets can be downloaded at here.

The directory of datasets used is like

datasets/data
├── pitts
│   ├── raw
│   │   ├── pitts250k_test.mat
│   │   ├── pitts250k_train.mat
│   │   ├── pitts250k_val.mat
│   │   ├── pitts30k_test.mat
│   │   ├── pitts30k_train.mat
│   │   ├── pitts30k_val.mat
│   └── └── Pittsburgh
│           ├──images/
│           └──queries/
└── tokyo
    ├── raw
    │   ├── tokyo247
    │   │   ├──images/
    │   │   └──query/
    │   ├── tokyo247.mat
    │   ├── tokyoTM/images/
    │   ├── tokyoTM_train.mat
    └── └── tokyoTM_val.mat

Pre-trained Weights

The file tree we used for storing the pre-trained weights is like

logs
├── mbv3_large.pth.tar # refer to (1)
└── mobilenetv3_large_pitts_64_desc_cen.hdf5 # refer to (2)

(1) ImageNet-pretrained weights for CNNs backbone

The ImageNet-pretrained weights for CNNs backbone or the pretrained weights for the model.

(2) initial cluster centers for VLAD layer

Note that the VLAD layer cannot work with random initialization. The original cluster centers provided by NetVLAD or self-computed cluster centers by running the scripts/cluster.sh.

bash scripts/cluster.sh mobilenetv3_large

Training

Train by running script in the terminal. Script location: scripts/train_tvlad_dist.sh

Format:

bash scripts/train_tvlad_dist.sh arch

where, arch is the backbone name, such as mobilenetv3_large.

For example:

bash scripts/train_tvlad_dist.sh mobilenetv3_large

In the train_tvlad_dist.sh. In case you want to fasten testing, enlarge GPUS for more GPUs, or enlarge the --tuple-size for more tuples on one GPU. In case your GPU does not have enough memory, reduce --pos-num or --neg-num for fewer positives or negatives in one tuple.

Testing

Test by running script in the terminal. Script location: scripts/test_dist.sh

Format:

bash scripts/test_dist.sh resume arch dataset scale

where, resume is the trained model path. arch is the backbone name, such as mobilenetv3_large. dataset scale, such as pitts 30k, pitts 250k, tokyo.

For example:

1. Test mobilenetv3_large on pitts 250k:

bash scripts/test_dist.sh logs/netVLAD/pitts30k-mobilenetv3_large/model_best.pth.tar mobilenetv3_large pitts 250k

In the test.sh. In case you want to fasten testing, enlarge GPUS for more GPUs, or enlarge the --test-batch-size on one GPU. In case your GPU does not have enough memory, reduce --test-batch-size on one GPU.