Learned Contextual Feature Reweighting for Image Geo-Localization

This code is developed based on Caffe

This code is the implementation for the network with the context-based feature reweighting in the paper:

Hyo Jin Kim, Enrique Dunn, and Jan-Michael Frahm. "Learned Contextual Feature Reweighting for Image Geo-Localization". Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. [pdf] [project page]

If you use our codes or models in your research, please cite:

@inproceedings{kim2017crn,
  title={Learned Contextual Feature Reweighting for Image Geo-Localization},
  author={Kim, Hyo Jin and Dunn, Enrique and  Frahm, Jan-Michael},
  booktitle={CVPR},
  year= {2017}
}

Dataset for San Francisco Benchmark

Original dataset by Chen et al. (2011): https://purl.stanford.edu/vn158kj2087

Training query images

1.1 Flickr Images

Flickr id's available at sf_flickr.txt

Each pair in the file consists of the saved image name and the corresponding Flickr id

Currently, original images are accessible by http://flickr.com/photo.gne?id=(Put Photo Id Here)

Please refer to copyrights of each images. We plan to provide scripts for downloading images later.

1.2 Google Streetview Research Dataset

Available at ICMLA'11 Streetview Recognition Challenge
Reference images

Available at https://purl.stanford.edu/vn158kj2087
Test query images

Available at https://purl.stanford.edu/vn158kj2087

Training

*** Important Details for Training on New Datasets (described in the paper) ***

Step 1> Train the base representation (e.g. NetVLAD) first.

Step 2> Jointly train CRN (normal learning rate) + the base representation (lower learning rate).

In this way, the CRN is trained in a more stable manner + yields better performance.

Install the custom Caffe & PyCaffe (Includes custom layers built for this method)
Download dataset and perform pre-processing on query images (cropping of training queries to three square patches: {left, center, right} or {top, center, bottom} based on the aspect ratio of the original image. The patches should be named as [OriginalName]_aux1.jpg, [OriginalName].jpg, and [OriginalName]_aux2.jpg, respectively.) (Todo: provide script)
Image data lists are available at https://www.dropbox.com/s/qv2qkzd4vx25wqm/data.zip?dl=0

This contains

1.1 lists of triplets used for training, validation
- training: all_sanfran_netvlad_trn_fr.txt
- validation: val_sanfran_netvlad_trn_fr.txt
1.2 lists of images for feature extraction used for evaluation
- query: sanfran_q3_featext_fr.txt
- reference: sanfran_sv_featext_fr.txt
- Note: The subfolder "download" is depreciated
- Adjust file paths before starting training
Run

   cd crn/caffe   
   crn_cvpr17/rw_net/train_rw_alex.sh # for alexnet-based network
   crn_cvpr17/rw_net/train_rw_vgg.sh # for vgg16-based network

3.1. Fine-tuning of NetVLAD on SF benchmark

   crn_cvpr17/netvlad/train_netvlad_alex.sh # for alexnet-based network
   crn_cvpr17/netvlad/train_netvlad_alex.sh # for vgg16-based network

Pre-Trained Models

We provide pre-trained models in trained_models

Evaluation

Evaluation scripts available at matlab_scripts

Evaluation of CRN+NetVLAD

run_eval_rw_netvlad_alexnet_fullres.m	
run_eval_rw_netvlad_vgg_fullres.m

Evaluation of NetVLAD

run_eval_netvlad_alexnet_fullres.m
run_eval_netvlad_vgg_fullres.m

Misc

Learning rate scheduling: Learning rate scheduling is done through babysitting. Whenever the training loss reached a plateau, learning rate was reduced by gamma (as specified in the solver.prototxt).
Current version was tested on Ubuntu14.04 with CUDA 7. For Ubuntu16.04 with CUDA 8, please follow the instructions at https://github.com/BVLC/caffe/wiki/Ubuntu-16.04-or-15.10-Installation-Guide