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GLDv2 code/models

Paper

These instructions can be used to reproduce results from the GLDv2 paper. We present here results on the Revisited Oxford/Paris datasets since they are smaller and quicker to reproduce -- but note that a very similar procedure can be used to obtain results on the GLDv2 retrieval or recognition datasets.

Note that this directory also contains code to compute GLDv2 metrics: see compute_retrieval_metrics.py, compute_recognition_metrics.py and associated file reading / metric computation modules.

For more details on the dataset, please refer to its website.

Install DELF library

To be able to use this code, please follow these instructions to properly install the DELF library.

Download Revisited Oxford/Paris datasets

mkdir -p ~/revisitop/data && cd ~/revisitop/data

# Oxford dataset.
wget http://www.robots.ox.ac.uk/~vgg/data/oxbuildings/oxbuild_images.tgz
mkdir oxford5k_images
tar -xvzf oxbuild_images.tgz -C oxford5k_images/

# Paris dataset. Download and move all images to same directory.
wget http://www.robots.ox.ac.uk/~vgg/data/parisbuildings/paris_1.tgz
wget http://www.robots.ox.ac.uk/~vgg/data/parisbuildings/paris_2.tgz
mkdir paris6k_images_tmp
tar -xvzf paris_1.tgz -C paris6k_images_tmp/
tar -xvzf paris_2.tgz -C paris6k_images_tmp/
mkdir paris6k_images
mv paris6k_images_tmp/paris/*/*.jpg paris6k_images/

# Revisited annotations.
wget http://cmp.felk.cvut.cz/revisitop/data/datasets/roxford5k/gnd_roxford5k.mat
wget http://cmp.felk.cvut.cz/revisitop/data/datasets/rparis6k/gnd_rparis6k.mat

Download model

# From models/research/delf/delf/python/google_landmarks_dataset
mkdir parameters && cd parameters

# RN101-ArcFace model trained on GLDv2-clean.
wget https://storage.googleapis.com/delf/rn101_af_gldv2clean_20200814.tar.gz
tar -xvzf rn101_af_gldv2clean_20200814.tar.gz

Feature extraction

We present here commands for extraction on roxford5k. To extract on rparis6k instead, please edit the arguments accordingly (especially the dataset_file_path argument).

Query feature extraction

In the Revisited Oxford/Paris experimental protocol, query images must be the cropped before feature extraction (this is done in the extract_features script, when setting image_set=query). Note that this is specific to these datasets, and not required for the GLDv2 retrieval/recognition datasets.

Run query feature extraction as follows:

# From models/research/delf/delf/python/google_landmarks_dataset
python3 ../delg/extract_features.py \
  --delf_config_path rn101_af_gldv2clean_config.pbtxt \
  --dataset_file_path ~/revisitop/data/gnd_roxford5k.mat \
  --images_dir ~/revisitop/data/oxford5k_images \
  --image_set query \
  --output_features_dir ~/revisitop/data/oxford5k_features/query

Index feature extraction

Run index feature extraction as follows:

# From models/research/delf/delf/python/google_landmarks_dataset
python3 ../delg/extract_features.py \
  --delf_config_path rn101_af_gldv2clean_config.pbtxt \
  --dataset_file_path ~/revisitop/data/gnd_roxford5k.mat \
  --images_dir ~/revisitop/data/oxford5k_images \
  --image_set index \
  --output_features_dir ~/revisitop/data/oxford5k_features/index

Perform retrieval

To run retrieval on roxford5k, the following command can be used:

# From models/research/delf/delf/python/google_landmarks_dataset
python3 ../delg/perform_retrieval.py \
  --dataset_file_path ~/revisitop/data/gnd_roxford5k.mat \
  --query_features_dir ~/revisitop/data/oxford5k_features/query \
  --index_features_dir ~/revisitop/data/oxford5k_features/index \
  --output_dir ~/revisitop/results/oxford5k

A file with named metrics.txt will be written to the path given in output_dir. The contents should look approximately like:

hard
  mAP=55.54
  mP@k[ 1  5 10] [88.57 80.86 70.14]
  mR@k[ 1  5 10] [19.46 33.65 42.44]
medium
  mAP=76.23
  mP@k[ 1  5 10] [95.71 92.86 90.43]
  mR@k[ 1  5 10] [10.17 25.96 35.29]