This is the second version of the Google Landmarks dataset (GLDv2), which
contains images annotated with labels representing human-made and natural
landmarks. The dataset can be used for landmark recognition and retrieval
experiments. This version of the dataset contains approximately 5 million
images, split into 3 sets of images: train, index and test. The dataset
was presented in our CVPR'20 paper and
Google AI blog post.
In this repository, we present download links for all dataset files, baseline
models and code for metric computation.
This dataset was associated to two Kaggle challenges, on
landmark recognition and
landmark retrieval. Results
were discussed as part of a
CVPR'19 workshop. In this
repository, we also provide scores for the top 10 teams in the challenges, based
on the latest ground-truth version. Please visit the challenge and workshop
webpages for more details on the data, tasks and technical solutions from top
teams.
As a reference, the previous version of the Google Landmarks dataset (referred
to as Google Landmarks dataset v1, GLDv1) is available
here. Note that we do
NOT plan to maintain GLDv1, so we STRONGLY encourage you to use mainly GLDv2.
If you make use of this dataset, please consider citing the following paper:
"Google Landmarks Dataset v2 - A Large-Scale Benchmark for Instance-Level Recognition and Retrieval"
T. Weyand*, A. Araujo*, B. Cao, J. Sim
Proc. CVPR'20
The current dataset version is 2.1. See the
release history for details, including re-scored challenge
submissions based on the latest ground-truth version.
The train set is split into 500 TAR files (each of size ~1GB) containing
JPG-encoded images. The files are located in the train/ directory, and are
named images_000.tar, images_001.tar, ..., images_499.tar. To download
them, access the following link:
This will automatically download, verify and extract the images to the train
directory.
Note: This script downloads files in parallel. To adjust the number of parallel
downloads, modify NUM_PROC in the script.
train image licenses
All images in the train set have CC-BY licenses without the NonDerivs (ND)
restriction. To verify the license for a particular image, please refer to
train_attribution.csv.
Download index set
There are 761,757 images in the index set.
Download the list of images and metadata
IMPORTANT: Note that the integer landmark id's mentioned here are different
from the ones in the train set above.
The index set is split into 100 TAR files (each of size ~850MB) containing
JPG-encoded images. The files are located in the index/ directory, and are
named images_000.tar, images_001.tar, ..., images_099.tar. To download
them, access the following link:
recognition_solution_v2.1.csv: CSV with three columns: id (16-character
string), landmarks (space-separated list of integer landmark IDs, or empty
if no landmark from the dataset is depicted), Usage (either "Public" or
"Private", referring to which subset the image belongs to). Available at:
https://s3.amazonaws.com/google-landmark/ground_truth/recognition_solution_v2.1.csv.
retrieval_solution_v2.1.csv: CSV with three columns: id (16-character
string), images (space-separated list of string index image IDs, or None
if this image is ignored), Usage (either "Public" or "Private", referring
to which subset the image belongs to). Available at:
https://s3.amazonaws.com/google-landmark/ground_truth/retrieval_solution_v2.1.csv.
Downloading the data
The test set is split into 20 TAR files (each of size ~500MB) containing
JPG-encoded images. The files are located in the test/ directory, and are
named images_000.tar, images_001.tar, ..., images_019.tar. To download
them, access the following link:
mkdir test&&cdtest
bash ../download-dataset.sh test 19
This will automatically download, verify and extract the images to the test
directory.
Note: This script downloads files in parallel. To adjust the number of parallel
downloads, modify NUM_PROC in the script.
test image licenses
All images in the test set have CC-0 or Public Domain licenses.
Checking the download
We also make available md5sum files for checking the integrity of the downloaded
files. Each md5sum file corresponds to one of the TAR files mentioned above;
they are located in the md5sum/index/, md5sum/test/ and md5sum/train/
directories, with file names md5.images_000.txt, md5.images_001.txt, etc.
For example, the md5sum file corresponding to the images_000.tar file in the
index set can be found via the following link:
If you use the provided download-dataset.sh script, the integrity of the files
is already checked right after download.
Extracting the data
We recommend that the set of TAR files corresponding to each dataset split be
extracted into a directory per split; ie, the index TARs extracted into an
index directory; train TARs extracted into a train directory; test TARs
extracted into a test directory. This is done automatically if you use the
above download instructions/script.
The directory structure of the image data is as follows: Each image is stored in
a directory ${a}/${b}/${c}/${id}.jpg, where ${a}, ${b} and ${c}
are the first three letters of the image id, and ${id} is the image id found
in the csv files. For example, an image with the id 0123456789abcdef would be
stored in 0/1/2/0123456789abcdef.jpg.
Baseline models
We make available the ResNet101-ArcFace baseline model from the paper, see
instructions
here.
Metric computation code
The metric computation scripts have been made available, via the
DELF github repository,
see the python scripts compute_recognition_metrics.py and
compute_retrieval_metrics.py. These scripts accept as input the ground-truth
files, along with predictions in the format submitted to Kaggle.
Dataset licenses
The annotations are licensed by Google under CC BY 4.0 license. The images
listed in this dataset are publicly available on the web, and may have different
licenses. Google does not own their copyright. Note: while we tried to identify
images that are licensed under a Creative Commons Attribution license, we make
no representations or warranties regarding the license status of each image and
you should verify the license for each image yourself.
Release history
Sept 2019 (version 2.1)
Ground-truth and labelmaps released. Note that the ground-truth has been
substantially updated since the end of the 2019 Kaggle challenges; it is not the
one that was used for scoring in the challenge.
We have re-computed metrics for the top 10 teams in the 2019 challenges (see the
Kaggle challenge webpages for precise definitions of the metrics):
Recognition metrics
Team
Private GAP (%)
Public GAP (%)
JL
66.53
61.86
GLRunner
53.08
52.07
smlyaka
69.39
65.85
Chundi Liu
60.86
56.77
Cookpad
33.66
31.12
bestfitting
54.53
52.46
Himanshu Rai
60.32
56.28
Eduardo
46.88
44.07
ods.ai
24.02
22.28
ZFTurbo & Weimin & David
38.99
39.83
Retrieval metrics
Team
Private mAP@100 (%)
Public mAP@100 (%)
smlyaka
37.14
35.63
imagesearch
34.38
32.04
Layer 6 AI
32.10
29.92
bestfitting
32.12
29.09
ods.ai
29.82
27.82
learner
28.98
27.33
CVSSP
28.07
26.59
Clova Vision, NAVER/LINE Corp.
27.77
25.85
VRG Prague
25.48
23.71
JL
24.98
22.73
May 2019 (version 2.0)
Included data for test and index sets.
Apr 2019 (version 2.0)
Initial version, including only train set.
Contact
For any questions/suggestions/comments/corrections, please open an issue in this
github repository, and tag @andrefaraujo. In particular, we plan to maintain and
release new versions of the ground-truth as corrections are found.
Paper reference
@inproceedings{weyand2020GLDv2,
author = {Weyand, T. and Araujo, A. and Cao, B. and Sim, J.},
title = {{Google Landmarks Dataset v2 - A Large-Scale Benchmark for Instance-Level Recognition and Retrieval}},
year = {2020},
booktitle = {Proc. CVPR},
}
Dataset Metadata
The following table is necessary for this dataset to be indexed by search
engines such as Google Dataset Search.
property
value
name
Google Landmarks Dataset v2
url
https://github.com/cvdfoundation/google-landmark
description
This is the second version of the Google Landmarks dataset (GLDv2), which contains images annotated with labels representing human-made and natural landmarks. The dataset can be used for landmark recognition and retrieval experiments. This version of the dataset contains approximately 5 million images, split into 3 sets of images: train, index and test. The dataset was presented in our CVPR'20 paper. In this repository, we present download links for all dataset files and relevant code for metric computation.
This dataset was associated to two Kaggle challenges, on landmark recognition and landmark retrieval. Results were discussed as part of a CVPR'19 workshop. In this repository, we also provide scores for the top 10 teams in the challenges, based on the latest ground-truth version. Please visit the challenge and workshop webpages for more details on the data, tasks and technical solutions from top teams.
provider
property
value
name
Google
sameAs
https://en.wikipedia.org/wiki/Google
license
The annotations are licensed by Google under CC BY 4.0 license. The images listed in this dataset are publicly available on the web, and may have different licenses. Google does not own their copyright. Note: while we tried to identify images that are licensed under a Creative Commons Attribution license, we make no representations or warranties regarding the license status of each image and you should verify the license for each image yourself.
citation
Weyand, T. and Araujo, A. and Cao, B. and Sim, J., "Google Landmarks Dataset v2 - A Large-Scale Benchmark for Instance-Level Recognition and Retrieval", Proc. CVPR 2020
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