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Google Landmarks Dataset v2

NEW: Explore the dataset visually here.

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:

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

Dataset webpage

Explore the dataset visually here.

Current version

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.

Download train set

There are 4,132,914 images in the train set.

Download the labels and metadata

Downloading the data

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:

https://s3.amazonaws.com/google-landmark/train/images_000.tar

And similarly for the other files.

Using the provided script

mkdir train && cd train
bash ../download-dataset.sh train 499

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.

Downloading the data

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:

https://s3.amazonaws.com/google-landmark/index/images_000.tar

And similarly for the other files.

Using the provided script

mkdir index && cd index
bash ../download-dataset.sh index 99

This will automatically download, verify and extract the images to the index directory.

Note: This script downloads files in parallel. To adjust the number of parallel downloads, modify NUM_PROC in the script.

index image licenses

All images in the index set have CC-0 or Public Domain licenses.

Download test set

There are 117,577 images in the test set.

Download the list of images and ground-truth

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:

https://s3.amazonaws.com/google-landmark/test/images_000.tar

And similarly for the other files.

Using the provided script

mkdir test && cd test
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:

https://s3.amazonaws.com/google-landmark/md5sum/index/md5.images_000.txt

And similarly for the other files.

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
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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Dataset with 5 million images depicting human-made and natural landmarks spanning 200 thousand classes.

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