google-landmark | 5 million images depicting human-made and natural landmarks | Machine Learning library
kandi X-RAY | google-landmark Summary
kandi X-RAY | google-landmark Summary
google-landmark is a Shell library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. google-landmark has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.
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.
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.
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google-landmark has a low active ecosystem.
It has 624 star(s) with 126 fork(s). There are 27 watchers for this library.
It had no major release in the last 6 months.
There are 5 open issues and 12 have been closed. On average issues are closed in 86 days. There are 1 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of google-landmark is current.
Quality
google-landmark has 0 bugs and 0 code smells.
Security
google-landmark has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
google-landmark code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
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google-landmark does not have a standard license declared.
Check the repository for any license declaration and review the terms closely.
Without a license, all rights are reserved, and you cannot use the library in your applications.
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google-landmark releases are not available. You will need to build from source code and install.
Installation instructions, examples and code snippets are available.
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Community Discussions
Trending Discussions on google-landmark
QUESTION
keras error got an unexpected keyword argument 'epochs'
Asked 2018-Mar-15 at 14:05
I'm trying to train a network in Keras to classify an image and after debugging the last issue got this one of unexpected keywork epochs
...ANSWER
Answered 2018-Mar-15 at 14:05model.compile does not take an epochs parameter. Only fit and fit_generator do.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install google-landmark
There are 4,132,914 images in the train set.
train.csv: CSV with id,url,landmark_id fields. id is a 16-character string, url is a string, landmark_id is an integer. Available at: https://s3.amazonaws.com/google-landmark/metadata/train.csv. train_clean.csv: CSV with landmark_id,images fields. landmark_id is an integer, images is a space-separated list of string train image IDs. Available at: https://s3.amazonaws.com/google-landmark/metadata/train_clean.csv. Courtesy of team smlyaka (see their paper). train_attribution.csv: CSV with id,url,author,license,title fields. id is a 16-character string, and the other fields are strings of variable length. Available at: https://s3.amazonaws.com/google-landmark/metadata/train_attribution.csv. train_label_to_category.csv: CSV with landmark_id,category fields: landmark_id is an integer, category is a Wikimedia URL referring to the class definition. Available at: https://s3.amazonaws.com/google-landmark/metadata/train_label_to_category.csv.
train.csv: CSV with id,url,landmark_id fields. id is a 16-character string, url is a string, landmark_id is an integer. Available at: https://s3.amazonaws.com/google-landmark/metadata/train.csv.
train_clean.csv: CSV with landmark_id,images fields. landmark_id is an integer, images is a space-separated list of string train image IDs. Available at: https://s3.amazonaws.com/google-landmark/metadata/train_clean.csv. Courtesy of team smlyaka (see their paper).
train_attribution.csv: CSV with id,url,author,license,title fields. id is a 16-character string, and the other fields are strings of variable length. Available at: https://s3.amazonaws.com/google-landmark/metadata/train_attribution.csv.
train_label_to_category.csv: CSV with landmark_id,category fields: landmark_id is an integer, category is a Wikimedia URL referring to the class definition. Available at: https://s3.amazonaws.com/google-landmark/metadata/train_label_to_category.csv.
There are 761,757 images in the index set.
IMPORTANT: Note that the integer landmark id's mentioned here are different from the ones in the train set above.
index.csv: single-column CSV with id field. id is a 16-character string. Available at: https://s3.amazonaws.com/google-landmark/metadata/index.csv.
index_image_to_landmark.csv: CSV with id,landmark_id fields: id is a 16-character string, landmark_id is an integer. Available at: https://s3.amazonaws.com/google-landmark/metadata/index_image_to_landmark.csv.
index_label_to_category.csv: CSV with landmark_id,category fields: landmark_id is an integer, category is a Wikimedia URL referring to the class definition. Available at: https://s3.amazonaws.com/google-landmark/metadata/index_label_to_category.csv.
There are 117,577 images in the test set.
test.csv: single-column CSV with id field. id is a 16-character string. Available at: https://s3.amazonaws.com/google-landmark/metadata/test.csv. 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.
test.csv: single-column CSV with id field. id is a 16-character string. Available at: https://s3.amazonaws.com/google-landmark/metadata/test.csv.
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.
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:. 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.
train.csv: CSV with id,url,landmark_id fields. id is a 16-character string, url is a string, landmark_id is an integer. Available at: https://s3.amazonaws.com/google-landmark/metadata/train.csv. train_clean.csv: CSV with landmark_id,images fields. landmark_id is an integer, images is a space-separated list of string train image IDs. Available at: https://s3.amazonaws.com/google-landmark/metadata/train_clean.csv. Courtesy of team smlyaka (see their paper). train_attribution.csv: CSV with id,url,author,license,title fields. id is a 16-character string, and the other fields are strings of variable length. Available at: https://s3.amazonaws.com/google-landmark/metadata/train_attribution.csv. train_label_to_category.csv: CSV with landmark_id,category fields: landmark_id is an integer, category is a Wikimedia URL referring to the class definition. Available at: https://s3.amazonaws.com/google-landmark/metadata/train_label_to_category.csv.
train.csv: CSV with id,url,landmark_id fields. id is a 16-character string, url is a string, landmark_id is an integer. Available at: https://s3.amazonaws.com/google-landmark/metadata/train.csv.
train_clean.csv: CSV with landmark_id,images fields. landmark_id is an integer, images is a space-separated list of string train image IDs. Available at: https://s3.amazonaws.com/google-landmark/metadata/train_clean.csv. Courtesy of team smlyaka (see their paper).
train_attribution.csv: CSV with id,url,author,license,title fields. id is a 16-character string, and the other fields are strings of variable length. Available at: https://s3.amazonaws.com/google-landmark/metadata/train_attribution.csv.
train_label_to_category.csv: CSV with landmark_id,category fields: landmark_id is an integer, category is a Wikimedia URL referring to the class definition. Available at: https://s3.amazonaws.com/google-landmark/metadata/train_label_to_category.csv.
There are 761,757 images in the index set.
IMPORTANT: Note that the integer landmark id's mentioned here are different from the ones in the train set above.
index.csv: single-column CSV with id field. id is a 16-character string. Available at: https://s3.amazonaws.com/google-landmark/metadata/index.csv.
index_image_to_landmark.csv: CSV with id,landmark_id fields: id is a 16-character string, landmark_id is an integer. Available at: https://s3.amazonaws.com/google-landmark/metadata/index_image_to_landmark.csv.
index_label_to_category.csv: CSV with landmark_id,category fields: landmark_id is an integer, category is a Wikimedia URL referring to the class definition. Available at: https://s3.amazonaws.com/google-landmark/metadata/index_label_to_category.csv.
There are 117,577 images in the test set.
test.csv: single-column CSV with id field. id is a 16-character string. Available at: https://s3.amazonaws.com/google-landmark/metadata/test.csv. 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.
test.csv: single-column CSV with id field. id is a 16-character string. Available at: https://s3.amazonaws.com/google-landmark/metadata/test.csv.
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.
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:. 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.
Support
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.
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