metmuseum/openaccess

The Metropolitan Museum of Art Open Access on HuggingFace

The Metropolitan Museum of Art presents over 5,000 years of art from around the world for everyone to experience and enjoy. The Museum lives in two iconic sites in New York City-The Met Fifth Avenue and The Met Cloisters. Millions of people also take part in The Met experience online.

Since it was founded in 1870, The Met has always aspired to be more than a treasury of rare and beautiful objects. Every day, art comes alive in the Museum's galleries and through its exhibitions and events, revealing both new ideas and unexpected connections across time and across cultures.

The Metropolitan Museum of Art provides select datasets of information on more than 470,000 artworks in its Collection for unrestricted commercial and noncommercial use. To the extent possible under law, The Metropolitan Museum of Art has waived all rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law, using Creative Commons Zero. This work is published from: The United States Of America. You can also find the text of the CC Zero deed in the file LICENSE in this repository. These select datasets are now available for use in any media without permission or fee; they also include identifying data for artworks under copyright. The datasets support the search, use, and interaction with the Museum's collection.

Documentation in progress

This data is provided “as is” and you use this data at your own risk. The Metropolitan Museum of Art makes no representations or warranties of any kind. Documentation of the Museum’s collection is an ongoing process and parts of the datasets are incomplete.

We plan to update the datasets with new and revised information on a regular basis. You are advised to regularly update your copy of the datasets to ensure you are using the best available information.

Pull requests

Because these datasets are generated from our internal database, we do not accept pull requests. If you have identified errors or have extra information to share, please email us at openaccess@metmuseum.org and we will forward to the appropriate department for review.

Attribution

Please consider attributing or citing The Metropolitan Museum of Art's CC0 select datasets, especially with respect to research or publication. Attribution supports efforts to release other datasets in the future. It also reduces the amount of "orphaned data," helping to retain source links.

Do not misrepresent the dataset

Do not mislead others or misrepresent the datasets or their source. You must not use The Metropolitan Museum of Art’s trademarks or otherwise claim or imply that the Museum or any other third party endorses you or your use of the dataset.

Whenever you transform, translate or otherwise modify the dataset, you must make it clear that the resulting information has been modified. If you enrich or otherwise modify the dataset, consider publishing the derived dataset without reuse restrictions.

The writers of these guidelines thank the The Museum of Modern Art, the Tate, Cooper-Hewitt, and Europeana.

Additional usage guidelines

For more details on how to use images of artworks in The Metropolitan Museum of Art’s collection, please visit our Open Access page.

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Notes on HuggingFace-specific Data

• This dataset includes images in the url column, and additional data generated by img2dataset
• We include all data, including rows that do not have images
  • You can filter by "Is Public Domain=True" or is "url" blank
• These images are the primaryImageSmall field via our API, i.e., they are not full-res, and have some compression
  • See below and our Collection API if you would like to recreate the data and include larger images (primaryImage) or additional views (additionalImages)
  • This would require edits to add_images.py

Updating or recreating the CSV + images

Right now, this is a manual process. This will eventually be automated.

1. Download or clone the CSV from our github
2. (Optional) Create a compressed CSV
   • Since some operating systems or machines choke on our huge CSV, it can be convenient to compress the file.
   • Easiest: gzip MetObjects.csv
3. Process the CSV
   • Right now, there are many \n characters in the CSV. Some Python interpreters don't like this.
   • Use clean.py to create a cleaned version, now called metadata.csv.gz
4. (Optional) add images to the CSV
   • Run add_images.py
   • It will take a while
   • CAUTION: be very careful with the do_verify variable. Some networks do SSL redirects that Python does not like. Disabling this will not verify SSL certs. This is a quick band-aid to bypass this, but totally unsafe.
5. Install img2dataset
   • pip install img2dataset
6. Run img2dataset with the following options:
   • img2dataset --processes_count 10 --thread_count 64 --url_list "cleaned_metadata_images.csv.gz" --input_format "csv.gz" --output_format "parquet" --output_folder "data/train" --url_col "primaryImageSmall" --disable_all_reencoding "True" --max_shard_retry 10 --retries 10 --save_additional_columns "['Is Highlight', 'Is Timeline Work', 'Is Public Domain', 'Object ID', 'Gallery Number', 'Department', 'AccessionYear', 'Object Name', 'Title', 'Culture', 'Period', 'Dynasty', 'Reign', 'Portfolio', 'Constituent ID', 'Artist Role', 'Artist Prefix', 'Artist Display Name', 'Artist Display Bio', 'Artist Suffix', 'Artist Alpha Sort', 'Artist Nationality', 'Artist Begin Date', 'Artist End Date', 'Artist Gender', 'Artist ULAN URL', 'Artist Wikidata URL', 'Object Date', 'Object Begin Date', 'Object End Date', 'Medium', 'Dimensions', 'Credit Line', 'Geography Type', 'City', 'State', 'County', 'Country', 'Region', 'Subregion', 'Locale', 'Locus', 'Excavation', 'River', 'Classification', 'Rights and Reproduction', 'Link Resource', 'Object Wikidata URL', 'Metadata Date', 'Repository', 'Tags', 'Tags AAT URL', 'Tags Wikidata URL']"
   • See img2dataset's docs for details on the above. You may want to remove the disable_all_reencoding option... As-is, it does not downsize or compress images at all
   • This will take some time
7. Voila! You should have a large data folder with many json and parquet files. You should be able to load this in the huggingface client library as a dataset, etc.