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configs:
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data_files:
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data_files:
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task_categories:
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language:
- en
annotations_creators:
- expert-generated
license: other
license_name: adobe-mit
license_link: LICENSE.md
license_details: A custom license developed for this dataset by Adobe and MIT.
tags:
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- aesthetic
pretty_name: MIT Adobe FiveK
size_categories:
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paperswithcode_id: mit-adobe-fivek
Adobe FiveK
This is an upload of the Adobe FiveK dataset. Note that I am not one of the authors of this dataset, if one of the authors would like to take ownership of this repository please reach out to me. The data provided is not in the original format either. Due to the massive size of the dataset >1TB I elected to convert all .tif and .dng files to a standard .webp with lossless compression. Please refer to the dataset homepage for access to the uncompressed versions of the data.
Dataset Details
Dataset Description
We collected 5,000 photographs taken with SLR cameras by a set of different photographers. They are all in RAW format; that is, all the information recorded by the camera sensor is preserved. We made sure that these photographs cover a broad range of scenes, subjects, and lighting conditions. We then hired five photography students in an art school to adjust the tone of the photos. Each of them retouched all the 5,000 photos using a software dedicated to photo adjustment (Adobe Lightroom) on which they were extensively trained. We asked the retouchers to achieve visually pleasing renditions, akin to a postcard. The retouchers were compensated for their work.
This dataset was collected for our project on learning photographic adjustments.
Acknowledgements: We are grateful to Katrin Eismann and Jeff Schewe for providing invaluable advice and for introducing us to the community of professional photographers. We thank Todd Carroll, David Mager, Jaime Permuth, LaNola Katheleen Stone, and Damian Wampler for their incredible patience while retouching thousands of photos. Special thanks to everyone who contributed their photos to this dataset: without you this work would not have been possible.
Funded by: Foxconn and NSF (0964004) and a gift from Adobe
License: You can use these photos for research under the terms of the following licenses:
- License LicenseAdobe.txt covers files listed in filesAdobe.txt.
- License LicenseAdobeMIT.txt covers files listed in filesAdobeMIT.txt.
Each photo is labled with the license it is under.
Dataset Sources [optional]
- Repository: https://data.csail.mit.edu/graphics/fivek/
- Paper: http://people.csail.mit.edu/vladb/photoadjust/
Uses
Direct Use
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Out-of-Scope Use
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Dataset Structure
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Dataset Creation
Curation Rationale
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Source Data
Data Collection and Processing
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Who are the source data producers?
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Annotations [optional]
Annotation process
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Who are the annotators?
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Personal and Sensitive Information
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Bias, Risks, and Limitations
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Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
Citation
BibTeX:
@inproceedings{fivek, author = "Vladimir Bychkovsky and Sylvain Paris and Eric Chan and Fr{'e}do Durand", title = "Learning Photographic Global Tonal Adjustment with a Database of Input / Output Image Pairs", booktitle = "The Twenty-Fourth IEEE Conference on Computer Vision and Pattern Recognition", year = "2011" }
Dataset Card Authors [optional]
@logasja
Dataset Card Contact
@logasja