README.md

---
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configs:
- config_name: a
  data_files:
  - split: train
    path: a/train-*
  - split: test
    path: a/test-*
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    path: a/validation-*
- config_name: b
  data_files:
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    path: b/train-*
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    path: b/test-*
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- config_name: c
  data_files:
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    path: c/train-*
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    path: c/test-*
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- config_name: d
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    path: d/train-*
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- config_name: e
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    path: e/train-*
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    path: e/test-*
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    path: e/validation-*
task_categories:
- image-to-image
- feature-extraction
language:
- en
annotations_creators:
- expert-generated
license: other  # Example: apache-2.0 or any license from https://hf.co/docs/hub/repositories-licenses
license_name: adobe-mit  # If license = other (license not in https://hf.co/docs/hub/repositories-licenses), specify an id for it here, like `my-license-1.0`.
license_link: LICENSE.md
license_details: A custom license developed for this dataset by Adobe and MIT.  # Legacy, textual description of a custom license.
tags:
- adobe
- aesthetic
pretty_name: MIT Adobe FiveK
size_categories:
- 1K<n<10K
paperswithcode_id: mit-adobe-fivek
---

# Adobe FiveK

<!-- Provide a quick summary of the dataset. -->

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

<!-- Provide a longer summary of what this dataset is. -->
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:

1. License [LicenseAdobe.txt](https://data.csail.mit.edu/graphics/fivek/legal/LicenseAdobe.txt) covers files listed in [filesAdobe.txt](https://data.csail.mit.edu/graphics/fivek/legal/filesAdobe.txt).
2. License [LicenseAdobeMIT.txt](https://data.csail.mit.edu/graphics/fivek/legal/LicenseAdobeMIT.txt) covers files listed in [filesAdobeMIT.txt](https://data.csail.mit.edu/graphics/fivek/legal/filesAdobeMIT.txt).

Each photo is labled with the license it is under.

### Dataset Sources [optional]

<!-- Provide the basic links for the dataset. -->

- **Repository:** https://data.csail.mit.edu/graphics/fivek/
- **Paper:** http://people.csail.mit.edu/vladb/photoadjust/

## Uses

<!-- Address questions around how the dataset is intended to be used. -->

### Direct Use

<!-- This section describes suitable use cases for the dataset. -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->

[More Information Needed]

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

[More Information Needed]

## Dataset Creation

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->

[More Information Needed]

### Source Data

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

[More Information Needed]

#### Who are the source data producers?

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->

[More Information Needed]

### Annotations [optional]

<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->

#### Annotation process

<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->

[More Information Needed]

#### Who are the annotators?

<!-- This section describes the people or systems who created the annotations. -->

[More Information Needed]

#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**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