Datasets › MIT-Adobe FiveK

MIT-Adobe FiveK

archive 2025-07-28

The MIT-Adobe FiveK dataset consists of 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. When using images from this dataset, please cite this dataset using the following 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"
}

Source: https://data.csail.mit.edu/graphics/fivek/

Image source: https://data.csail.mit.edu/graphics/fivek/

Benchmarks archive 2025-07-28

All 4 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

Papers archive 2025-07-28

14 shown of 14 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 28. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

DateSamples run Syntology
Image Enhancement Based on Histogram-Guided Multiple Transformation Function Estimation 1 1 8 Oct 2024 not harvested
PQDynamicISP: Dynamically Controlled Image Signal Processor for Any Image Sensors Pursuing Perceptual Quality 0 1 15 Mar 2024 not harvested
Multiple transformation function estimation for image enhancement 1 1 1 Sep 2023 not harvested
RSFNet: A White-Box Image Retouching Approach using Region-Specific Color Filters 1 1 15 Mar 2023 ran 3 of 12 samples (9 unverified; 12 pointer-only for licence)
Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement 5 3 12 Mar 2023 ran 2 of 10 samples (8 unverified)
4D LUT: Learnable Context-Aware 4D Lookup Table for Image Enhancement 0 1 5 Sep 2022 not harvested
SepLUT: Separable Image-adaptive Lookup Tables for Real-time Image Enhancement 1 1 18 Jul 2022 not harvested
TreEnhance: A Tree Search Method For Low-Light Image Enhancement 1 1 25 May 2022 not harvested
AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement 1 2 29 Apr 2022 ran 1 of 1 samples (0 unverified)
MAXIM: Multi-Axis MLP for Image Processing 3 1 9 Jan 2022 ran 27 of 46 samples (19 unverified)
High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network 1 1 19 May 2021 ran 3 of 5 samples (2 unverified)
Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time 1 1 30 Sep 2020 not harvested
DeepLPF: Deep Local Parametric Filters for Image Enhancement 2 1 31 Mar 2020 ran 0 of 2 samples (2 unverified; 2 pointer-only for licence)
CURL: Neural Curve Layers for Global Image Enhancement 3 2 29 Nov 2019 not harvested

Dataset loaders archive 2025-07-28

2 loaders as listed in the archive; links are outbound and not re-checked here.

Tasks archive 2025-07-28

License archive 2025-07-28

Custom

Modalities archive 2025-07-28

Languages archive 2025-07-28

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Variants archive 2025-07-28

  • MIT-Adobe 5k
  • MIT-Adobe FiveK

2 variant names, as the archive lists them.

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