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PIRM
- huggingface.co
Updated Sep 9, 2021
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EmailClick to copy linkLink copiedCiteEugene Siow (2021). PIRM [Dataset]. https://huggingface.co/datasets/eugenesiow/PIRMCroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.Dataset updatedSep 9, 2021AuthorsEugene SiowLicenseAttribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
License information was derived automaticallyDescriptionThe PIRM dataset consists of 200 images, which are divided into two equal sets for validation and testing. These images cover diverse contents, including people, objects, environments, flora, natural scenery, etc. Images vary in size, and are typically ~300K pixels in resolution.
This dataset was first used for evaluating the perceptual quality of super-resolution algorithms in The 2018 PIRM challenge on Perceptual Super-resolution, in conjunction with ECCV 2018.
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PIRM (Perceptual Image Restoration and Manipulation)
- opendatalab.com
zipUpdated Sep 17, 2018ShareFacebookTwitterEmailClick to copy linkLink copiedCiteIsrael Institute of Technology (2018). PIRM (Perceptual Image Restoration and Manipulation) [Dataset]. https://opendatalab.com/OpenDataLab/PIRMzipAvailable download formatsDataset updatedSep 17, 2018Dataset provided byETH Zurich
Israel Institute of TechnologyLicenseAttribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
License information was derived automaticallyDescriptionThe PIRM dataset consists of 200 images, which are divided into two equal sets for validation and testing. These images cover diverse contents, including people, objects, environments, flora, natural scenery, etc. Images vary in size, and are typically ~300K pixels in resolution.
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DIV2K High Resolution Images
- gts.ai
jpgUpdated Jul 15, 2024ShareFacebookTwitterEmailClick to copy linkLink copiedCiteGTS (2024). DIV2K High Resolution Images [Dataset]. https://gts.ai/dataset-download/div2k-high-resolution-images/jpgAvailable download formatsDataset updatedJul 15, 2024Dataset provided byGLOBOSE TECHNOLOGY SOLUTIONS PRIVATE LIMITEDAuthorsGTSLicenseCC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automaticallyVariables measuredHigh-resolution images (RGB), Downscaling factors (×2, ×3, ×4), Train/Validation split (800/100 images)DescriptionThe DIV2K dataset is a large-scale benchmark designed for image super-resolution research and development, widely used in NTIRE and PIRM challenges. It contains 1,000 high-resolution RGB images divided into training and validation sets. The training set provides 800 high-quality images along with downsampled versions at scaling factors ×2, ×3, and ×4. The validation set includes 100 images, with both low-resolution and high-resolution versions released at different challenge phases. This dataset is ideal for developing, training, and benchmarking super-resolution and image restoration algorithms.
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FacebookTwitterEmailAttribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
License information was derived automatically
The PIRM dataset consists of 200 images, which are divided into two equal sets for validation and testing. These images cover diverse contents, including people, objects, environments, flora, natural scenery, etc. Images vary in size, and are typically ~300K pixels in resolution.
This dataset was first used for evaluating the perceptual quality of super-resolution algorithms in The 2018 PIRM challenge on Perceptual Super-resolution, in conjunction with ECCV 2018.