The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/05246b12-f291-460c-be8a-f14a89d249e5

Attila Lengyel; Garg, Sourav; Milford, Michael; van Gemert, Jan (2023): CODaN: Common Objects Day and Night, an image classification dataset. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/05246b12-f291-460c-be8a-f14a89d249e5.v1

Software

• Artificial Intelligence and Image Processing
• Information and Computing Sciences

color invariants, computer vision, day-night, domain adaptation, image classification

MIT

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Attila Lengyel , Sourav Garg , Michael Milford , Jan van Gemert

Common Objects Day and Night (CODaN) is an image classification dataset for zero-shot day-night domain adaptation / generalization.

The CODaN dataset consists of 15,500 224x224 colour images in 10 classes, with 1,550 images per class. There are 10,000 training images, 500 validation images, 2,500 daytime test images and 2,500 nighttime test images.

The dataset is collected from the excellent COCO, ImageNet and ExDark datasets. All images are filtered and cropped such that they have the same dimensions and are completely mutually exclusive, i.e. do not contain objects of different classes, nor do belong objects to multiple classes.

• 2023-11-29 first online, published, posted

4TU.ResearchData

Zero-Shot Day-Night Domain Adaptation with a Physics Prior

• https://research.tudelft.nl/en/publications/zero-shot-day-night-domain-adaptation-with-a-physics-prior

• Tabula Inscripta: Prior knowledge for deep learning (grant code VI.Vidi.192.100) [more info...] Dutch Research Council

TU Delft, TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems, Computer Vision Lab
QUT Centre for Robotics, Queensland University of Technology (QUT), Brisbane, Australia