10.57892/100-316Wahmhoff, JohannJohannWahmhoff0009-0008-0328-7066Institut for Animal Breeding and Husbandry, Christian-Albrechts-University, GermanyTraulsen, ImkeImkeTraulsen0000-0001-9761-0291Institut for Animal Breeding and Husbandry, Christian-Albrechts-University, Germanyvan Asten, AstridAstridvan Asten0009-0001-9091-4732Chamber of Agriculture of North Rhine-Westphalia, Centre of Agriculture Haus Düsse, GermanyKrieter, JoachimJoachimKrieterInstitut for Animal Breeding and Husbandry, Christian-Albrechts-University, GermanyDirksen, NeeleNeeleDirksen0000-0003-0942-7900Chamber of Agriculture of Lower Saxony, GermanyDiers, SophieSophieDiers0000-0002-3706-15904. Chamber of Agriculture of Schleswig-HolsteinWutke, MartinMartinWutke0000-0001-7839-2751Institut for Animal Breeding and Husbandry, Christian-Albrechts-University, GermanyKiel University2026ComputervisionObject DetectionYOLOSow husbandry630Kiel University2026-05-27enresearch_datahttps://opendata.uni-kiel.de/receive/fdr_mods_00000316fdr_mods_00000316https://opendata.uni-kiel.de/receive/fdr_mods_00000316?XSL.Transformer=modsAttribution 4.0SowPostureDS is a dataset designed to support the development of robust computer vision and artificial intelligence models for sow posture detection in farrowing environments. The dataset comprises 14.400 annotated images collected across three distinct housing environments, with balanced representation of the four posture classes lying lateral, lying, sitting and standing. The data includes both daytime and nighttime recordings. All images have been standardized by resizing to match a 1280 x 800 pixel resolution. The images are contained in .jpg format and the corresponding annotation informations are saved as .txt files. The dataset is provided in a .zip archive and contains the two subfolders’ images and labels, which follow the standard directory structure commonly used for training YOLO models, enabling seamless integration into existing training workflows. The images and labels from the different environments are marked with corresponding prefixes (“ziss”, “digi” and “inno”). For further information see the following publication. If you use this dataset, please reference: Wahmhoff, J., Traulsen, I., van Asten, A. L., Krieter, J., Dirksen, N., Diers, S., Wutke, M. (2026), SowPostureDS: A Multi-Class Image Dataset for YOLO-Based Detection of Sow Postures in diverse Farrowing Systems. Scientific Data