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dc.contributor{"last":"Fuchs","first":"Erich","role":"ProjectLeader","affiliation":"University of Passau"}
dc.contributor{"last":"Sick","first":"Bernhard","role":"ProjectLeader","affiliation":"University of Kassel","id":"orcid","id_value":"0000-0001-9467-656X"}
dc.contributor{"last":"Susetzky","first":"Tobias","role":"ProjectMember","affiliation":"University of Passau","id":"orcid","id_value":"0000-0001-9201-7587"}{"last":"Heidecker","first":"Florian","affiliation":"University of Kassel","id":"orcid","id_value":"0000-0003-2895-0254"}
dc.descriptionThis dataset provides additional context annotations and extends the BDD100K dataset [1]. Therefore, the approximately 80,000 images with annotation for 2D object detection in the BDD100K dataset were annotated with additional context attributes. The application possibilities of the dataset are diverse. It could be used for model training or evaluating the model performance in different contexts combination, as we did. The annotated contexts per image contain 11 attributes (time_of_day, sky, illumination, precipitation, infrastructure, road, tunnel, construction_site, clear_windshield, light_exposure, and reflections). The underlying annotation guideline with further details is also available for download.de_DE
dc.descriptionTo use the context annotations, the official [BDD100K Dataset]( is required. On top, the provided python-script "" has to be executed to merge the BDD100K original annotations and our additional context annotations. Further instructions are available in the readme file.de_DE
dc.description[1] Yu, F. and Chen, H. and Wang, X. and Xian, W. and Chen, Y. and Liu, F. and Madhavan, V. and Darrell, T.: "BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning", in Proc. of CVPR, Seattle, WA, USA, 2020, doi: 10.1109/CVPR42600.2020.00271de_DE
dc.descriptionIMPORTANT: In case you use the dataset, please cite our corresponding article mentioned below.de_DE
dc.description.sponsorship{"funderName":"BMWK - Bundesministerium für Wirtschaft und Klimaschutz","awardTitle":"KI Data Tooling","awardNumber":"19A20001O"}de_DE
dc.relation{"relationType":"IsCitedBy","id_value":"Heidecker, F. and Susetzky, T. and Fuchs, E. and Sick, B.: Context Information for Corner Case Detection in Highly Automated Driving, in Proc. of ITSC, 2023, (accepted)"}
dc.rightsCreative Commons Attribution-NonCommercial 4.0
dc.titleAdditional BDD100K Context Annotationsde_DE
dc.description.versionVersion 1.0de_DE

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Creative Commons Attribution-NonCommercial 4.0
Except where otherwise noted, this item's license is described as Creative Commons Attribution-NonCommercial 4.0