Heidecker, Florian0000-0003-2895-02542023-07-242023-07-24https://daks.uni-kassel.de/handle/123456789/56This 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.To use the context annotations, the official [BDD100K Dataset](https://doc.bdd100k.com/download.html) is required. On top, the provided python-script "extent_bdd_with_context.py" has to be executed to merge the BDD100K original annotations and our additional context annotations. Further instructions are available in the readme file.[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/CVPR42600.2020.00271IMPORTANT: In case you use the dataset, please cite our corresponding article mentioned below.engCreative Commons Attribution-NonCommercial 4.0https://creativecommons.org/licenses/by-nc/4.0/DatasetFuchs, ErichSick, Bernhard0000-0001-9467-656XSusetzky, Tobias0000-0001-9201-758710.48662/daks-25Bundesministerium für Wirtschaft und Klimaschutz – BMWK02vgg2808Federal Ministry for Economic Affairs and Climate Action02vgg2808