U.S. Department of EnergyOffice of Scientific and Technical Information
2021 Smoky Mountains Conference Data Challenge Synthetic-to-Real Domain Adaptation for Autonomous Driving Dataset
Abstract
The dataset is comprised of both real and synthetic images from a vehicleâs forward-facing camera. Each camera image is accompanied by a corresponding pixel-level semantic segmentation image (all files are .png files). In total, the dataset contains 5600 images in the training/validation set and 1400 images in the testing set. The training dataset contains mostly synthetic RGB images collected with a wide range of weather and lighting conditions using the CARLA simulator [1]. In addition, the training data also includes a small pre-selected subset of data from the Cityscapes training dataset â which is comprised of RGB-segmentation image pairs from driving scenarios in various European cities [2]. The testing data is split into three sets. The first set contains synthetic CARLA images with weather/lighting conditions that were not present in the training set. The second set is a subset of the Cityscapes testing dataset. Finally, the third set is an unknown testing set which will not be revealed to the participants until after the submission deadline. [1] Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., & Koltun, V. (2017, October). CARLA: An open urban driving simulator. In Conference on robot learning (pp. 1-16). PMLR. [2] Cordts, M., Omran, M., Ramos,more »
- Authors:
-
- ORNL-OLCF
- Publication Date:
- DOE Contract Number:
- AC05-00OR22725
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); General Motors
- Sponsoring Org.:
- Office of Science (SC)
- Collaborations:
- General Motors
- Subject:
- 99 GENERAL AND MISCELLANEOUS; autonomous driving, computer vision, semantic segmentation, domain adaptation, synthetic data
- OSTI Identifier:
- 1772569
- DOI:
- https://doi.org/10.13139/OLCF/1772569
Citation Formats
Coletti, Mark, and Chipka, Jordan. 2021 Smoky Mountains Conference Data Challenge Synthetic-to-Real Domain Adaptation for Autonomous Driving Dataset. United States: N. p., 2021.
Web. doi:10.13139/OLCF/1772569.
Coletti, Mark, & Chipka, Jordan. 2021 Smoky Mountains Conference Data Challenge Synthetic-to-Real Domain Adaptation for Autonomous Driving Dataset. United States. doi:https://doi.org/10.13139/OLCF/1772569
Coletti, Mark, and Chipka, Jordan. 2021.
"2021 Smoky Mountains Conference Data Challenge Synthetic-to-Real Domain Adaptation for Autonomous Driving Dataset". United States. doi:https://doi.org/10.13139/OLCF/1772569. https://www.osti.gov/servlets/purl/1772569. Pub date:Fri Mar 26 04:00:00 UTC 2021
@article{osti_1772569,
title = {2021 Smoky Mountains Conference Data Challenge Synthetic-to-Real Domain Adaptation for Autonomous Driving Dataset},
author = {Coletti, Mark and Chipka, Jordan},
abstractNote = {The dataset is comprised of both real and synthetic images from a vehicleâs forward-facing camera. Each camera image is accompanied by a corresponding pixel-level semantic segmentation image (all files are .png files). In total, the dataset contains 5600 images in the training/validation set and 1400 images in the testing set. The training dataset contains mostly synthetic RGB images collected with a wide range of weather and lighting conditions using the CARLA simulator [1]. In addition, the training data also includes a small pre-selected subset of data from the Cityscapes training dataset â which is comprised of RGB-segmentation image pairs from driving scenarios in various European cities [2]. The testing data is split into three sets. The first set contains synthetic CARLA images with weather/lighting conditions that were not present in the training set. The second set is a subset of the Cityscapes testing dataset. Finally, the third set is an unknown testing set which will not be revealed to the participants until after the submission deadline. [1] Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., & Koltun, V. (2017, October). CARLA: An open urban driving simulator. In Conference on robot learning (pp. 1-16). PMLR. [2] Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., ... & Schiele, B. (2016). The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3213-3223).},
doi = {10.13139/OLCF/1772569},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Mar 26 04:00:00 UTC 2021},
month = {Fri Mar 26 04:00:00 UTC 2021}
}