License

Copyright ©2018. The Regents of the University of California (Regents). All Rights Reserved. 

THIS SOFTWARE AND/OR DATA WAS DEPOSITED IN THE BAIR OPEN RESEARCH COMMONS REPOSITORY ON 1/1/2021

Permission to use, copy, modify, and distribute this software and its documentation for educational, research, and not-for-profit purposes, without fee and without a signed licensing agreement; and permission to use, copy, modify and distribute this software for commercial purposes (such rights not subject to transfer) to BDD and BAIR Commons members and their affiliates, is hereby granted, provided that the above copyright notice, this paragraph and the following two paragraphs appear in all copies, modifications, and distributions. Contact The Office of Technology Licensing, UC Berkeley, 2150 Shattuck Avenue, Suite 510, Berkeley, CA 94720-1620, (510) 643-7201, otl@berkeley.edu, http://ipira.berkeley.edu/industry-info for commercial licensing opportunities.

IN NO EVENT SHALL REGENTS BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF REGENTS HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

REGENTS SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE AND ACCOMPANYING DOCUMENTATION, IF ANY, PROVIDED HEREUNDER IS PROVIDED "AS IS". REGENTS HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.

BDD100K



Please visit BDD100K documentation for the details of the downloaded files. To cite the data in your paper

@InProceedings{bdd100k,
    author = {Yu, Fisher and Chen, Haofeng and Wang, Xin and Xian, Wenqi and Chen, Yingying and Liu, Fangchen and Madhavan, Vashisht and Darrell, Trevor},
    title = {BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning},
    booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    month = {June},
    year = {2020}
}

CVPR 2017 Paper



HTTP Link
An online folder containing all the training and validation videos and sensor info for imitation learning.
Google Drive Link
Alternative downloading source from Google Drive.

To cite the data in your paper

@inproceedings{xu2017end,
    title={End-to-end learning of driving models from large-scale video datasets},
    author={Xu, Huazhe and Gao, Yang and Yu, Fisher and Darrell, Trevor},
    booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    year={2017}
}

BDD-Attention

Videos
Over 1,000 driving videos accompanied by driver attention maps and GPS measurements.
Size: 5GB


To cite the data in your paper

@inproceedings{xia2018predicting,
    title={Predicting driver attention in critical situations},
    author={Xia, Ye and Zhang, Danqing and Kim, Jinkyu and Nakayama, Ken and Zipser, Karl and Whitney, David},
    booktitle={Asian conference on computer vision},
    pages={658--674},
    year={2018},
    organization={Springer}
}

FAQ

The download buttons do not work
The website is fully supported by Chrome now. We are working on compatibility with other browsers.

Where can I find the test set labels for BDD100K?
You can check the BDD100K homepage https://www.bdd100k.com for how to evaluate your results on the test set.

I still have other questions.
For questions on BDD100K, you can ask at https://github.com/bdd100k/bdd100k/discussions. For the other datasets, you can contact the authors.