danjacobellis/bdd500_pl_f14

BDD500 Validation Set (Pseudo-Label, Frame 14)

This dataset was created for Dedelayed: Deleting Remote Inference Delay via On-Device Correction (CVPR 2026). Code is available at InterDigitalInc/dedelayed.

The underlying data is derived from the BDD100K driving video dataset. It contains 500 validation sequences of 15 frames each.

Usage

This dataset is the validation split. Use it together with danjacobellis/bdd100k_train as follows:

import datasets

dataset = datasets.DatasetDict({
    'train': datasets.load_dataset("danjacobellis/bdd100k_train", split='train'),
    'validation': datasets.load_dataset("danjacobellis/bdd500_pl_f14", split='validation')
})

For an example evaluation function and mIoU computation, see the reference training notebook. Semantic labels use Cityscapes class IDs with ignore index 255.

License

This dataset is distributed under the BDD100K 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.

Citation

@inproceedings{jacobellis2026dedelayed,
  title     = {Dedelayed: Deleting Remote Inference Delay via On-Device Correction},
  author    = {Jacobellis, Dan and Ulhaq, Mateen and Racap{\'e}, Fabien and Choi, Hyomin and Yadwadkar, Neeraja J.},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2026},
  note      = {To appear}
}

@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}
}

@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}
}