README.md

---
license: other
source_datasets:
- bdd100k
license_name: bdd100k-license
license_link: LICENSE
task_categories:
- image-segmentation
dataset_info:
  features:
  - name: very_lossy_0
    dtype: image
  - name: very_lossy_1
    dtype: image
  - name: very_lossy_2
    dtype: image
  - name: very_lossy_3
    dtype: image
  - name: very_lossy_4
    dtype: image
  - name: very_lossy_5
    dtype: image
  - name: very_lossy_6
    dtype: image
  - name: very_lossy_7
    dtype: image
  - name: very_lossy_8
    dtype: image
  - name: very_lossy_9
    dtype: image
  - name: very_lossy_10
    dtype: image
  - name: very_lossy_11
    dtype: image
  - name: very_lossy_12
    dtype: image
  - name: very_lossy_13
    dtype: image
  - name: very_lossy_14
    dtype: image
  - name: near_lossless_0
    dtype: image
  - name: near_lossless_1
    dtype: image
  - name: near_lossless_2
    dtype: image
  - name: near_lossless_3
    dtype: image
  - name: near_lossless_4
    dtype: image
  - name: near_lossless_5
    dtype: image
  - name: near_lossless_6
    dtype: image
  - name: near_lossless_7
    dtype: image
  - name: near_lossless_8
    dtype: image
  - name: near_lossless_9
    dtype: image
  - name: near_lossless_10
    dtype: image
  - name: near_lossless_11
    dtype: image
  - name: near_lossless_12
    dtype: image
  - name: near_lossless_13
    dtype: image
  - name: near_lossless_14
    dtype: image
  - name: label_0
    dtype: image
  - name: label_1
    dtype: image
  - name: label_2
    dtype: image
  - name: label_3
    dtype: image
  - name: label_4
    dtype: image
  - name: label_5
    dtype: image
  - name: label_6
    dtype: image
  - name: label_7
    dtype: image
  - name: label_8
    dtype: image
  - name: label_9
    dtype: image
  - name: label_10
    dtype: image
  - name: label_11
    dtype: image
  - name: label_12
    dtype: image
  - name: label_13
    dtype: image
  - name: label_14
    dtype: image
  splits:
  - name: train
    num_bytes: 107074809546.0
    num_examples: 69800
  download_size: 107081584776
  dataset_size: 107074809546.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# BDD100K Train

This dataset was created for [DeDelayed: Deleting Remote Inference Delay via On-Device Correction](https://huggingface.co/papers/2510.13714) (CVPR 2026). Code is available at [InterDigitalInc/dedelayed](https://github.com/InterDigitalInc/dedelayed).

The underlying data is derived from the [BDD100K](https://www.bdd100k.com/) driving video dataset. It contains 69,800 training sequences of 15 frames each.

## Usage

This dataset is the training split. Use it together with [danjacobellis/bdd500_pl_f14](https://huggingface.co/datasets/danjacobellis/bdd500_pl_f14) as follows:

```python
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 training `collate_fn`, see the [reference training notebook](https://github.com/InterDigitalInc/dedelayed/blob/papers/2026-cvpr-dedelayed/bdd100k_mixed_res.ipynb).

## 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

```bibtex
@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}
}
```