zod 0.0.16

Zenseact Open Dataset

Zenseact Open Dataset

The Zenseact Open Dataset (ZOD) is a large multi-modal autonomous driving dataset developed by a team of researchers at Zenseact. The dataset is split into three categories: Frames, Sequences, and Drives. For more information about the dataset, please refer to our coming soon, or visit our website.

Examples

Find examples of how to use the dataset in the examples folder. Here you will find a set of jupyter notebooks that demonstrate how to use the dataset, as well as an example of how to train an object detection model using Detectron2.

Installation

To intstall the library only, run:

pip install zod

To install the library, along with the CLI, run:

pip install zod[cli]

To install the full devkit, with all dependencies, run:

pip install zod[all]

Download using the CLI

This is an example of how to download the ZOD Frames mini-dataset using the CLI. Prerequisites are that you have applied for access and received a download link. To download the mini-dataset, run:

zod download --url <download-link> --output-dir <path/to/outputdir> frames --mini

similarly, to download the full dataset (including all lidar scans before and after the keyframe), run:

zod download --url <download-link> --output-dir <path/to/outputdir> frames --lidar --num-scans-before -1 --num-scans-after -1 --oxts --images --blur --dnat --calibrations --annotations

this will download all the previous and future lidar scans (as num-scans-before=-1 and num-scans-after=-1), the OxTS data, the images (with both the blur and DNAT anonymization), the calibration files, the annotations, and all other necessary files. If you dont want any previous or future lidar scans, run:

zod download --url <download-link> --output-dir <path/to/outputdir> frames --lidar --num-scans-before 0 --num-scans-after 0 --oxts --images --blur --dnat --calibrations --annotations

For a full list of options for ZOD download, run:

zod download frames --help
zod download sequences --help

depending on which dataset you want to download.

Anonymization

To preserve privacy, the dataset is anonymized. The anonymization is performed by brighterAI, and we provide two separate modes of anonymization: deep fakes (DNAT) and blur. In our paper, we show that the performance of an object detector is not affected by the anonymization method. For more details regarding this experiment, please refer to our coming soon.

Citation

If you publish work that uses Zenseact Open Dataset, please cite: coming soon

@misc{zod2021,
  author = {TODO},
  title = {Zenseact Open Dataset},
  year = {2023},
  publisher = {TODO},
  journal = {TODO},

Contact

For questions about the dataset, please Contact Us.

Contributing

We welcome contributions to the development kit. If you would like to contribute, please open a pull request.

License

Dataset: This dataset is the property of Zenseact AB (© 2023 Zenseact AB) and is licensed under CC BY-SA 4.0. Any public use, distribution, or display of this dataset must contain this notice in full:

For this dataset, Zenseact AB has taken all reasonable measures to remove all personally identifiable information, including faces and license plates. To the extent that you like to request the removal of specific images from the dataset, please contact [e-mail].

Development kit: This development kit is the property of Zenseact AB (© 2023 Zenseact AB) and is licensed under MIT.