farnez/drive-synood-od-data

Drive-SynOOD-OD

A Diffusion-Inpainted Synthetic Out-of-Distribution Object Detection Benchmark for Autonomous Driving.

An evaluation-only benchmark for out-of-distribution (OOD) object detection in driving scenes: BDD100K validation images augmented with synthetic OOD objects (Wild Boar, Roe Deer, Other Deer, Dog, Stroller, Scooter) inserted via a diffusion inpainting model, alongside the original BDD labels and reconstructed OOD boxes. Detectors are trained on BDD100K train elsewhere and evaluated here — there is no train split.

• 24,079 images — 10,000 original .jpg + 14,079 OOD-augmented .png.
• Eval subsets: ood_positive (14,076) · clean_paired (5,618) · clean_extra (4,382) · clean_all (10,000) · ood_unannotated (3, excluded).
• OOD classes (1-based): 1 Dog · 2 Other Deer · 3 Roe Deer · 4 Scooter · 5 Stroller · 6 Wild Boar.

Contents & usage

This repository ships the benchmark as a single archive, drive-synood-od-data.tar, containing dataset/ (images, COCO/YOLO annotations, eval splits, and the FiftyOne samples.json) and provenance documents/. Download, extract, and use the companion package:

hf download farnez/drive-synood-od-data drive-synood-od-data.tar --repo-type dataset --local-dir .   # replace owner if different
tar xf drive-synood-od-data.tar
pip install "drive-synood-od @ git+https://github.com/FabioArnez/drive-synood-od.git"

Code, documentation, eval pipeline, and the full construction plan: https://github.com/FabioArnez/drive-synood-od

License & attribution

This dataset is derived from BDD100K (© 2018 Fisher Yu / Berkeley DeepDrive), released under the BSD 3-Clause License. Please refer to the original BDD100K license terms for usage of the underlying images and labels. The synthetic OOD-augmented images and the OOD annotations added in this work are provided under the same BSD 3-Clause terms. When using this dataset, please cite both BDD100K and Drive-SynOOD-OD, and do not use the names of the original authors or UC Berkeley to endorse or promote derived work.

Citation

@inproceedings{yu2020bdd100k,
  title     = {{BDD100K}: A Diverse Driving Dataset for Heterogeneous Multitask Learning},
  author    = {Yu, Fisher and Chen, Haofeng and Wang, Xin and Xian, Wenqi and Chen, Yingying and
               Liu, Fangchen and Madhavan, Vashisht and Darrell, Trevor},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2020},
}

@misc{arnez2026drivesynoodod,
  title  = {Drive-SynOOD-OD: A Diffusion-Inpainted Synthetic Out-of-Distribution Object
            Detection Benchmark for Autonomous Driving},
  author = {Arnez, Fabio and others},
  year   = {2026},
  note   = {CEA-LIST. https://github.com/FabioArnez/drive-synood-od},
}