---
license: bsd-3-clause
language:
- en
task_categories:
- object-detection
pretty_name: "Drive-SynOOD-OD"
size_categories:
- 10K<n<100K
tags:
- object-detection
- out-of-distribution
- ood-detection
- anomaly-detection
- autonomous-driving
- driving
- bdd100k
- synthetic
- diffusion-inpainting
- benchmark
---

# Drive-SynOOD-OD

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

![Drive-SynOOD-OD examples: one inpainted OOD object per class in real BDD100K scenes, with ground-truth boxes](assets/teaser.jpg)

*One example per OOD class, inserted into real BDD100K validation scenes by diffusion inpainting, across placement
positions, times of day, and weather. Red boxes are the quality-controlled ground truth. Every OOD image is paired
with its unmodified base image for counterfactual comparison.*

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` (14,079) · `ood_positive_hiconf` (7,792, QA-gated high-confidence filter over
  `ood_positive`) · `clean_paired` (5,618) · `clean_extra` (4,382) · `clean_all` (10,000).
- **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
and extract it:

```bash
hf download CEAai/Drive-SynOOD-OD drive-synood-od-data.tar --repo-type dataset --local-dir .
tar xf drive-synood-od-data.tar
```

The current (v3) OOD annotations are also published as loose files next to the archive: `ood_annotations.json` and
`splits/` (manifest, per-subset lists, COCO views, and the `ood_positive_hiconf` card). They **supersede** the
annotation files inside the archive, which date from the initial release. Pin revision `v3-annotations` to reproduce
the paper results.

The companion code (dataloaders, detector adapters, and the evaluation pipeline) will be released on the CEA-List
GitHub; the link will be added here.

## License & attribution

This dataset is **derived from [BDD100K](https://github.com/bdd100k/bdd100k)** (© 2018 Fisher Yu / Berkeley DeepDrive),
released under the **BSD 3-Clause License**. Please refer to the original
[BDD100K license terms](https://github.com/bdd100k/bdd100k/blob/master/LICENSE) 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

```bibtex
@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},
}

@inproceedings{montoya2026drivesynoodod,
  title     = {{Drive-SynOOD-OD}: A Diffusion-Inpainted Synthetic Out-of-Distribution Object
               Detection Benchmark for Autonomous Driving},
  author    = {Montoya, Daniel and Espinoza Mayzer, Mauricio Sayri and Arnez, Fabio},
  booktitle = {2026 IEEE International Conference on Vehicular Electronics and Safety (ICVES)},
  address   = {Cochabamba, Bolivia},
  month     = nov,
  year      = {2026},
  publisher = {IEEE},
}
```