README.md

---
license: cc-by-4.0
task_categories:
- image-segmentation
- object-detection
task_ids:
- semantic-segmentation
- instance-segmentation
tags:
- automotive
- autonomous driving
- synthetic
- safe ai
- validation
- pedestrian detection
- 2d object-detection
- 3d object-detection
- semantic-segmentation
- instance-segmentation
pretty_name: VALERIE22
size_categories:
- 1K<n<10K
---
# VALERIE22 - A photorealistic, richly metadata annotated dataset of urban environments

## Dataset Description

- **Paper:** https://arxiv.org/abs/2308.09632
- **Point of Contact:** korbinian.hagn@intel.com

### Dataset Summary

The VALERIE22 dataset was generated with the VALERIE procedural tools pipeline providing a photorealistic sensor simulation rendered from automatically synthesized scenes. The dataset provides a uniquely rich set of metadata, allowing extraction of specific scene and semantic features (like pixel-accurate occlusion rates, positions in the scene and distance + angle to the camera). This enables a multitude of possible tests on the data and we hope to stimulate research on understanding performance of DNNs.

### Supported Tasks and Leaderboards

- pedestrian detection
- 2d object-detection
- 3d object-detection
- semantic-segmentation
- instance-segmentation
- ai-validation

## Dataset Structure
 
```
VALERIE22
└───intel_results_sequence_0050
│   └───ground-truth
│   │   └───2d-bounding-box_json   
│   │   │   └───car-camera000-0000-{UUID}-0000.json
│   │   └───3d-bounding-box_json
│   │   │   └───car-camera000-0000-{UUID}-0000.json
│   │   └───class-id_png
│   │   │   └───car-camera000-0000-{UUID}-0000.png
│   │   └───general-globally-per-frame-analysis_json
│   │   │   └───car-camera000-0000-{UUID}-0000.json
│   │   │   └───car-camera000-0000-{UUID}-0000.csv
│   │   └───semantic-group-segmentation_png
│   │   │   └───car-camera000-0000-{UUID}-0000.png
│   │   └───semantic-instance-segmentation_png
│   │   │   └───car-camera000-0000-{UUID}-0000.png
│   │   │   └───car-camera000-0000-{UUID}-0000
│   │   │   │   └───{Entity-ID}
│   └───sensor
│   │   └───camera
│   │   │   └───left
│   │   │   │   └───png
│   │   │   │   │   └───car-camera000-0000-{UUID}-0000.png
│   │   │   │   └───png_distorted
│   │   │   │   │   └───car-camera000-0000-{UUID}-0000.png
└───intel_results_sequence_0052
└───intel_results_sequence_0054
└───intel_results_sequence_0057
└───intel_results_sequence_0058
└───intel_results_sequence_0059
└───intel_results_sequence_0060
└───intel_results_sequence_0062
```

### Data Splits

Train/Validation/Test splits are provided

### Licensing Information

Creative Commons Zero v1.0 Universal

### Citation Information
Relevant publications:

```
@misc{grau2023valerie22,
    title={VALERIE22 -- A photorealistic, richly metadata annotated dataset of urban environments},
    author={Oliver Grau and Korbinian Hagn},
    year={2023},
    eprint={2308.09632},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

@inproceedings{hagn2022increasing,
  title={Increasing pedestrian detection performance through weighting of detection impairing factors},
  author={Hagn, Korbinian and Grau, Oliver},
  booktitle={Proceedings of the 6th ACM Computer Science in Cars Symposium},
  pages={1--10},
  year={2022}
}

@inproceedings{hagn2022validation,
  title={Validation of Pedestrian Detectors by Classification of Visual Detection Impairing Factors},
  author={Hagn, Korbinian and Grau, Oliver},
  booktitle={European Conference on Computer Vision},
  pages={476--491},
  year={2022},
  organization={Springer}
}

@incollection{grau2022variational,
  title={A variational deep synthesis approach for perception validation},
  author={Grau, Oliver and Hagn, Korbinian and Syed Sha, Qutub},
  booktitle={Deep Neural Networks and Data for Automated Driving: Robustness, Uncertainty Quantification, and Insights Towards Safety},
  pages={359--381},
  year={2022},
  publisher={Springer International Publishing Cham}
}

@incollection{hagn2022optimized,
  title={Optimized data synthesis for DNN training and validation by sensor artifact simulation},
  author={Hagn, Korbinian and Grau, Oliver},
  booktitle={Deep Neural Networks and Data for Automated Driving: Robustness, Uncertainty Quantification, and Insights Towards Safety},
  pages={127--147},
  year={2022},
  publisher={Springer International Publishing Cham}
}

@inproceedings{syed2020dnn,
  title={DNN analysis through synthetic data variation},
  author={Syed Sha, Qutub and Grau, Oliver and Hagn, Korbinian},
  booktitle={Proceedings of the 4th ACM Computer Science in Cars Symposium},
  pages={1--10},
  year={2020}
}
```