Datasets:

Intel
/

VALERIE22

---
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
<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/teaser_c.png">

## Dataset Description

- **Paper:** https://arxiv.org/abs/2308.09632
- **Point of Contact:** [email protected]

### Dataset Summary

The VALERIE22 dataset was generated with the VALERIE procedural tools pipeline (see image below) 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.

<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/VALERIE_overview1.png">

Each sequence of the dataset contains for each scene two rendered images. One is rendered with the default Blender tonemapping (/png) whereas the second is renderd with our photorealistic sensor simulation (see hagn2022optimized). The image below shows the difference of the two methods.

<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/SensorSimulation.png">

Following are some example images showing the unique characteristics of the different sequences.

|Sequence0052|Sequence0054|Sequence0057|Sequence0058|
|:---:|:---:|:---:|:---:|
|<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/seq52_1.jpg" width="500">|<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/seq54_1.jpg" width="500">|<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/seq57_1.jpg" width="500">|<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/seq58_1.png" width="500">|

|Sequence0059|Sequence0060|Sequence0062|
|:---:|:---:|:---:|
|<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/seq59_1.jpg" width="500">|<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/seq60_1.jpg" width="500">|<img src="https://huggingface.co/datasets/Intel/VALERIE22/resolve/main/images/seq62_1.jpg" width="500">|



### Supported Tasks

- 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

13476 images for trainining:
```
dataset = load_dataset("Intel/VALERIE22", split="train")
```

8406 images for validation and test:
```
dataset = load_dataset("Intel/VALERIE22", split="validation")
dataset = load_dataset("Intel/VALERIE22", split="test")
```

### Licensing Information

CC BY 4.0

## Grant Information

Generated within project KI-Abischerung with funding of the German Federal Ministry of Industry and Energy under grant number 19A19005M.

### 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}
}
```