| --- |
| 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:** korbinian.hagn@intel.com |
|
|
| ### 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} |
| } |
| ``` |
