| --- |
| language: |
| - en |
| license: gpl-3.0 |
| tags: |
| - vision |
| - image-segmentation |
| - instance-segmentation |
| - object-detection |
| - optical-flow |
| - depth |
| - synthetic |
| - sim-to-real |
| annotations_creators: |
| - machine-generated |
| pretty_name: SMVB Dataset |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - object-detection |
| - image-segmentation |
| - depth-estimation |
| - video-classification |
| - other |
| task_ids: |
| - instance-segmentation |
| - semantic-segmentation |
| --- |
| |
| # Synthetic Multimodal Video Benchmark (SMVB) |
|
|
| A dataset consisting of synthetic images from distinct synthetic scenes, annotated with object/instance/semantic segmentation masks, depth data, surface normal information and optical for testing and benchmarking model performance for multi-task/multi-objective learning. |
|
|
| ### Supported Tasks and Leaderboards |
|
|
| The dataset supports tasks such as semantic segmentation, instance segmentation, object detection, image classification, depth, surface normal, and optical flow estimation, and video object segmentation. |
|
|
| ## Dataset Structure |
|
|
| ### Data Instances |
|
|
|
|
| ### Data Fields |
|
|
| ### Data Splits |
|
|
|
|
| ## Dataset Creation |
|
|
| ### Curation Rationale |
|
|
| ### Source Data |
|
|
| ### Citation Information |
|
|
| ```bibtex |
| @INPROCEEDINGS{karoly2024synthetic, |
| author={Károly, Artúr I. and Nádas, Imre and Galambos, Péter}, |
| booktitle={2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics (SAMI)}, |
| title={Synthetic Multimodal Video Benchmark (SMVB): Utilizing Blender for rich dataset generation}, |
| year={2024}, |
| volume={}, |
| number={}, |
| pages={}, |
| doi={}} |
| ``` |
