| | --- |
| | 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={}} |
| | ``` |
