| 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 | |
| - zero-shot-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={}} | |
| ``` |