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--- |
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language: |
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- en |
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license: gpl-3.0 |
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tags: |
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- vision |
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- image-segmentation |
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- instance-segmentation |
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- object-detection |
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- optical-flow |
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- depth |
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- synthetic |
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- sim-to-real |
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annotations_creators: |
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- machine-generated |
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pretty_name: SMVB Dataset |
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size_categories: |
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- 1K<n<10K |
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task_categories: |
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- object-detection |
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- image-segmentation |
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- depth-estimation |
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- video-classification |
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- other |
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task_ids: |
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- instance-segmentation |
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- semantic-segmentation |
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--- |
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# Synthetic Multimodal Video Benchmark (SMVB) |
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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. |
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### Supported Tasks and Leaderboards |
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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. |
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## Dataset Structure |
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### Data Instances |
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### Data Fields |
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### Data Splits |
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## Dataset Creation |
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### Curation Rationale |
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### Source Data |
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### Citation Information |
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```bibtex |
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@INPROCEEDINGS{karoly2024synthetic, |
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author={Károly, Artúr I. and Nádas, Imre and Galambos, Péter}, |
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booktitle={2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics (SAMI)}, |
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title={Synthetic Multimodal Video Benchmark (SMVB): Utilizing Blender for rich dataset generation}, |
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year={2024}, |
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volume={}, |
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number={}, |
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pages={}, |
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doi={}} |
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``` |
