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={}} | |
``` |