File size: 1,545 Bytes
486b7b4 c174f79 486b7b4 c174f79 486b7b4 c174f79 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 |
---
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={}}
``` |