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SVIRO
2D Box
2D Polygon
Pose Estimation
|...
许可协议: CC-BY-NC-SA

Overview

SVIRO was created to investigate and benchmark machine learning approaches for application in the passenger compartment regarding common challenges of realistic engineering applications. In particular, SVIRO can be used to evaluate the generalization and robustness of machine learning models when trained on a limited number of variations.

The sceneries in the different vehicle interiors were generated randomly. We partitioned the available human models, child seats and backgrounds such that one part is only used for the training images (for all the vehicles) and the other part is used for the test images. Consequently, the dataset has an intrinsic dominant background, object and texture bias: all of the images are taken in a few passenger compartments, but generalization to new, unseen, passenger compartments and child seats should be achieved.

The dataset consists of 10 different vehicle interiors and 25,000 sceneries in total.

数据概要
数据格式
image,
数据量
25K
文件大小
--
发布方
Steve DIAS DA CRUZ
| 数据量 25K | 大小 --
SVIRO
2D Box 2D Polygon
Pose Estimation
许可协议: CC-BY-NC-SA

Overview

SVIRO was created to investigate and benchmark machine learning approaches for application in the passenger compartment regarding common challenges of realistic engineering applications. In particular, SVIRO can be used to evaluate the generalization and robustness of machine learning models when trained on a limited number of variations.

The sceneries in the different vehicle interiors were generated randomly. We partitioned the available human models, child seats and backgrounds such that one part is only used for the training images (for all the vehicles) and the other part is used for the test images. Consequently, the dataset has an intrinsic dominant background, object and texture bias: all of the images are taken in a few passenger compartments, but generalization to new, unseen, passenger compartments and child seats should be achieved.

The dataset consists of 10 different vehicle interiors and 25,000 sceneries in total.

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