VMMRdb
Classification
Vehicle
|...
许可协议: MIT

Overview

The Vehicle Make and Model Recognition dataset (VMMRdb) is large in scale and diversity, containing 9,170 classes consisting of 291,752 images, covering models manufactured between 1950 and 2016. VMMRdb dataset contains images that were taken by different users, different imaging devices, and multiple view angles, ensuring a wide range of variations to account for various scenarios that could be encountered in a real-life scenario. The cars are not well aligned, and some images contain irrelevant background. The data covers vehicles from 712 areas covering all 412 sub-domains corresponding to US metro areas. Our dataset can be used as a baseline for training a robust model in several real-life scenarios for traffic surveillance.

Citation

If you use this dataset, please cite the following paper:

@inproceedings{tafazzoli2017large,
  title={A large and diverse dataset for improved vehicle make and model recognition},
  author={Tafazzoli, Faezeh and Frigui, Hichem and Nishiyama, Keishin},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
  pages={1--8},
  year={2017}
}

License

MIT

数据概要
数据格式
Image,
数据量
--
文件大小
11.51GB
发布方
University of Louisville
The University of Louisville is a public research university in Louisville, Kentucky.
数据集反馈
| 66 | 数据量 -- | 大小 11.51GB
VMMRdb
Classification
Vehicle
许可协议: MIT

Overview

The Vehicle Make and Model Recognition dataset (VMMRdb) is large in scale and diversity, containing 9,170 classes consisting of 291,752 images, covering models manufactured between 1950 and 2016. VMMRdb dataset contains images that were taken by different users, different imaging devices, and multiple view angles, ensuring a wide range of variations to account for various scenarios that could be encountered in a real-life scenario. The cars are not well aligned, and some images contain irrelevant background. The data covers vehicles from 712 areas covering all 412 sub-domains corresponding to US metro areas. Our dataset can be used as a baseline for training a robust model in several real-life scenarios for traffic surveillance.

Citation

If you use this dataset, please cite the following paper:

@inproceedings{tafazzoli2017large,
  title={A large and diverse dataset for improved vehicle make and model recognition},
  author={Tafazzoli, Faezeh and Frigui, Hichem and Nishiyama, Keishin},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
  pages={1--8},
  year={2017}
}

License

MIT

数据集反馈
0
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