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NEXET
2D Box
Autonomous Driving
|...
许可协议: Research Only

Overview

NEXET, the Nexar dataset, is a massive set consisting of 50,000 images from all over the world with bounding box annotations of the rear of vehicles collected from a variety of locations, lighting, and weather conditions.

The set is comprised of 50,000 training images and 5,000 test images.

The training images were collected through randomly sampling Nexar’s database of images, which were all taken by drivers using the Nexar dashcam. Filtering was deployed on the dataset in order to balance between images taken at day (~50%) and images taken during the night (~46%), with a small amount of images taken in twilight lighting conditions (~4%).

The test set was produced as follows:

  • A set of 41,190 annotated images was extracted from the same distribution as the train set
  • An expert reviewed the annotations and made sure all bounding boxes are tightly and accurately positioned

More details can be found in this blog post.

数据概要
数据格式
image,
数据量
50K
文件大小
--
发布方
Nexar
| 数据量 50K | 大小 --
NEXET
2D Box
Autonomous Driving
许可协议: Research Only

Overview

NEXET, the Nexar dataset, is a massive set consisting of 50,000 images from all over the world with bounding box annotations of the rear of vehicles collected from a variety of locations, lighting, and weather conditions.

The set is comprised of 50,000 training images and 5,000 test images.

The training images were collected through randomly sampling Nexar’s database of images, which were all taken by drivers using the Nexar dashcam. Filtering was deployed on the dataset in order to balance between images taken at day (~50%) and images taken during the night (~46%), with a small amount of images taken in twilight lighting conditions (~4%).

The test set was produced as follows:

  • A set of 41,190 annotated images was extracted from the same distribution as the train set
  • An expert reviewed the annotations and made sure all bounding boxes are tightly and accurately positioned

More details can be found in this blog post.

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