graviti
产品服务
解决方案
知识库
公开数据集
关于我们
avatar
Cityscapes Dataset
2D Box
Urban
|Autonomous Driving
|...
许可协议: Unknown

Overview

We present a new large-scale dataset that contains a diverse set of stereo video sequences recorded in street scenes from 50 different cities, with high quality pixel-level annotations of 5 000 frames in addition to a larger set of 20 000 weakly annotated frames. The dataset is thus an order of magnitude larger than similar previous attempts. Details on annotated classes and examples of our annotations are available at this webpage.
The Cityscapes Dataset is intended for assessing the performance of vision algorithms for two major tasks of semantic urban scene understanding: pixel-level and instance-level semantic labeling; supporting research that aims to exploit large volumes of (weakly) annotated data, e.g. for training deep neural networks.
License
This Cityscapes Dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications, or personal experimentation. Permission is granted to use the data given that you agree to our license terms.

数据概要
数据格式
image,
数据量
--
文件大小
--
发布方
Marius Cordts,Mohamed Omran,Sebastian Ramos,Timo Rehfeld,Markus Enzweiler,Rodrigo Benenson,Uwe Franke,Stefan Roth,Bernt Schiele
| 数据量 -- | 大小 --
Cityscapes Dataset
2D Box
Urban | Autonomous Driving
许可协议: Unknown

Overview

We present a new large-scale dataset that contains a diverse set of stereo video sequences recorded in street scenes from 50 different cities, with high quality pixel-level annotations of 5 000 frames in addition to a larger set of 20 000 weakly annotated frames. The dataset is thus an order of magnitude larger than similar previous attempts. Details on annotated classes and examples of our annotations are available at this webpage.
The Cityscapes Dataset is intended for assessing the performance of vision algorithms for two major tasks of semantic urban scene understanding: pixel-level and instance-level semantic labeling; supporting research that aims to exploit large volumes of (weakly) annotated data, e.g. for training deep neural networks.
License
This Cityscapes Dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications, or personal experimentation. Permission is granted to use the data given that you agree to our license terms.

0
立即开始构建AI
graviti
wechat-QR
长按保存识别二维码,关注Graviti公众号

Copyright@Graviti
沪ICP备19019574号
沪公网安备 31011002004865号