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CamVid (Cambridge-Driving Labeled Video Database)
Aesthetics
|...
许可协议: CC-BY-SA 4.0

Overview

N.B. The owner of this Dataset is The University of Cambridge.

I'm not in any way affiliated with The University of Cambridge. I just thought it would be nice for people to have this dataset available on Kaggle.

Context

The Cambridge-driving Labeled Video Database (CamVid) provides ground truth labels that associate each pixel with one of 32 semantic classes. This dataset is often used in (real-time) semantic segmentation research.

The dataset is split up as follows:

  • 367 training pairs
  • 101 validation pairs
  • 233 test pairs

These splits are also used in many academic papers on semantic segmentation (Brostow et al., 2008b; Sturgess et al., 2009; Badrinarayanan
et al., 2017; Yu et al., 2020).

The images and masks for each split are in a separate directory.

Citations

(1)
Segmentation and Recognition Using Structure from Motion Point Clouds, ECCV 2008
Brostow, Shotton, Fauqueur, Cipolla

(2)
Semantic Object Classes in Video: A High-Definition Ground Truth Database
Pattern Recognition Letters
Brostow, Fauqueur, Cipolla

The original dataset can be found here:
http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid

Source / Contact

http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid

Image Source

https://sthalles.github.io/deep_segmentation_network

数据概要
数据格式
image,
数据量
1.403K
文件大小
71.87MB
发布方
Carlo Lepelaars
| 数据量 1.403K | 大小 71.87MB
CamVid (Cambridge-Driving Labeled Video Database)
Aesthetics
许可协议: CC-BY-SA 4.0

Overview

N.B. The owner of this Dataset is The University of Cambridge.

I'm not in any way affiliated with The University of Cambridge. I just thought it would be nice for people to have this dataset available on Kaggle.

Context

The Cambridge-driving Labeled Video Database (CamVid) provides ground truth labels that associate each pixel with one of 32 semantic classes. This dataset is often used in (real-time) semantic segmentation research.

The dataset is split up as follows:

  • 367 training pairs
  • 101 validation pairs
  • 233 test pairs

These splits are also used in many academic papers on semantic segmentation (Brostow et al., 2008b; Sturgess et al., 2009; Badrinarayanan
et al., 2017; Yu et al., 2020).

The images and masks for each split are in a separate directory.

Citations

(1)
Segmentation and Recognition Using Structure from Motion Point Clouds, ECCV 2008
Brostow, Shotton, Fauqueur, Cipolla

(2)
Semantic Object Classes in Video: A High-Definition Ground Truth Database
Pattern Recognition Letters
Brostow, Fauqueur, Cipolla

The original dataset can be found here:
http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid

Source / Contact

http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid

Image Source

https://sthalles.github.io/deep_segmentation_network

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