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Dataset Card for sidewalk-semantic

Dataset Summary

A dataset of sidewalk images gathered in Belgium in the summer of 2021. Label your own semantic segmentation datasets on segments.ai

Supported Tasks and Leaderboards

  • semantic-segmentation: The dataset can be used to train a semantic segmentation model, where each pixel is classified. The model performance is measured by how high its mean IoU (intersection over union) to the reference is.

Dataset Structure

Data categories

Id Name Description
0 unlabeled -
1 flat-road -
2 flat-sidewalk -
3 flat-crosswalk -
4 flat-cyclinglane -
5 flat-parkingdriveway -
6 flat-railtrack -
7 flat-curb -
8 human-person -
9 human-rider -
10 vehicle-car -
11 vehicle-truck -
12 vehicle-bus -
13 vehicle-tramtrain -
14 vehicle-motorcycle -
15 vehicle-bicycle -
16 vehicle-caravan -
17 vehicle-cartrailer -
18 construction-building -
19 construction-door -
20 construction-wall -
21 construction-fenceguardrail -
22 construction-bridge -
23 construction-tunnel -
24 construction-stairs -
25 object-pole -
26 object-trafficsign -
27 object-trafficlight -
28 nature-vegetation -
29 nature-terrain -
30 sky -
31 void-ground -
32 void-dynamic -
33 void-static -
34 void-unclear -

Data Instances

[Needs More Information]

Data Fields

[Needs More Information]

Data Splits

This dataset only contains one split.

Dataset Creation

Curation Rationale

[Needs More Information]

Source Data

Initial Data Collection and Normalization

[Needs More Information]

Who are the source language producers?

[Needs More Information]

Annotations

Annotation process

[Needs More Information]

Who are the annotators?

[Needs More Information]

Personal and Sensitive Information

[Needs More Information]

Considerations for Using the Data

Social Impact of Dataset

[Needs More Information]

Discussion of Biases

[Needs More Information]

Other Known Limitations

[Needs More Information]

Additional Information

Dataset Curators

[Needs More Information]

Licensing Information

[Needs More Information]

Citation Information

[Needs More Information]

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