Method Details


Details for method 'Kronecker Convolution Networks'

 

Method overview

name Kronecker Convolution Networks
challenge pixel-level semantic labeling
details We proposed a novel kronecker convolution networks for semantic image segmentation.
publication Anonymous
project page / code
used Cityscapes data fine annotations
used external data ImageNet
runtime 1 s
Tesla K80
subsampling no
submission date February, 2018
previous submissions

 

Average results

Metric Value
IoU Classes 78.8518
iIoU Classes 56.8644
IoU Categories 90.6926
iIoU Categories 77.4714

 

Class results

Class IoU iIoU
road 98.4268 -
sidewalk 85.3838 -
building 92.9535 -
wall 53.4949 -
fence 62.2114 -
pole 66.4596 -
traffic light 74.9408 -
traffic sign 79.0891 -
vegetation 93.4821 -
terrain 72.5224 -
sky 94.7897 -
person 86.2491 66.6433
rider 69.8628 48.4754
car 95.9119 89.1979
truck 67.8552 40.214
bus 83.753 51.9918
train 76.9482 52.7598
motorcycle 67.4482 43.3514
bicycle 76.4019 62.2819

 

Category results

Category IoU iIoU
flat 98.6634 -
nature 93.2069 -
object 72.8903 -
sky 94.7897 -
construction 93.4489 -
human 86.5793 67.6839
vehicle 95.2696 87.2589

 

Links

Download results as .csv file

Benchmark page