Method Details


Details for method 'RRR-ResNet152-MultiScale'

 

Method overview

name RRR-ResNet152-MultiScale
challenge pixel-level semantic labeling
details update: this submission actually used the coarse labels, which was previously not marked accordingly
publication Anonymous
project page / code
used Cityscapes data fine annotations, coarse annotations
used external data ImageNet
runtime n/a
subsampling no
submission date September, 2016
previous submissions

 

Average results

Metric Value
IoU Classes 75.7876
iIoU Classes 48.5119
IoU Categories 89.2502
iIoU Categories 73.9681

 

Class results

Class IoU iIoU
road 98.3166 -
sidewalk 83.9747 -
building 92.0444 -
wall 50.8108 -
fence 54.501 -
pole 62.5959 -
traffic light 67.7161 -
traffic sign 73.721 -
vegetation 92.8241 -
terrain 70.8051 -
sky 94.9583 -
person 82.6301 60.6025
rider 60.6455 37.9521
car 94.9582 88.4288
truck 65.2808 26.1271
bus 83.0683 39.5569
train 76.5937 43.0835
motorcycle 63.2536 38.7812
bicycle 71.2663 53.5634

 

Category results

Category IoU iIoU
flat 98.5059 -
nature 92.4816 -
object 68.9638 -
sky 94.9583 -
construction 92.3136 -
human 83.2252 61.7922
vehicle 94.303 86.1441

 

Links

Download results as .csv file

Benchmark page