AnomalySegmentation_CourseProjectBaseCode/eval/results_miou/miou_void.txt at main · ImBlurryF4c3/AnomalySegmentation_CourseProjectBaseCode · GitHub

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================================ Model:ENet con modello pretrainato e reitranato su 20 classi per 280 epoche (+ 30 rispetto al best model) ================================
Per-Class IoU:
Road -----> 40.81
sidewalk -----> 0.32
building -----> 16.91
wall -----> 0.44
fence -----> 0.82
pole -----> 2.88
traffic light -----> 0.50
traffic sign -----> 0.05
vegetation -----> 0.00
terrain -----> 0.00
sky -----> 1.46
person -----> 0.00
rider -----> 0.00
car -----> 0.00
truck -----> 0.16
bus -----> 5.53
train -----> 0.04
motorcycle -----> 0.00
bicycle -----> 0.00
=======================================
MEAN IoU: 3.68% with method: msp with temperature: 1.0


================================ Model:BiSeNet  con modello pretrainato e reitranato su 20 classi  ================================
Per-Class IoU:
Road -----> 84.48
sidewalk -----> 40.80
building -----> 71.29
wall -----> 2.92
fence -----> 14.63
pole -----> 20.58
traffic light -----> 12.95
traffic sign -----> 33.59
vegetation -----> 69.15
terrain -----> 21.53
sky -----> 82.61
person -----> 43.55
rider -----> 8.34
car -----> 69.18
truck -----> 15.20
bus -----> 7.79
train -----> 21.14
motorcycle -----> 4.81
bicycle -----> 35.24
=======================================
MEAN IoU: 34.73% with method: msp with temperature: 1.0


================================ Model:ERFNet pretrainato e trainato su 20 classi ================================
Per-Class IoU:
Road -----> 91.19
sidewalk -----> 74.19
building -----> 79.88
wall -----> 43.58
fence -----> 45.63
pole -----> 44.84
traffic light -----> 21.53
traffic sign -----> 51.47
vegetation -----> 86.47
terrain -----> 48.33
sky -----> 85.01
person -----> 61.71
rider -----> 39.38
car -----> 89.31
truck -----> 40.49
bus -----> 52.85
train -----> 23.55
motorcycle -----> 31.57
bicycle -----> 57.15
=======================================
MEAN IoU: 56.22% with method: msp with temperature: 1.0