Semi-supervised Domain Adaptation on Cityscapes GTA5 to Cityscapes (val)
65.6mIoUSemiSeg-Contrastive
Evaluation Results
| Method | Links | |
|---|---|---|
| SemiSeg-ContrastiveLabeled Ratio=1/3, Backbone=Deeplabv2 with ResNet-101, Training=ImageNet pre-trained, Domain Adaptation=with domain adaptation2021.04 | 65.6 | |
| Liu et al.Labeled Ratio=1/3, Backbone=Deeplabv2 with ResNet-101, Training=ImageNet pre-trained, Domain Adaptation=with domain adaptation2021.04 | 64.6 | |
| ASSLabeled Ratio=1/3, Backbone=Deeplabv2 with ResNet-101, Training=ImageNet pre-trained, Domain Adaptation=with domain adaptation2021.04 | 64.5 | |
| SemiSeg-ContrastiveLabeled Ratio=1/6, Backbone=Deeplabv2 with ResNet-101, Training=ImageNet pre-trained, Domain Adaptation=with domain adaptation2021.04 | 64.2 | |
| SemiSeg-ContrastiveLabeled Ratio=1/15, Backbone=Deeplabv2 with ResNet-101, Training=ImageNet pre-trained, Domain Adaptation=with domain adaptation2021.04 | 62 | |
| Liu et al.Labeled Ratio=1/6, Backbone=Deeplabv2 with ResNet-101, Training=ImageNet pre-trained, Domain Adaptation=with domain adaptation2021.04 | 60.4 | |
| ASSLabeled Ratio=1/6, Backbone=Deeplabv2 with ResNet-101, Training=ImageNet pre-trained, Domain Adaptation=with domain adaptation2021.04 | 60.2 | |
| SemiSeg-ContrastiveLabeled Ratio=1/30, Backbone=Deeplabv2 with ResNet-101, Training=ImageNet pre-trained, Domain Adaptation=with domain adaptation2021.04 | 59.9 | |
| Liu et al.Labeled Ratio=1/15, Backbone=Deeplabv2 with ResNet-101, Training=ImageNet pre-trained, Domain Adaptation=with domain adaptation2021.04 | 57 | |
| ASSLabeled Ratio=1/15, Backbone=Deeplabv2 with ResNet-101, Training=ImageNet pre-trained, Domain Adaptation=with domain adaptation2021.04 | 56 | |
| Liu et al.Labeled Ratio=1/30, Backbone=Deeplabv2 with ResNet-101, Training=ImageNet pre-trained, Domain Adaptation=with domain adaptation2021.04 | 55.2 | |
| ASSLabeled Ratio=1/30, Backbone=Deeplabv2 with ResNet-101, Training=ImageNet pre-trained, Domain Adaptation=with domain adaptation2021.04 | 54.2 |