Semantic Segmentation on PlantSeg
29.77mIoUDeCon-ML-L
Evaluation results
| Method | Links | |
|---|---|---|
| DeCon-ML-LLabel percentage=100%, Backbone=ResNet-50, Pre-trained dataset=COCO, Alpha (α)=0, Dropout=0.5, Decoder architecture=FPN2025.03 | 29.77 | |
| DeCon-SLLabel percentage=100%, Backbone=ResNet-50, Pre-trained dataset=COCO, Alpha (α)=0.25, Decoder architecture=FCN2025.03 | 29.39 | |
| DeCon-ML-SLabel percentage=100%, Backbone=ResNet-50, Pre-trained dataset=COCO, Alpha (α)=0, Dropout=0.5, Decoder architecture=FPN2025.03 | 29.16 | |
| SlotConLabel percentage=100%, Backbone=ResNet-50, Pre-trained dataset=COCO2025.03 | 28.78 | |
| Random Init.Label percentage=100%, Backbone=ResNet-502025.03 | 24.96 | |
| DeCon-ML-SLabel percentage=10%, Backbone=ResNet-50, Pre-trained dataset=COCO, Alpha (α)=0, Dropout=0.5, Decoder architecture=FPN2025.03 | 21 | |
| SlotConLabel percentage=10%, Backbone=ResNet-50, Pre-trained dataset=COCO2025.03 | 20.72 | |
| DeCon-ML-LLabel percentage=10%, Backbone=ResNet-50, Pre-trained dataset=COCO, Alpha (α)=0, Dropout=0.5, Decoder architecture=FPN2025.03 | 20.66 | |
| DeCon-SLLabel percentage=10%, Backbone=ResNet-50, Pre-trained dataset=COCO, Alpha (α)=0.25, Decoder architecture=FCN2025.03 | 20.56 | |
| Random Init.Label percentage=10%, Backbone=ResNet-502025.03 | 16.96 |