Object Detection on Detecting Diseases
48.85APDeCon-SL
Evaluation results
| Method | Links | |
|---|---|---|
| DeCon-SLLabel percentage=100%, Backbone=ResNet-50, Pre-trained dataset=COCO, Alpha (α)=0.25, Decoder architecture=FCN2025.03 | 48.85 | |
| DeCon-ML-LLabel percentage=100%, Backbone=ResNet-50, Pre-trained dataset=COCO, Alpha (α)=0, Dropout=0.5, Decoder architecture=FPN2025.03 | 48.57 | |
| DeCon-ML-SLabel percentage=100%, Backbone=ResNet-50, Pre-trained dataset=COCO, Alpha (α)=0, Dropout=0.5, Decoder architecture=FPN2025.03 | 48.53 | |
| SlotConLabel percentage=100%, Backbone=ResNet-50, Pre-trained dataset=COCO2025.03 | 48.45 | |
| Random Init.Label percentage=100%, Backbone=ResNet-502025.03 | 34.76 | |
| DeCon-ML-LLabel percentage=10%, Backbone=ResNet-50, Pre-trained dataset=COCO, Alpha (α)=0, Dropout=0.5, Decoder architecture=FPN2025.03 | 27.16 | |
| DeCon-ML-SLabel percentage=10%, Backbone=ResNet-50, Pre-trained dataset=COCO, Alpha (α)=0, Dropout=0.5, Decoder architecture=FPN2025.03 | 26.82 | |
| DeCon-SLLabel percentage=10%, Backbone=ResNet-50, Pre-trained dataset=COCO, Alpha (α)=0.25, Decoder architecture=FCN2025.03 | 26.81 | |
| SlotConLabel percentage=10%, Backbone=ResNet-50, Pre-trained dataset=COCO2025.03 | 26.54 | |
| Random Init.Label percentage=10%, Backbone=ResNet-502025.03 | 19.14 |