Object Detection on PlantDoc
40.03APDeCon-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 | 40.03 | |
| DeCon-SLLabel percentage=100%, Backbone=ResNet-50, Pre-trained dataset=COCO, Alpha (α)=0.25, Decoder architecture=FCN2025.03 | 39.94 | |
| DeCon-ML-SLabel percentage=100%, Backbone=ResNet-50, Pre-trained dataset=COCO, Alpha (α)=0, Dropout=0.5, Decoder architecture=FPN2025.03 | 39.4 | |
| SlotConLabel percentage=100%, Backbone=ResNet-50, Pre-trained dataset=COCO2025.03 | 38.37 | |
| Random Init.Label percentage=100%, Backbone=ResNet-502025.03 | 19.81 | |
| DeCon-ML-LLabel percentage=10%, Backbone=ResNet-50, Pre-trained dataset=COCO, Alpha (α)=0, Dropout=0.5, Decoder architecture=FPN2025.03 | 17.98 | |
| DeCon-ML-SLabel percentage=10%, Backbone=ResNet-50, Pre-trained dataset=COCO, Alpha (α)=0, Dropout=0.5, Decoder architecture=FPN2025.03 | 17.82 | |
| SlotConLabel percentage=10%, Backbone=ResNet-50, Pre-trained dataset=COCO2025.03 | 17.53 | |
| DeCon-SLLabel percentage=10%, Backbone=ResNet-50, Pre-trained dataset=COCO, Alpha (α)=0.25, Decoder architecture=FCN2025.03 | 17.06 | |
| Random Init.Label percentage=10%, Backbone=ResNet-502025.03 | 6.59 |