Dynamic Head: Unifying Object Detection Heads with Attentions

Benchmark Model Rank Results
object-detection-on-cocoDyHead (Swin-L, multi scale, self-training)#25box mAP: 60.6AP50: 78.5AP75: 66.6APM: 64.0APL: 74.2
object-detection-on-cocoDyHead (Swin-L, multi scale)#34box mAP: 58.7AP50: 77.1AP75: 64.5APM: 62.0APL: 72.8
object-detection-on-cocoDyHead (ResNeXt-64x4d-101-DCN, multi scale)#57box mAP: 54AP50: 72.1AP75: 59.3
object-detection-on-cocoDyHead (ResNeXt-64x4d-101)#115box mAP: 47.7AP50: 65.7AP75: 51.9
object-detection-on-cocoDyHead (ResNet-50)#161box mAP: 43AP50: 60.7AP75: 46.8
object-detection-on-coco-2017-valDyHead (Swin-T, multi scale)#33AP50: 68AP75: 54.3APL: 64.2
object-detection-on-coco-minivalDyHead (Swin-L, multi scale, self-training)#26box AP: 60.3AP50: 78.2APL: 74.2
object-detection-on-coco-minivalDyHead (Swin-L, multi scale)#36box AP: 58.4AP50: 76.8APS: 44.5APM: 62.2APL: 73.2
object-detection-on-coco-minivalDyHead (ResNet-101)#103box AP: 46.5
object-detection-on-coco-minivalDyHead (ResNeXt-64x4d-101-DCN, multi scale)#219APL: 66.3
object-detection-on-coco-oDyHead (Swin-L)#9Average mAP: 35.3Effective Robustness: 10.00
object-detection-on-coco-oDyHead (ResNet-50)#31Average mAP: 19.3Effective Robustness: 0.16