Object Detection on Steel Pipe
81.4mAP@0.5AGBP
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| AGBPBackbone=Swin-Base, Pretraining Dataset=Industrial, Base Pretraining=SimMIM2025.09 | 81.4 | 43.7 | |
| AGSSPBackbone=Swin-Base, Pretraining Dataset=Industrial, Base Pretraining=SimMIM2025.09 | 79 | 43.7 | |
| AGSSPBackbone=Swin-Base, Pretraining Dataset=Industrial, Base Pretraining=Classification2025.09 | 78.7 | 44 | |
| AGBPBackbone=Swin-Base, Pretraining Dataset=Industrial, Base Pretraining=Classification2025.09 | 78.2 | 39.9 | |
| ClassificationBackbone=Swin-Base, Pretraining Dataset=ImageNet2025.09 | 78 | 39 | |
| SimMIMBackbone=Swin-Base, Pretraining Dataset=ImageNet2025.09 | 77.5 | 40.7 | |
| SimMIMBackbone=Swin-Base, Pretraining Dataset=Industrial2025.09 | 77.5 | 40.1 | |
| AGSSPBackbone=ResNet50, Pretraining Dataset=Industrial, Base Pretraining=Classification2025.09 | 76.7 | 42.3 | |
| AGSSPBackbone=CSPDarknet, Pretraining Dataset=Industrial, Base Pretraining=Classification2025.09 | 76.4 | 42.7 | |
| AGBPBackbone=ResNet50, Pretraining Dataset=Industrial, Base Pretraining=Classification2025.09 | 74.9 | 40.6 | |
| ClassificationBackbone=Swin-Base, Pretraining Dataset=Industrial2025.09 | 74.9 | 37.1 | |
| ClassificationBackbone=CSPDarknet, Pretraining Dataset=ImageNet2025.09 | 74.3 | 38.7 | |
| AGBPBackbone=CSPDarknet, Pretraining Dataset=Industrial, Base Pretraining=Classification2025.09 | 74.2 | 39.7 | |
| AGSSPBackbone=WideResNet50, Pretraining Dataset=Industrial, Base Pretraining=Classification2025.09 | 74.2 | 40.4 | |
| ClassificationBackbone=ResNet50, Pretraining Dataset=ImageNet2025.09 | 73.8 | 38.9 | |
| ClassificationBackbone=CSPDarknet, Pretraining Dataset=Industrial2025.09 | 73.3 | 38.2 | |
| ClassificationBackbone=ResNet50, Pretraining Dataset=Industrial2025.09 | 73.2 | 39.8 | |
| DetectionBackbone=CSPDarknet, Pretraining Dataset=COCO2025.09 | 72.7 | 41.6 | |
| AGBPBackbone=WideResNet50, Pretraining Dataset=Industrial, Base Pretraining=Classification2025.09 | 71.7 | 36.8 | |
| AGBPBackbone=ResNet50, Pretraining Dataset=Industrial, Base Pretraining=MoCov32025.09 | 71 | 36.6 | |
| MoCov3Backbone=ResNet50, Pretraining Dataset=ImageNet2025.09 | 70.7 | 37.1 | |
| MoCov3Backbone=ResNet50, Pretraining Dataset=Industrial2025.09 | 70.6 | 37 | |
| ClassificationBackbone=WideResNet50, Pretraining Dataset=ImageNet2025.09 | 69.9 | 35.8 | |
| DINOv1Backbone=ResNet50, Pretraining Dataset=ImageNet2025.09 | 69.1 | 34.8 | |
| Training from scratchBackbone=CSPDarknet2025.09 | 68.8 | 35.5 | |
| Training from scratchBackbone=Swin-Base2025.09 | 55.7 | 27.7 | |
| Training from scratchBackbone=ResNet502025.09 | 37.2 | 16.3 |