Image Retrieval on Revisited Paris (RPar) Hard 1.0 (test)
0.751mAPDELG + RRT
Evaluation results
| Method | Links | ||
|---|---|---|---|
| DELG + RRTTraining set=GLDv2-clean, Net=ResNet-50, Number of local descriptors=500, Feature Type=Global features + Re-ranking2021.03 | 0.751 | 0.494 | |
| DELG + GVTraining set=GLDv2-clean, Net=ResNet-50, Number of local descriptors=1000, Feature Type=Global features + Re-ranking2021.03 | 0.71 | 0.457 | |
| DELG + RRTTraining set=GLDv1&v2-clean, Net=ResNet-50, Number of local descriptors=500, Feature Type=Global features + Re-ranking2021.03 | 0.686 | 0.375 | |
| DELG + GVTraining set=GLDv1, Net=ResNet-50, Number of local descriptors=1000, Feature Type=Global features + Re-ranking2021.03 | 0.649 | 0.348 | |
| DELGTraining set=GLDv1, Net=ResNet-50, Number of local descriptors=0, Feature Type=Global features2021.03 | 0.634 | 0.341 | |
| HOW-ASMKTraining set=SfM-120k, Net=ResNet-50, Number of local descriptors=2000, Feature Type=Local feature aggregation2021.03 | 0.624 | 0.337 | |
| GeM-APTraining set=SfM-120k, Net=ResNet-101, Number of local descriptors=0, Feature Type=Global features2021.03 | 0.605 | 0.251 | |
| HOW-ASMKTraining set=SfM-120k, Net=ResNet-50, Number of local descriptors=1000, Feature Type=Local feature aggregation2021.03 | 0.601 | 0.307 | |
| R-MACTraining set=Landmarks, Net=ResNet-101, Number of local descriptors=0, Feature Type=Global features2021.03 | 0.594 | 0.28 | |
| GeMTraining set=SfM-120k, Net=ResNet-101, Number of local descriptors=0, Feature Type=Global features2021.03 | 0.563 | 0.247 | |
| GeM + DSMTraining set=SfM-120k, Net=ResNet-101, Number of local descriptors=1000, Feature Type=Global features + Re-ranking2021.03 | 0.562 | 0.25 | |
| DELF-ASMKTraining set=Landmarks, Net=ResNet-50, Number of local descriptors=1000, Feature Type=Local feature aggregation2021.03 | 0.554 | 0.264 |