Person Re-identification on Partial-REID
92.9Rank-1CityGuard
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| CityGuard2026.02 | 92.9 | — | — | 89.8 | |
| THCB-NetPre-training=LUPerson2026.02 | 91.3 | — | — | 88 | |
| KPR_SOLPrompt usage=true, Backbone=SOLIDER2024.07 | 90.7 | 94 | — | — | |
| KPR_SOLPrompt usage=false, Backbone=SOLIDER2024.07 | 90.3 | 93.7 | — | — | |
| FRT2026.02 | 88.2 | — | — | — | |
| PAT2024.07 | 88 | 92.3 | — | — | |
| PAT2026.02 | 88 | — | — | — | |
| THCB-NetPre-training=ImageNet2026.02 | 87.4 | — | — | 84.2 | |
| RGANet2026.02 | 87.2 | — | — | — | |
| KPR_INPrompt usage=true, Backbone=IN2024.07 | 86 | 90 | — | — | |
| PRE-Net2026.02 | 86 | — | — | — | |
| VGTri2024.07 | 85.7 | 93.7 | — | — | |
| DOAN2026.02 | 85.7 | — | — | 89 | |
| HOReIDTraining dataset=Market-15012020.03 | 85.3 | 91 | — | — | |
| HOREID2024.07 | 85.3 | 91 | — | — | |
| HOReIDBackbone=ResNet-502021.07 | 85.3 | 91 | — | — | |
| HOReID2026.02 | 85.3 | — | — | — | |
| PGFL-KDBackbone=ResNet-502021.07 | 85.1 | 90.8 | — | — | |
| FED2024.07 | 84.6 | — | — | — | |
| FED2026.02 | 84.6 | — | — | 82.3 | |
| Swin-BPre-training=LUPerson2026.02 | 84.1 | — | — | 81 | |
| TCSDOTraining dataset=Market-15012020.03 | 82.7 | — | — | — | |
| TCSDOBackbone=ResNet-502021.07 | 82.7 | — | — | — | |
| Swin-BPre-training=ImageNet2026.02 | 82 | — | — | 79.1 | |
| KPR_INPrompt usage=false, Backbone=IN2024.07 | 81.7 | 86 | — | — | |
| FPRTraining dataset=Market-15012020.03 | 81 | — | — | — | |
| FPRBackbone=ResNet-502021.07 | 81 | — | — | — | |
| AFPBTraining dataset=Market-15012020.03 | 78.5 | — | — | — | |
| AFPBBackbone=ResNet-502021.07 | 78.5 | — | — | — | |
| PVPM2024.07 | 78.3 | — | — | — | |
| DSR2026.02 | 73.7 | — | — | 68.1 | |
| TransReID2026.02 | 71.3 | — | — | 68.6 | |
| AGW2020.01 | 69.7 | 80 | 56.7 | — | |
| PGFATraining dataset=Market-15012020.03 | 68 | 80 | — | — | |
| PGFABackbone=ResNet-502021.07 | 68 | 80 | — | — | |
| VPMCrop strategy=Bilateral2019.04 | 67.7 | 81.9 | — | — | |
| VPMTraining dataset=Market-15012020.03 | 67.7 | 81.9 | — | — | |
| VPM2020.01 | 67.7 | 81.9 | — | — | |
| VPMBackbone=ResNet-502021.07 | 67.7 | 81.9 | — | — | |
| VPMCrop strategy=Top2019.04 | 64.3 | 83.6 | — | — | |
| BagTricks2020.01 | 62 | 74 | 45.4 | — | |
| DAAF-BoTTraining Dataset=Market-1501, Base Model=BoT2020.03 | 60.7 | 68.7 | — | — | |
| BoTTraining Dataset=Market-15012020.03 | 57.3 | 65.3 | — | — | |
| SFR2019.04 | 56.9 | 78.5 | — | — | |
| SFRTraining dataset=Market-15012020.03 | 56.9 | 78.5 | — | — | |
| SFR2020.01 | 56.9 | 78.5 | — | — | |
| SFRBackbone=ResNet-502021.07 | 56.9 | 78.5 | — | — | |
| PCB2026.02 | 56.3 | — | — | 54.7 | |
| VPMCrop strategy=Bottom2019.04 | 53.2 | 73.2 | — | — | |
| DSR2019.04 | 50.7 | 70 | — | — | |
| DSRTraining dataset=Market-15012020.03 | 50.7 | 70 | — | — | |
| DSR2020.01 | 50.7 | 70 | — | — | |
| DSRBackbone=ResNet-502021.07 | 50.7 | 70 | — | — | |
| DAAF-TriNetTraining Dataset=Market-1501, Base Model=TriNet2020.03 | 47 | 53.33 | — | — | |
| TriNetTraining Dataset=Market-15012020.03 | 43 | 50 | — | — | |
| PCBTraining Dataset=Market-15012020.03 | 42.7 | 50.3 | — | — | |
| AMC+SWM2019.04 | 37.3 | 46 | — | — | |
| MTRC2019.04 | 23.7 | 27.3 | — | — |