Person Re-identification on Market1501 MS → Mar (test)
92.1mAPCION
Evaluation results
| Method | Links | ||
|---|---|---|---|
| CIONBackbone=ResNet50-IBN, Pre-training dataset=Large-scale Person Images2024.09 | 92.1 | 96.5 | |
| CIONBackbone=ResNet-50, Pre-training dataset=Large-scale Person Images2024.09 | 90.6 | 96.1 | |
| CIONBackbone=ViT-S, Pre-training dataset=Large-scale Person Images2024.09 | 90.4 | 95.6 | |
| PASSBackbone=ViT-S, Pre-training dataset=Large-scale Person Images2024.09 | 90.2 | 95.8 | |
| TranSSLBackbone=ViT-S, Pre-training dataset=Large-scale Person Images, add CFS=true2024.09 | 89.9 | 95.5 | |
| TranSSLBackbone=ViT-S, Pre-training dataset=Large-scale Person Images2024.09 | 89.6 | 95.6 | |
| LUPBackbone=ResNet50-IBN, Pre-training dataset=Large-scale Person Images2024.09 | 86.9 | 94.6 | |
| LUPBackbone=ResNet-50, Pre-training dataset=Large-scale Person Images2024.09 | 85.1 | 94.4 | |
| C-ContrastBackbone=ResNet-50, Pre-training dataset=ImageNet1K-1.3M2024.09 | 82.4 | 92.5 | |
| SpCLBackbone=ResNet-50, Pre-training dataset=ImageNet1K-1.3M2024.09 | 77.5 | 89.7 | |
| MMTBackbone=ResNet-50, Pre-training dataset=ImageNet1K-1.3M2024.09 | 75.6 | 83.9 | |
| DG-Net++Backbone=ResNet-50, Pre-training dataset=ImageNet1K-1.3M2024.09 | 64.6 | 83.1 |