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Vehicle Re Identification On Vehicleid Large
评估指标
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评测结果
各个模型在此基准测试上的表现结果
| Paper Title | |||
|---|---|---|---|
| Recall@k Surrogate loss (ViT-B/16) | 94.7 | 97.1 | Recall@k Surrogate Loss with Large Batches and Similarity Mixup |
| Recall@k Surrogate loss (ResNet-50) | 93.8 | 96.6 | Recall@k Surrogate Loss with Large Batches and Similarity Mixup |
| PNP Loss | 93.2 | 96.6 | Rethinking the Optimization of Average Precision: Only Penalizing Negative Instances before Positive Ones is Enough |
| RPTM | 92.9 | 96.3 | Relation Preserving Triplet Mining for Stabilising the Triplet Loss in Re-identification Systems |
| Smooth-AP | 91.9 | 96.2 | Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval |
| ANet | 80.5 | 94.6 | AttributeNet: Attribute Enhanced Vehicle Re-Identification |
| vehiclenet | 79.46 | - | VehicleNet: Learning Robust Feature Representation for Vehicle Re-identification |
| MSINet (2.3M w/o RK) | 77.9 | 91.7 | MSINet: Twins Contrastive Search of Multi-Scale Interaction for Object ReID |
| CAL | 75.1 | - | Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification |
| QD-DLF | - | - | Vehicle Re-identification Using Quadruple Directional Deep Learning Features |
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