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Vehicle Re Identification On Vehicleid Large

Metrics

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Results

Performance results of various models on this benchmark

			Paper Title	Repository
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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Vehicle Re Identification On Vehicleid Large | SOTA | HyperAI