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Vehicle Re Identification On Vehicleid Small

Métriques

Rank-1

Rank-5

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Rank-1	Rank-5	Paper Title	Repository
Recall@k Surrogate loss (ResNet-50)	95.7	97.9	Recall@k Surrogate Loss with Large Batches and Similarity Mixup
CLIP-ReID (without re-ranking)	85.5	97.2	CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text Labels
HPGN	-	-	Exploring Spatial Significance via Hybrid Pyramidal Graph Network for Vehicle Re-identification
PNP Loss	95.5	97.8	Rethinking the Optimization of Average Precision: Only Penalizing Negative Instances before Positive Ones is Enough
QD-DLF	-	-	Vehicle Re-identification Using Quadruple Directional Deep Learning Features	-
Recall@k Surrogate loss (ViT-B/16)	96.2	98.0	Recall@k Surrogate Loss with Large Batches and Similarity Mixup
MBR-4B (without RK)	88.3	-	Strength in Diversity: Multi-Branch Representation Learning for Vehicle Re-Identification
ANet	87.9	97.8	AttributeNet: Attribute Enhanced Vehicle Re-Identification	-
Smooth-AP	94.9	97.6	Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval
GiT	-	-	GiT: Graph Interactive Transformer for Vehicle Re-identification	-
vehiclenet	83.64	-	VehicleNet: Learning Robust Feature Representation for Vehicle Re-identification
RPTM	95.5	97.4	Relation Preserving Triplet Mining for Stabilising the Triplet Loss in Re-identification Systems
CAL	82.5	-	Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification

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