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Vehicle Re Identification On Vehicleid Small

Metrics

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Results

Performance results of various models on this benchmark

Model Name
Rank-1
Rank-5
Paper TitleRepository
Recall@k Surrogate loss (ResNet-50)95.797.9Recall@k Surrogate Loss with Large Batches and Similarity Mixup-
CLIP-ReID (without re-ranking)85.597.2CLIP-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 Loss95.597.8Rethinking 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)96.298.0Recall@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-
ANet87.997.8AttributeNet: Attribute Enhanced Vehicle Re-Identification-
Smooth-AP94.997.6Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval-
GiT--GiT: Graph Interactive Transformer for Vehicle Re-identification-
vehiclenet83.64-VehicleNet: Learning Robust Feature Representation for Vehicle Re-identification
RPTM95.597.4Relation Preserving Triplet Mining for Stabilising the Triplet Loss in Re-identification Systems-
CAL82.5-Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification-
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Vehicle Re Identification On Vehicleid Small | SOTA | HyperAI