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Image Retrieval On Sop

Metrics

R@1

Results

Performance results of various models on this benchmark

Model Name	R@1	Paper Title	Repository
Cross-Batch Memory	80.6	Cross-Batch Memory for Embedding Learning	-
Unicom+ViT-L@336px	91.2	Unicom: Universal and Compact Representation Learning for Image Retrieval	-
ROADMAP (DeiT-B)	86.0	Robust and Decomposable Average Precision for Image Retrieval	-
MS512	78.2	Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning	-
ABE-8	76.3	Attention-based Ensemble for Deep Metric Learning	-
ROADMAP (ResNet-50)	83.1	Robust and Decomposable Average Precision for Image Retrieval	-
EPSHN512	78.3	Improved Embeddings with Easy Positive Triplet Mining	-
Smooth-AP	80.1	Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval	-
PNP Loss	81.1	Rethinking the Optimization of Average Precision: Only Penalizing Negative Instances before Positive Ones is Enough	-
ProxyNCA++	81.4	ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis	-
HDC	69.5	Hard-Aware Deeply Cascaded Embedding	-
CGD (SG/GS)	84.2	Combination of Multiple Global Descriptors for Image Retrieval	-
NormSoftmax2048 (ResNet-50)	79.5	Classification is a Strong Baseline for Deep Metric Learning	-
A-BIER	74.2	Deep Metric Learning with BIER: Boosting Independent Embeddings Robustly	-

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Image Retrieval On Sop | SOTA | HyperAI