HyperAI
HyperAI
Home
Console
Docs
News
Papers
Tutorials
Datasets
Wiki
SOTA
LLM Models
GPU Leaderboard
Events
Search
About
Terms of Service
Privacy Policy
English
HyperAI
HyperAI
Toggle Sidebar
Search the site…
⌘
K
Command Palette
Search for a command to run...
Console
Home
SOTA
Image Retrieval
Image Retrieval On Sop
Image Retrieval On Sop
Metrics
R@1
Results
Performance results of various models on this benchmark
Columns
Model Name
R@1
Paper Title
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
CGD (SG/GS)
84.2
Combination of Multiple Global Descriptors for Image Retrieval
ROADMAP (ResNet-50)
83.1
Robust and Decomposable Average Precision for Image Retrieval
ProxyNCA++
81.4
ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis
PNP Loss
81.1
Rethinking the Optimization of Average Precision: Only Penalizing Negative Instances before Positive Ones is Enough
Cross-Batch Memory
80.6
Cross-Batch Memory for Embedding Learning
Smooth-AP
80.1
Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval
NormSoftmax2048 (ResNet-50)
79.5
Classification is a Strong Baseline for Deep Metric Learning
EPSHN512
78.3
Improved Embeddings with Easy Positive Triplet Mining
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
A-BIER
74.2
Deep Metric Learning with BIER: Boosting Independent Embeddings Robustly
HDC
69.5
Hard-Aware Deeply Cascaded Embedding
0 of 14 row(s) selected.
Previous
Next
HyperAI
HyperAI
Home
Console
Docs
News
Papers
Tutorials
Datasets
Wiki
SOTA
LLM Models
GPU Leaderboard
Events
Search
About
Terms of Service
Privacy Policy
English
HyperAI
HyperAI
Toggle Sidebar
Search the site…
⌘
K
Command Palette
Search for a command to run...
Console
Home
SOTA
Image Retrieval
Image Retrieval On Sop
Image Retrieval On Sop
Metrics
R@1
Results
Performance results of various models on this benchmark
Columns
Model Name
R@1
Paper Title
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
CGD (SG/GS)
84.2
Combination of Multiple Global Descriptors for Image Retrieval
ROADMAP (ResNet-50)
83.1
Robust and Decomposable Average Precision for Image Retrieval
ProxyNCA++
81.4
ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis
PNP Loss
81.1
Rethinking the Optimization of Average Precision: Only Penalizing Negative Instances before Positive Ones is Enough
Cross-Batch Memory
80.6
Cross-Batch Memory for Embedding Learning
Smooth-AP
80.1
Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval
NormSoftmax2048 (ResNet-50)
79.5
Classification is a Strong Baseline for Deep Metric Learning
EPSHN512
78.3
Improved Embeddings with Easy Positive Triplet Mining
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
A-BIER
74.2
Deep Metric Learning with BIER: Boosting Independent Embeddings Robustly
HDC
69.5
Hard-Aware Deeply Cascaded Embedding
0 of 14 row(s) selected.
Previous
Next