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SOTA
Metric Learning
Metric Learning On In Shop 1
Metric Learning On In Shop 1
Metrics
R@1
Results
Performance results of various models on this benchmark
Columns
Model Name
R@1
Paper Title
Unicom+ViT-L@336px
96.7
Unicom: Universal and Compact Representation Learning for Image Retrieval
STIR
95
STIR: Siamese Transformer for Image Retrieval Postprocessing
MGA
94.3
Fashion Image Retrieval with Multi-Granular Alignment
Hyp-ViT
92.5
Hyperbolic Vision Transformers: Combining Improvements in Metric Learning
Hyp-DINO
92.4
Hyperbolic Vision Transformers: Combining Improvements in Metric Learning
CCL (ResNet-50)
92.31
Center Contrastive Loss for Metric Learning
Gradient Surgery
92.21
Dissecting the impact of different loss functions with gradient surgery
ResNet-50 + Metrix
92.2
It Takes Two to Tango: Mixup for Deep Metric Learning
EfficientDML-VPTSP-G/512
92.1
Learning Semantic Proxies from Visual Prompts for Parameter-Efficient Fine-Tuning in Deep Metric Learning
ViT-Triplet
92.1
STIR: Siamese Transformer for Image Retrieval Postprocessing
NED
91.3
Calibrated neighborhood aware confidence measure for deep metric learning
ResNet-50 + ProxyNCA++
90.9
ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis
ResNet-50 + Cross-Entropy
90.6
A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses
SCT(512)
90
Hard negative examples are hard, but useful
EPSHN(512)
87.8
Improved Embeddings with Easy Positive Triplet Mining
0 of 15 row(s) selected.
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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
Metric Learning
Metric Learning On In Shop 1
Metric Learning On In Shop 1
Metrics
R@1
Results
Performance results of various models on this benchmark
Columns
Model Name
R@1
Paper Title
Unicom+ViT-L@336px
96.7
Unicom: Universal and Compact Representation Learning for Image Retrieval
STIR
95
STIR: Siamese Transformer for Image Retrieval Postprocessing
MGA
94.3
Fashion Image Retrieval with Multi-Granular Alignment
Hyp-ViT
92.5
Hyperbolic Vision Transformers: Combining Improvements in Metric Learning
Hyp-DINO
92.4
Hyperbolic Vision Transformers: Combining Improvements in Metric Learning
CCL (ResNet-50)
92.31
Center Contrastive Loss for Metric Learning
Gradient Surgery
92.21
Dissecting the impact of different loss functions with gradient surgery
ResNet-50 + Metrix
92.2
It Takes Two to Tango: Mixup for Deep Metric Learning
EfficientDML-VPTSP-G/512
92.1
Learning Semantic Proxies from Visual Prompts for Parameter-Efficient Fine-Tuning in Deep Metric Learning
ViT-Triplet
92.1
STIR: Siamese Transformer for Image Retrieval Postprocessing
NED
91.3
Calibrated neighborhood aware confidence measure for deep metric learning
ResNet-50 + ProxyNCA++
90.9
ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis
ResNet-50 + Cross-Entropy
90.6
A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses
SCT(512)
90
Hard negative examples are hard, but useful
EPSHN(512)
87.8
Improved Embeddings with Easy Positive Triplet Mining
0 of 15 row(s) selected.
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Next
Metric Learning On In Shop 1 | SOTA | HyperAI