Image Retrieval On In Shop
Metrics
R@1
Results
Performance results of various models on this benchmark
Model Name | R@1 | Paper Title | Repository |
|---|---|---|---|
| Cross-Batch Memory | 91.3 | Cross-Batch Memory for Embedding Learning | |
| MS512 | 89.7 | Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning | |
| CGD (SG/GS) | 91.9 | Combination of Multiple Global Descriptors for Image Retrieval | |
| EPSHN512 | 87.8 | Improved Embeddings with Easy Positive Triplet Mining | |
| ProxyNCA++ | 90.9 | ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis | |
| NormSoftmax2048 (ResNet-50) | 89.4 | Classification is a Strong Baseline for Deep Metric Learning | |
| ABE-8 | 87.3 | Attention-based Ensemble for Deep Metric Learning | - |
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