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Image Retrieval On Inaturalist

Metrics

R@1

Results

Performance results of various models on this benchmark

		Paper Title	Repository
Unicom+ViT-L@336px	88.9	Unicom: Universal and Compact Representation Learning for Image Retrieval
EfficientDML-VPTSP-G/512	84.5	Learning Semantic Proxies from Visual Prompts for Parameter-Efficient Fine-Tuning in Deep Metric Learning
Recall@k Surrogate loss (ViT-B/16)	83.0	Recall@k Surrogate Loss with Large Batches and Similarity Mixup
ROADMAP (DeiT-S)	73.6	Robust and Decomposable Average Precision for Image Retrieval
Recall@k Surrogate loss (ResNet-50)	71.8	Recall@k Surrogate Loss with Large Batches and Similarity Mixup
HAPPIER_F (ResNet-50)	71.0	Hierarchical Average Precision Training for Pertinent Image Retrieval
HAPPIER (ResNet-50)	70.7	Hierarchical Average Precision Training for Pertinent Image Retrieval
ROADMAP (ResNet-50)	69.1	Robust and Decomposable Average Precision for Image Retrieval
Smooth-AP	67.2	Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval
PNP Loss	66.6	Rethinking the Optimization of Average Precision: Only Penalizing Negative Instances before Positive Ones is Enough

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Image Retrieval On Inaturalist | SOTA | HyperAI