Deep　metric　learning　(DML)　aims　to　learn　a　consistent　distance　embedding　where　an　anchor　is　closer　within　the　same　category　than　others.　It　underpins　a　variety　of　essential　and　significant　tasks　in　the　development　of　smart　city　including　face　recognition,　landmark　retrieval,　pedestrian　detection,　person/vehicle　re-identification,　and　so　on.　Traditional　pair-based　DML　methods　try　to　make　full　use　of　the　data-to-data　relations　within　a　(mini-)batch,　but　they　cannot　grasp　the　data　distribution　information　due　to　the　batch　size　limitation.　On　the　other　hand,　proxy-based　DML　schemes　use　different　proxies　to　approximate　the　data　distribution.　However,　the　proxies　are　too　sample　to　represent　the　intra-category　variance.　In　this　paper,　we　propose　a　simple　but　effective　method,　named　soft-instance-label　proxy,　for　embedding　learning.　It　can　capture　the　globe　data　distribution　information　while　depicting　the　detailed　intra-class　data　structure.　The　state-of-the-art　empirical　results　on　three　public　image　retrieval　benchmarks　and　two　backbone　networks　demonstrate　the　superiority　of　our　proposed　method.　Our　Soft-instance-label　proxy　method　can　have　a　Recall@1　improvement　of　2.4%　with　Googlenet,　largely　surpassing　the　current　state-of-art-methods　while　demonstrating　great　potential　in　the　development　of　smart　city.　©　2021