Deep　supervised　hashing　hash　has　been　widely　utilized　in　large-scale　image　retrieval　due　to　its　lightweight　storage　and　fast　search　speed.　The　distribution　of　features　from　the　existing　hashing　methods　has　been　inevitably　distorted　from　the　original　feature　distribution,　which　resulted　in　the　performance　decline　on　image　retrieval.　With　the　constraints　of　the　Euclidian　metric,　the　distortion　can　be　mitigated　to　certain　extent.　However,　the　Euclidian　distance　is　sensitive　to　the　norm　of　the　features.　On　the　point,　we　propose　a　novel　deep　hashing　method,　called　cosine　metric　supervised　deep　hashing　(CMDH),　to　perform　hash　learning　by　incorporating　cosine　metric　and　category　loss.　CMDH　uses　the　cosine　metric　to　effectively　mitigate　the　impact　caused　by　the　diverse　sample　vector　norms　on　retrieval　performance.　In　addition,　joint　deep　hash　learning　constructs　binary　coding　over　cosine　loss　and　category　loss,　which　further　alleviates　distortion　in　binary　feature　learning　for　efficient　retrieval.　©　2022,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.