Deep　supervised　hashing　takes　prominent　advantages　of　low　storage　cost,　high　computational　efficiency　and　good　retrieval　performance,　which　draws　attention　in　the　field　of　large-scale　image　retrieval.　However,　similarity-preserving,　quantization　errors　and　imbalanced　data　are　still　great　challenges　in　deep　supervised　hashing.　This　paper　proposes　a　pairwise　similarity-preserving　deep　hashing　scheme　to　handle　the　aforementioned　problems　in　a　unified　framework,　termed　as　Cosine　Metric　Supervised　Deep　Hashing　with　Balanced　Similarity　(BCMDH).　BCMDH　integrates　contrastive　cosine　similarity　and　Cosine　distance　entropy　quantization　to　preserve　the　original　semantic　distribution　and　reduce　the　quantization　errors　simultaneously.　Furthermore,　a　weighted　similarity　measure　with　cosine　metric　entropy　is　designed　to　reduce　the　impact　of　imbalanced　data,　which　adaptively　assigns　weights　according　to　sample　attributes　(pos/neg　and　easy/hard)　in　the　embedding　process　of　similarity-preserving.　The　experimental　results　on　four　widely-used　datasets　demonstrate　that　the　proposed　method　is　capable　of　generating　hash　codes　of　high　quality　and　improve　large-scale　image　retrieval　performance.　CO　2021　Published　by　Elsevier　B.V.