In　this　letter,　we　construct　MaRine　ShiP　(MRSP-13),　a　novel　dataset　containing　37,161　ship　target　images　belonging　to　13　classes　with　bounding　box　annotation,　and　among　them　there　are　3051　images　labeled　with　pixel-level　annotation.　This　dataset　equips　us　with　the　capability　to　conduct　baseline　experiments　on　maritime　target　classification,　detection　and　segmentation.　We　propose　a　cross-layer　multi-task　CNN　model　for　maritime　target　detection,　which　can　simultaneously　solve　ship　target　detection,　classification,　and　segmentation.　Experimental　results　have　demonstrated　the　efficiency　of　the　MRSP-13　dataset　to　be　used　for　maritime　target　analysis.　In　addition,　the　results　validate　the　fact　that　by　adopting　the　strategies　of　feature　sharing,　joint　learning,　and　cross-layer　connections,　the　proposed　model　achieves　superior　performance　with　less　annotations.　We　believe　that　our　MRSP-13　dataset　and　corresponding　baseline　experiments　will　lay　down　the　foundation　for　further　research　in　maritime　target　processing.