Due　to　the　high　joint　flexibility　and　deformation　degree　of　hands,　hand　pose　estimation　is　more　challenging　in　the　detection　task.　In　order　to　ensure　the　accuracy　of　prediction,　two-stage　algorithms　are　proposed　recently,　which　requires　a　huge　and　redundant　model　structure　and　is　difficult　to　implement　end-to-end　deployment.　In　this　paper,　we　propose　a　novel　dynamic　single-stage　CNN　(RetinaHand)　for　end-to-end　2D　handpose　estimation　of　RGB　images　based　on　RetinaNet.　RetinaHand　firstly　extracts　image　features　through　the　backbone　with　dynamic　convolutional　layers.　In　the　neck　module,　we　propose　Context　Path　Aggregation　Network　(CPANet)　that　fuse　different　scale　features　and　expands　context　information　to　improve　performance.　In　addition,　we　use　the　idea　of　multi-task　learning　to　add　a　keypoints　heatmap　regression　branch　on　the　basis　of　the　existing　classification　and　bounding　box　regression　branch,　and　use　multi-task　loss　training　model.　Experimental　results　on　the　Eric.Lee　and　Panoptic　datasets　consistently　show　that　our　proposed　RetinaHand　has　comparable　performance　to　existing　hand　pose　estimation　methods　at　more　efficient　inference　rates.