Pedestrian　attribute　recognition　is　a　technology　that　uses　computer　vision　technology　to　determine　some　observable　external　characteristics　of　pedestrians　in　an　image　or　video　sequence,　such　as　gender,　age,　clothing,　and　equipment,　etc.　It　is　widely　used　in　many　fields,　such　as　intelligent　security,　clustering　based　passenger　flow　statistics　in　public　places,　human-computer　interaction,　album　clustering　and　so　on,　It　has　high　research　value　in　the　field　of　computer　vision.　Due　to　the　complexity　and　diversity　of　pedestrian　attributes,　the　convergence　rate　of　each　attribute　is　not　consistent　in　the　process　of　model　training.　Therefore,　the　manual　training　with　fixed　weight　set　by　experience　cannot　adapt　to　the　diversification　of　attributes,　resulting　in　poor　performance　of　the　model.　In　recent　years,　in　some　researches,　a　multi-branch　model　is　usually　needed　to　solve　this　problem　through　multiple　trainings.　This　method　has　a　lot　of　parameter　redundancy,　which　affects　the　efficiency　of　the　model.　The　aim　of　this　paper　is　to　improve　the　accuracy　by　using　a　dynamic　adaptive　weight　loss　model　without　multiple　branches　in　a　single　training.　The　depthwise　separable　convolutions　is　used　to　further　improve　the　model　recognition　efficiency.　After　training　in　the　PETA　dataset　and　adding　dynamic　adaptive　weight　loss,　the　accuracy　of　the　model　is　similar　to　that　of　some　multi-branch　models,　and　the　recognition　efficiency　is　greatly　improved.　©　2020　IEEE.