A system for creating a learned model for component image recognition, comprising a computer , wherein the computer is connectable to a component mounting line comprising multiple component mounters and to an inspection device installed on the unloading side of the component mounting line to inspect the good or bad quality of the mounted state of each mounted component, the learned model being usable when performing image recognition by imaging an imaging target with a camera, the imaging target being a component that is picked up by a suction nozzle of a component mounter, wherein the computer is configured to: acquire a reference-learned model usable for image recognition of a reference component; collect sample component images of normal pickup and abnormal pickup or component images of presence and absence of the component picked up by the suction nozzle for each type of a component having a predetermined similarity with the reference component; acquire information on an inspection result from the inspection device ; discard , from the collected sample component images of normal pickup or of presence of the component, sample component images obtained by imaging components determined by the inspection device as a mounting failure; calculate a mounting failure occurrence rate for each type of the component based on the information on the inspection result acquired from the inspection device; and for each type of the component, when the mounting failure occurrence rate exceeds a predetermined determination threshold , create a component-by-component learned model usable for image recognition of the component by adding a sample component image for the type of the component, as teacher data of the reference-learned model, and re-learning the added sample component image, and transfer the created component-by-component learned model to the component mounter for using the component-by-component learned model. acquire a reference-learned model usable for image recognition of a reference component; collect sample component images of normal pickup and abnormal pickup or component images of presence and absence of the component picked up by the suction nozzle for each type of a component having a predetermined similarity with the reference component; acquire information on an inspection result from the inspection device ; discard , from the collected sample component images of normal pickup or of presence of the component, sample component images obtained by imaging components determined by the inspection device as a mounting failure; calculate a mounting failure occurrence rate for each type of the component based on the information on the inspection result acquired from the inspection device; and for each type of the component, when the mounting failure occurrence rate exceeds a predetermined determination threshold , create a component-by-component learned model usable for image recognition of the component by adding a sample component image for the type of the component, as teacher data of the reference-learned model, and re-learning the added sample component image, and transfer the created component-by-component learned model to the component mounter for using the component-by-component learned model. <EOS>