Deep learning methods can improve the speed and accuracy of AOI. However, due to the variable nature of industrial production scenarios, the models have to be updated continuously to ensure performance, which increases time and labor consumption. In order to improve the model iteration efficiency of deep learning methods in actual practice of AOI, an automatic training system for defect detection models of PCBA electronic components is developed in this paper. The system can automatically train the defect detection models required for four common types of electronic components(Chip, IC, SOT, and Plug-in). The automatic training process is divided into three parts: automatic data enhancement, parameter tuning, and deployment. Experimental results show that the models automatically trained by the system outperform the manual training models. Compared with manual training, the training time is shortened by 36% to 42%, and the overall accuracy is increased by 1.3% to 4.1%. At present, the proposed system has completed testing and the automatically trained model can meet the requirements of actual practice of AOI. It effectively improves the speed of model iteration, reduces labor costs and demonstrates its good application prospects.