Applying the Taguchi Method to the Optimized Design of YOLO Hyperparameters: Blood Cell Count and Detection

In recent years, deep learning has achieved excellent results in several applications across various fields. However, as the scale of deep learning models increases, the training time of the models also increases dramatically. Furthermore, hyperparameters have a significant influence on model training results and selecting the model’s hyperparameters efficiently is essential. In this study, the orthogonal array of the Taguchi method is used to find the best experimental combination of hyperparameters. This research uses three hyperparameters of the you only look once-version 3 (YOLOv3) detector and five hyperparameters of data augmentation as the control factor of the Taguchi method in addition to the traditional signal-to-noise ratio (S/N ratio) analysis method with larger-the-better (LB) characteristics. Experimental results show that the mean average precision of the blood cell count and detection dataset is 84.67%, which is better than the available results in literature. The method proposed herein can provide a fast and effective search strategy for optimizing hyperparameters in deep learning.