Method for Hyperparameter Tuning of EfficientNetV2-based Image Classification by Deliberately Modifying Optuna Tuned Result

Citations Over Time

Abstract

Method for hyperparameter tuning of EfficientNetV2-based image classification by deliberately modifying Optuna tuned result is proposed. An example of the proposed method for textile pattern quality evaluation (good or bad textile pattern fluctuation quality classification) is shown. When using the hyperparameters obtained by Optuna without changing them, the accuracy certainly improved. Furthermore, as a result of learning by changing the hyperparameter with the highest degree of importance, the accuracy changed, so it could be said that the degree of importance was certainly high. However, the accuracy also changes when learning is performed by changing the least important hyperparameter, and sometimes the accuracy is improved compared to when learning is performed using the optimal hyperparameter. From this result, it is found that the optimal hyperparameters obtained with Optuna are not necessarily optimal.

Related Papers

• → Deep Neural Network with Hyperparameter Tuning for Detection of Heart Disease(2021)23 cited
• → Hyperparameter Tuning on Classification Algorithm with Grid Search(2022)16 cited
• → Hyp-RL : Hyperparameter Optimization by Reinforcement Learning(2019)34 cited
• → Novel Suboptimal approaches for Hyperparameter Tuning of Deep Neural Network [under the shelf of Optical Communication](2019)3 cited
• → Hyperparameter Tuning of Dense Neural Network for ECG Signal Classification(2022)