EfficientNet-V2
This study trained EfficientNet-V2 to classify images. EfficientNet is a convolutional neural network that scales down the number of layers while scaling down the model (Tan and Le 2019). EfficientNet-V2 is an improved version of EfficientNet with increased training speed and parameter efficiency relative to the previous EfficientNet (Tan and Le 2021). The EfficientNet-V2 model employs neural architecture search (NAS) to optimize model accuracy, size, and training speed. In this study, the EfficientNetV2-B0 model was used as the network, and fine-tuning was performed using a model that had been pre-trained with the Imagenet21k data set. The number of epochs was set to 50, and the batch size was set to 32 for training. Adam was used as the optimization algorithm (optimizer), and dropout was set to 0.3. We employed early stopping to prevent overfitting. Automatic termination was performed when validation loss did not improve more than 0.001 for five consecutive epochs, and we used the weights when validation loss was the best. These analyses were performed using the NVIDIA DGX Station A100. Finally, overall accuracy was used for evaluation.