# EffShuffNet: An Efficient Neural Architecture for Adopting a Multi-Model
## 4 Experimental Results
### Results  
In the multi-model proposed for age and gender classification, ResNet50, DenseNet201, EfficientNetB0, MobileNetV2, ShuffleNetV2, the EffShuffNet, and the EffShuffDenseNet, were trained and evaluated from scratch. The datasets for age and gender classification were divided into the age dataset and gender dataset, respectively, and the learning process was executed. Additionally, transfer learning was performed on the models, ResNet50, DenseNet201, EfficientNetB0, and MobileNetV2, which were provided as TensorFlow2 formulas and had their weights pre-trained with ImageNet. The hyperparameters used for age and gender classification included the learning rate and Adam optimizer, with batch sizes of 16 or 64 and 100 epochs. The validation accuracy was monitored using early stopping, and if no change was observed within a set period, the learning process was terminated. Table 3 presents the results of a comparative experiment on image classification models for age classification. The EffShuffNet exhibits the smallest model size at 1.13 M and the lowest computational cost at 4.85 G compared to other scratch models, while attaining the highest classification accuracy of 96.37%, rivaling that of ResNet50. Although the EffShuffDenseNet showed an improvement in classification accuracy to 97.42% relative to the EffShuffNet, it was observed that the model size and computation cost slightly increased. However, both the EffShuffNet and EffShuffDenseNet were confirmed to have smaller model sizes compared to MobileNetV2 and ShuffleNetV2, which are well-known lightweight convolutional neural network models. Table 4 presents the results of a comparative experiment on image classification models for gender classification. Similar to the results of the age classification, the EffShuffNet has the smallest model size and lowest computational cost relative to other scratch models, while achieving the highest classification accuracy at 97.58%. The EffShuffDenseNet was observed to have improved classification accuracy compared to the EffShuffNet. Figure 7: Examples from three datasets. (**a**) The Audience dataset; (**b**) Butterfly & Moths dataset; (**c**) NA Birds dataset. The results of this experiment indicate that the multi-model of EffShuffNet surpasses ResNet50, which previously demonstrated the highest accuracy in scratch-based age and gender classification, with a classification accuracy of 96.975%. Furthermore, the multi \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Top-1 Accuracy** & **Params** & **Flops** \\ \hline ResNet50 & 98.75 & 24.59 M & 124.33 G \\ \hline ResNet50 & 97.18 & 24.59 M & 124.33 G \\ \hline DenseNet201 & 98.15 & 19.26 M & 138.39 G \\ \hline DenseNet201 & 96.73 & 19.26 M & 138.39 G \\ \hline EfficientNetB0 & 92.94 & 4.67 M & 13.01 G \\ \hline EfficientNetB0 & 95.09 & 4.67 M & 13.01 G \\ \hline MobileNetV2 & 98.43 & 2.88 M & 10.00 G \\ \hline MobileNetV2 & 96.92 & 2.88 M & 10.00 G \\ \hline ShuffleNetV2 & 96.53 & 1.95 M & 5.86 G \\ \hline EffShuffNet & 97.58 & 1.13 M & 4.85 G \\ \hline EffShuffDenseNet & 97.84 & 1.43 M & 7.44 G \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results of gender classification.