# EffShuffNet: An Efficient Neural Architecture for Adopting a Multi-Model
## 4 Experimental Results
### Dataset  
The evaluation of the performance of the EffShuffNet and EffShuffDenseNet was conducted using several datasets in the proposed multi-model for age and gender classification. The datasets used were the Audience dataset [30], the Butterfly & Moths dataset [31], and the NA Birds dataset [32].  
* The Audience dataset [30] is a well-known dataset for age and gender classification that was published in 2014. The images used in this dataset were captured under various real-world conditions, including different appearances, poses, lighting conditions, and image quality. The dataset consisted of 15,163 images for age classification, which was  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layers** & **Stage** & **Output Size** & **Output Filters** & **Growth Rate (K)** \\ \hline Input & & 224 \(\times\) 224 & 3 & \\ \hline Convolution & \multirow{2}{*}{General Stem} & 112 \(\times\) 112 & 64 & \\ \cline{3-5} MaxPool & & 56 \(\times\) 56 & 64 & \\ \hline EffShuff transition & & 28 \(\times\) 28 & 92 & \\ \hline EffShuff-Dense block & \multirow{2}{*}{Stage 1} & 28 \(\times\) 28 & 108 & 16 \\ \cline{3-5} EffShuff-Dense block & & 28 \(\times\) 28 & 124 & 16 \\ \cline{3-5} EffShuff-Dense block & & 28 \(\times\) 28 & 140 & 16 \\ \hline EffShuff transition & & 14 \(\times\) 14 & 176 & \\ \cline{3-5} EffShuff-Dense block & & 14 \(\times\) 14 & 192 & 16 \\ \cline{3-5} EffShuff-Dense block & & 14 \(\times\) 14 & 208 & 16 \\ \cline{3-5} EffShuff-Dense block & & 14 \(\times\) 14 & 224 & 16 \\ \cline{3-5} EffShuff-Dense block & & 14 \(\times\) 14 & 224 & 16 \\ \cline{3-5} EffShuff-Dense block & & 14 \(\times\) 14 & 272 & 16 \\ \cline{3-5} EffShuff-Dense block & & 14 \(\times\) 14 & 288 & 16 \\ \cline{3-5} EffShuff transition & & 7 \(\times\) 7 & 440 & \\ \cline{3-5} EffShuff-Dense block & & 7 \(\times\) 7 & 456 & 16 \\ \cline{3-5} EffShuff-Dense block & & 7 \(\times\) 7 & 472 & 16 \\ \cline{3-5} EffShuff-Dense block & & 7 \(\times\) 7 & 488 & 16 \\ \hline GAP & \multirow{2}{*}{Classifier} & 1 \(\times\) 1 & 488 & \\ \cline{3-5} Softmax & & 1 \(\times\) 1 & No of classes & \\ \hline \hline \end{tabular}
\end{table}
Table 2: EffShuff-Dense-Block model architecture.  
divided into 10 classes, and 15,300 images for gender classification, which was divided into four classes.
* The Butterfly & Moths dataset [31], as depicted in Figure 7b, is a dataset for butterfly and moth classification. This dataset was published on Kaggle in 2019 and consisted of 12,639 images with 100 classes and an input size of 224 \(\times\) 224 \(\times\) 3.
* The NA Birds dataset [32], illustrated in Figure 7c, is a representative dataset for fine-grain image classification. This dataset comprises photos of 400 species of birds commonly found in North America and contains 48,000 annotations. The dataset consists of 48,562 images and 555 classes. All datasets were resized to the input size of 224 \(\times\) 224 \(\times\) 3. The NA Birds dataset, in particular, was zero-padded to create a square image, and then resized to the desired size. For each dataset, 90% of the images were used for training and 10% for testing. The same ratio was used for all datasets in the experiment.