# EffShuffNet: An Efficient Neural Architecture for Adopting a Multi-Model
## 1 Introduction  
In recent years, deep learning technology has seen significant advancements, leading to an increase in research on deep learning-based image classification models. The ImageNet Dataset [1], a benchmark in the field of image classification, has been widely used to evaluate these models. The evolution of convolutional neural networks (CNNs) can be traced back to AlexNet [2], and since then, numerous CNN models have been developed and have demonstrated impressive performance in image classification, including VGG [3], GoogleNet [4], ResNet [5], and DenseNet [6]. The breakthrough of Transformer [7] in the field of natural language processing (NLP) in 2017 has also influenced the development of image classification models. Attention mechanisms have been applied in models such as Residual Attention Network [8], Squeeze-and-Excitation [9], Non-local Network [10], BottleNeck Attention Network [11] and Convolutional Block Attention Network [12]. In addition, recent studies have directly applied Transformer in image classification tasks, with models such as Vision Transformer [13] and Data efficient Image Transformer [14] showing promising results. Despite the steady improvement in classification accuracy, image classification research faces a trade-off between accuracy and model size, as larger models tend to consume more computational resources. The image classification studies conducted thus far have broadened the scope of image classification tasks. Image classification tasks can be divided into three main categories:binary class classification, in which only one correct label exists; multi-class classification, in which there is only one correct label; and multi-label classification, in which multiple correct labels exist. Multi-label classification requires a different learning approach compared to general classification due to the presence of multiple correct labels. This classification problem is considered a challenge in the field of data science because prediction difficulty increases as the number of labels and classes increases. A possible solution to this problem is to utilize multiple deep-learning models, each capable of classifying the correct label from an input image. However, using a multi-model approach in deep learning has limitations, such as increased complexity as the number of multi-label classification types increases, and an increase in computation and calculation due to the use of predictions output by multiple models. Generally, state-of-the-art image classification models have large model sizes and high amounts of computation, which can increase even further when applying an ensemble method. In this work, we propose the application of the ensemble method to the classification of age and gender in portrait images using the EffShuff and EffShuff-Dense networks. These networks are lightweight and demonstrate good classification performance. The EffShuff network operates by dividing the input feature map into two parts, keeping half of the feature map unchanged, and applying lightweight convolution to the other half. The results from each convolution layer are then combined while preserving the features from the previous feature map. The EffShuff-Dense block, on the other hand, accumulates the features from previous feature maps in a highly efficient manner. By employing the ensemble method on either the EffShuff or EffShuff-Dense, we aim to demonstrate the possibility of achieving better classification results in the multi-label classification task of age and gender in portraits, compared to existing single convolutional neural network-based models. Furthermore, the performance of our method will be verified on a fine-grained image classification dataset with over 100 labels and a balanced distribution of classes. The present research introduces a novel architecture, referred to as EffShuffNet, which is designed to address the challenge of efficient and effective deep learning model development for practical deployment in edge devices.