HybridBranchNet: A novel structure for branch hybrid convolutional neural networks architecture

ConvNet deep neural networks are developed with a consistent structure. The availability of abundant resources helps these structures to be scaled and redesigned in different sizes so that they can be optimized for different applications. By increasing one or more dimensions of the network, such as depth, resolution and width, the number of trainable network parameters will increase and, as a result, the accuracy and performance It should be noted that the backtracking of the convolutional neural network will improve. However, but increasing the number of network parameters increases the complexity of the network, which is not desirable. Therefore, adjusting the structure of the network, increasing the speed, and reducing the number of network parameters along with ensuring accuracy optimization will be important. This study aims to examine a branch network structure systematically, which can lead to better performance. In this study, in order to increase the speed, to reduce the size of the convolutional network model, and to increase the accuracy optimization, a new scaling method, which optimally designs all dimensions of depth, width, and resolution, is proposed based on a branch neural network. A family of HybridBranchNet networks, which is more accurate and efficient than ConvNets, has been created along with this design. HybridBranchNet3 has a classification accuracy of 83.1%. The proposed model was compared with a family of EfficientNet convolutional networks. The comparison results revealed that the proposed network exceeded the mentioned models in terms of accuracy and speed by 1.03% and 39%, respectively. They also showed that the number of trainable parameters is 13% less than that of the EfficientNet network. The proposed method has an accuracy of 92.3% in the CIFAR-100 dataset and 98.8% in the Flowers-102 dataset. Although the architectures such as CoAtNet have slightly higher classification accuracy than the proposed method, they have a greater number of parameters that cannot be used in a conventional system.