## 2 Background  
The transfer learning method is used to speed up training process and improve the performance of new untrained models by using the weights of an existing model [12]. The concept of transfer learning is modeled after human intelligence by using the current knowledge to solve new problems faster or better [13]. In transfer learning, the top layers of a pretrained model are frozen and specific layers above it are added to create a new model [12]. However, the benefit of transfer learning is affected by the data and task of the new model [13]. We chose to test the four VGG16, InceptionV3,RestNet50, and ResNeXt50 as possible platforms for classifier for FEAPAS because they have been widely successful in image classification.  
In the ILSVRC 2014 competition, Visual Geometry Group VGG16 was ranked the best method in localization and second best in classification [12]. VGG16 addressed the depth of CNN architecture by adding more layers with small filters (3 \(\times\) 3). VGG16 was trained on ImageNet with 1000 classes. The structure of VGG16 is shown in Figure 1[14].  
InceptionV1 won the ILSVRC 2014 classification. It was proposed to overcome the overfitting of the data in the deep convolutional neural networks by using multiple filters of different sizes on the same level [15]. InceptionV3 is designed to optimize the performance  
Figure 1: VGG16 architecture where each color represents a block of layers with the same kernel size.  
of the previous versions of inception which suffer from high computations by applying factorized convolutions and aggressive regularization [16].  
The residual neural network (ResNet) won ILSVRC 2015 in image classification, detection, and localization. Moreover, it won MS COCO 2015 detection and segmentation. ResNet was inspired by VGG even though it is deeper and less complex [17].  
Neural networks may become less efficient as the number of layers increase and the model deepens [18; 19; 20]. ResNet adds a direct connection to the layers of the network to solve the problem of vanishing gradients, which arises from the deep depth of the network. This connection preserves a certain percentage of the output of the previous network layers [18; 19; 20]. Deep neural networks have more layers where wide neural networks have more kernels. However, a study comparing wider and deeper neural networks showed that shallow networks outperformed the much deeper residual networks in classification and segmentation [20]. In fact, the number of trainable parameters is the cause of ResNet performance [20]. The structure of ResNet50 is shown in Figure 2[17].  
ResNeXt was a runner up in ILSVRC 2015. It was designed to overcome the complexity in ResNet, which emerge from staking the modules of the same topology. ResNeXt inspired its structure from the inception model and used a simpler branch design method than inception [21; 22].  
The optimizer algorithm plays a critical role in the neural network training process. Studies have investigated the performance and declared a specific optimizer to work better with a particular problem [23; 24; 25].  
The stochastic gradient descent (SGD) is the popular algorithm for solving optimization problems, but it requires manually adjusting the learning rate decay [23; 24]. To overcome this manual process in SGD, [PERSON] proposed adaptive moment estimation (ADAM). ADAM makes the model converge faster and occupies little memory [24; 25]. Root mean square propagation RMSProp is an optimization algorithm first proposed by [PERSON] to speed up the model convergence through loss function optimization [24]. Where ADAM and RMSProp suffer from the variance in adaptive learning rate, rectified ADAM (RAdam) has effectively solved it [26; 27].