# Comparison of Deep Transfer Learning Models for the Quantification of Photoelastic Images
## 2 Materials and Method
### Deep Transfer Learning Model Method  
In transfer learning, a neural network in a similar or new field can achieve high accuracy by copying some of the weights trained on a large dataset provided by ImageNet [45] and then retraining from that state. While deep learning methods generally require a large number of data for training, transfer learning has the advantage of achieving high accuracy even when the number of data is limited. When the number of photoelastic images that can be obtained experimentally is limited, transfer learning can be considered a good learning method to apply.  
Table 2 shows the representative transfer learning models applied in this study to solve the regression problem based on photoelastic images. A research team from the University of Oxford first introduced the VGG model [38] in 2014. The model is utilized for various computer vision tasks, including image classification, object detection, and image segmentation. It is characterized by its simple structure, primarily consisting of 3 \(\times\) 3 convolutional layers and 2 \(\times\) 2 max-pooling layers. VGG-16 and VGG-19 indicate the number of weight layers, with 16 and 19 layers, respectively. However, the model requires a large amount of computational power and memory, making it challenging to run the model in resource-limited environments.  
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Google developed Inception [39], a convolutional neural network model, in 2015. It is also known as GoogLeNet. It uses a parallel convolutional structure to apply convolutional filters of various sizes in parallel, extracting features at different scales. While it is computationally efficient, its complex structure makes it difficult to interpret. It also requires a significant amount of memory and computational power, necessitating appropriate hyperparameter tuning. InceptionV3 is an improved version of previous models, breaking down the \(5\times 5\) convolutional filters into smaller \(3\times 3\) filters to increase computational efficiency.  
Microsoft Research developed ResNet (Residual Network) [40], a convolutional neural network architecture, in 2016. ResNet addresses the learning problems in deep networks by adding the input to the output of each layer using a structure called residual connections, making it easier to backpropagate gradients and mitigating the vanishing gradient problem. However, it may require significant computational power and memory for deep models. An improved model, ResNet152V2, introduced in 2016, features a deep network structure comprising a total of 152 layers. The pre-activation structure of ResNet152V2 applies the activation function before the convolutional operation, further stabilizing the learning process.  
In 2017, researchers introduced DenseNet [41], a convolutional neural network architecture characterized by dense connections where each layer is connected to all its preceding layers. This allows for higher performance with fewer parameters, mitigates the vanishing gradient problem, and enables efficient learning in deeper networks. However, the dense connections can make the model complex and difficult to interpret. DenseNet201 is a variant of the DenseNet architecture, consisting of a total of 201 layers. Its deeper network structure allows for the learning of more complex features.  
Introduced in 2019, EfficientNet [42] is designed to prioritize model size and computational efficiency. It uses compound scaling to simultaneously adjust the width, depth, and image resolution, achieving high performance with relatively fewer parameters and computational resources. This gives it the advantage of excellent computational efficiency. EfficientNetB7 is one of the most complex versions of the EfficientNet architecture, having the highest number of layers, nodes, and input image sizes. It is the model in the EfficientNet series that can be expected to deliver the highest performance.