## 2 Related Works  
CNNs are a specific type of neural network designed to automatically learn the spatial hierarchies of features from images through convolution kernels in different layers [24]. CNNs have been applied to image classification [25], face recognition [23], object detection [26], segmentation [27], and oil sheet detection [10; 16]. Here, we use a CNN as the basis algorithm to classify close-proximity oil sheet images.  
A deep neural network is usually more powerful in feature extraction and in learning non-linear decision boundaries. A CNN with more layers can solve more complex problems or improve the model performance [25]. However, a deeper neural network also makes the gradient hard to backpropagate, resulting in the vanishing gradient problem. The deeper the network, the harder earlier layers are to optimize, leading to degraded performance and lower accuracy [23]. Residual Neural Network [25] (ResNet), was introduced in 2015 to solve this problem. The residual block is the core idea of Resnet. By adding skip connections with no trainable parameters across blocks, the residual block creates a \"highway\" for gradient backpropagation in deep neural networks, which solves the vanishing gradient problem and efficiently optimizes the earlier layers. Here, to efficiently use deeper neural networks with powerful learning ability, we borrow the idea of the residual block to develop the OS-Net.  
The transfer learning approach allows for shifting learned knowledge (extract basic feature, aggregate basic features, and form higher-level feature) from one task with sufficient data to improve the performance in another task (target task) with fewer data and improves performance in the target task [28; 29]. This approach uses layers of the pre-trained model as the starting point to train the target model. There are two popular strategies when using the pre-trained model approach of transfer learning. The first strategy can use a pre-trained model as a feature extractor to apply some of the pre-trained model's weighted layers to extract features and not update the weights of these layers during training with new data for the new task. The second strategy is fine tuning a pre-trained model. The second strategy is a more involved technique, where previous layers are retrained [30] in addition to replacing the final layer. Transfer learning strategies have been widely applied in image-related tasks [29; 30]. Here, we explore transfer learning strategies to determine the most appropriate approach for our purpose. Specifically, we compare three models (OS-Net Without Transfer Learning, OS-Net With Transfer Learning Feature Extraction Strategy, and OS-Net With Transfer Learning Fine-tuning Strategy) to obtain our best OS-Net.  
ImageNet [31] is a large (millions of images) and diverse (1000 classes) dataset that can be used to pre-train models for transfer learning. Models pre-trained on ImageNet have been widely applied, including VGG [32], AlexNet [33], ResNet [34], and Inception [35]. These pre-trained models can extract generic visual features (such as color pattern, edges, elementary shapes) efficiently, achieve high accuracy for various visual task, and are easy to access [35; 36]. Moreover, these networks have repeatedly been applied to different tasks, from which they were originally trained, to improve the target model's performance [36; 37]. Here, we pre-train our OS-Net on a base dataset (ImageNet) and transfer the learned general visual feature extraction ability and adjust the model parameters to fit for the oil sheet prediction task.