# Trademark Image Similarity Detection Using Convolutional Neural Network
## 4 Experiments and Results  
Several parameters and procedures played essential roles in the final results. Thus, many experiments with different techniques should be conducted to consider all possible situations. Some of these procedures include the methods used to process the input images and extract the image features, as well as additional processes to improve the system performance.  
There is no single optimal or standard CNN structure. Each problem might be solved using various CNN structures, but the best-fitting one can be obtained through experiments for a given problem. Indeed, several CNN architectures that were pretrained on a large dataset for image classification purposes and achieved high accuracy are available to reuse for related problems.  
With reference to one of the most valuable competitions that encourages researchers and developers to develop such a solution, is the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). It is an annual competition that was conducted from 2010 to 2017 for object detection and image classification. Many of the current innovations have sprung from the ILSVRC competition. AlexNet, VGGNet, and ResNet are some of the popular CNN architectures that were developed during an ILSVRC [26].  
**AlexNet** was introduced in 2012 by [PERSON] et al. [27]. AlexNet was trained on more than 1.2 million images of the ImageNet dataset to classify them into 1000 classes belonging to different natural objects. It consists of 650,000 neurons and eight layers in total (five convolutional layers and three fully connected layers). The max-pooling layer follows the first, second, and fifth convolutional layers. Moreover, the rectified linear unit (ReLU) is applied to each neuron's output. The ReLU is the factor that makes AlexNet more efficient compared with other activation functions, as it helps to accelerate the learning process and computational efficiency. To reduce the overfitting, data augmentation and dropout methods were applied. As a result, AlexNet won the ILSVRC-2012 competition by achieving top-1 and top-5 test set error rates of 37.5% and 17%, respectively.  
**VGGNet** was inspired by the AlexNet architecture [28]. It consists of a \(3\times 3\) kernel-size filter to produce a better recognition rate than the large-size filter. There are two different architectures of VGGNet: VGGNet16 and VGGNet19; these consist of 16 and 19 layers, respectively. Both architectures are composed of \(3\times 3\) convolutional, \(2\times 2\) max-pooling, and three fully connected layers. The last of the fully connected layers utilizes a softmax for classification purposes. Consequently, VGGNet achieved a top-5 test set error rate of 6.8%.  
**ResNet** stands for Residual Network, a neural network introduced by [PERSON] et al. [29]. It was inspired by the VGG network. The main idea behind ResNet is to \"identity shortcut connections,\" which skip one or more network layers. The ResNet architecture consists of a 34-layer plain network, along with the shortcut connection. Most convolution layers have 3 \(\times\) 3 filters and perform downsampling with a stride of two. The network ends with a global average pooling layer, followed by a fully connected layer that utilizes a softmax. As a result, ResNet won first place in the ILSVRC-2015 classification competition by achieving a top-5 test set error rate of 3.57%.  
The following subsections briefly describe each experiment along with the corresponding evaluation results. The experiments are divided based on the image features extracted to make them easy to read.