## 2 State of the Art  
Image classification is the task of assigning a label to an image. Traditionally, hand-crafted features [8] were extracted from the images and used for training classifiers. CNNs have been established among the best learning algorithm for image-based tasks [9] by achieving the best results on the ILSVRC (ImageNet Large-Scale Visual Recognition Challenge) [10]. Despite this, there are cases where the number of images for training a model is scarce, or the classification tasks are challenging. In these cases, manually crafted feature extraction can outperform the results obtained by CNNs [11; 12]. Their parameter optimization and their capability of changing their structures to fit different problems [9; 13] have allowed the improvement of CNNs' performance over the years. Technological advances like the use of graphics processing units (GPUs) have also made their progress possible [14]. As seen in [15], CNNs can be divided into seven different categories according to their architecture: spatial exploitation, feature-map exploitation, depth, width, multi-path, channel boosting, and attention. Multiple networks can appear in various categories. However, these networks need to be trained on a large amount of data, and data gathering and annotation can be a complex, tedious, and time-consuming process. Furthermore, these datasets may soon become outdated, needing the addition of new data [16]. Transfer learning is a technique that allows one to take a model trained for a specific application and apply it to a similar or related task [17]. This approach is used to retain the features obtained from bigger datasets and use them for training a new model on a smaller, similar dataset. Several works have studied the use of transfer learning applied to CNNs for the task of image classification in different fields, but often omit the most recent architectures [6; 18], such as NasNetLarge [19]. A brief summary of the most notable image classification CNNs can be seen in Table 1.  
[PERSON] et al. [17] studied the application of transfer learning on the InceptionV3 [29] architecture pre-trained on the ImageNet dataset [37] and re-trained it on the CIFAR-10 [22] dataset, obtaining 70.1% accuracy and surpassing CNNs trained from scratch on this dataset. [PERSON] et al. [38] applied transfer learning on AlexNet [23], GoogLeNet [25], and ResNet50 [27]. They replaced the last three layers with a fully connected layer, a softmax layer, and a classification output layer for each network. Afterwards, they trained the networks on the CIFAR-10 dataset, obtaining classification accuracy per image category. The resulting average performance for each network was 71.67% for GoogLeNet, 78.10% for ResNet50, and 36.12% for AlexNet. Extensive architecture research and engineering are required to improve neural network classification [39]. To fix this problem, [PERSON] et al. [40] proposed an approach called \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Architecture** & **Top-5 Accuracy (\%)** & **Top-1 Accuracy (\%)** & **Dataset** \\ \hline LeNet [20] & 99.80 & - & MNIST [20] \\ DenseNet [21] & 93.88 & 77.85 & CIFAR-10 [22] \\ AlexNet [23] & 84.60 & 63.30 & ImageNet \\ ZFNet [24] & 84.00 & 62.50 & ImageNet \\ GoogleNet [25] & 89.90 & 69.80 & ImageNet \\ VGG16 [26] & 91.90 & 74.40 & ImageNet \\ ResNet [27] & 94.29 & 78.57 & ImageNet \\ ResNeXt-101 [28] & 95.60 & 80.90 & ImageNet \\ Inception-V3 [29] & 94.40 & 78.80 & ImageNet \\ SENet [30] & 96.20 & 82.70 & ImageNet \\ MobileNet-V1 [31] & 90.92 & 71.56 & ImageNet \\ MobileNet-V2 [32] & - & 74.70 & ImageNet \\ MobileNet-V3 [33] & - & 75.20 & ImageNet \\ EfficientNet [34] & 97.00 & 84.30 & ImageNet \\ Xception [35] & 94.50 & 79.00 & ImageNet \\ Inception-ResNet-V2 [36] & 95.10 & 80.10 & ImageNet \\ NasNetLarge [19] & 96.20 & 82.70 & ImageNet \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Image classification architectures’ accuracy scores.