# Urban Plants Classification Using Deep-Learning Methodology: A Case Study on a New Dataset
## 4 Case Study  
### Methods  
All recent most successful models for image classification are CNN-based. It has been shown that shallow layers extract simple (low-level) features of an image, and deeper layers can extract more complex (high-level) features. Thus, to make CNN more accurate, researchers mainly increase their depth by adding more layers. Table 2 summarizes all the networks applied in our study--including their architecture, size, and number of parameters--in the chronological order of their introduction. The size of the networks addresses the models pre-trained on ImageNet. Below, we briefly explain about each model. One of the first successful inventions that demonstrated that the representation depth is beneficial for the classification accuracy is VGGNet [26], introduced in 2014 for large-scale image classification. VGGNet is composed of a sequence of convolutional and pooling layers, followed by three dense layers. In this work, we use VGG16 and VGG19. The main difference between them is a number of convolutional layers. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Network** & **Used Models** & **Architecture** & **Size** & **Params** \\ \hline \multirow{2}{*}{**VGGNet**} & VGGNet16 & 13 convolution and 3 fully connected & 113 MB & 138 M \\  & VGGNet19 & 16 convolution and 3 fully connected & 153 MB & 144 M \\ \hline \multirow{2}{*}{**Inception**} & Inception-v3 & 48 layers & 169 MB & 24 M \\  & Inception-ResNet-v2 & 164 layers & 419 MB & 56 M \\ \hline
**Xception** & Xception & 36 convolution layers & 160 MB & 24 M \\ \hline
**DenseNet** & DenseNet201 & 5 convolution (201 total) layers & 144 MB & 20 M \\ \hline
**MobileNet** & MobileNet-v2 & 3 convolution (20 in total) layers & 19 MB & 13 M \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of networks used in our study. Figure 2: Classes that cannot be distinguished on color alone (moon cactus, nerve plant, poinsetia) or shape alone (colens). The Inception [27] deep convolutional architecture was introduced in 2015 (Inception-v1). Later, the Inception architecture was refined, first by the introduction of batch normalization [28] in Inception-v2, subsequently by additional factorization ideas [29] in Inception-v3. Next year, this architecture was refined again, in [30], and several architectures for Inception-ResNet, including Inception-ResNet-v2, were proposed. In our case study, we use two Inception-based models: Inception-v3 and Inception-ResNet-v2. The difference between them is that Inception-v3 is a deep CNN not utilizing residual connections, while Inception-ResNet-v2 is Inception style networks that utilize residual connections instead of filter concatenation. The Xception [31] model was proposed in 2017. It is an extension of the Inception architecture which replaces the standard Inception modules with depthwise Separable Convolutions. The Xception architecture has the same number of parameters as Inception-v3, but its performance is better due to more efficient use of these parameters. While CNNs go deeper and the path from the network input layer to its output layer becomes longer, the chance of information to reach the other side gets lower. **DenseNet**[32], introduced in 2017, solves this problem by ensuring maximum information flow with connections from each layer to every other layer in a feed-forward fashion. DenseNet has several compelling advantages: it alleviates the vanishing-gradient problem, strengthens feature propagation, exploits the potential of the network through feature reuses, and substantially reduces the number of parameters. MobileNets [33], also introduced in 2017, are based on a streamlined architecture that uses depthwise separable convolutions to build light-weight deep neural networks. The inventors of MobileNets empirically showed that smaller and faster MobileNets can be built, using width and resolution multipliers by trading off a reasonable amount of accuracy to reduce size and latency.