## 5 Results and Findings  
This section defines the performance evaluation matrix (such as inference time and model space complexity) and also describes the performance of the model. The inference time of a model is the time required to complete all the model operations. \[\text{inference time}=\frac{\text{FLOPs}}{\text{FLOPS}} \tag{4}\]  
* **FLOPs**: To measure the inference time of a model, we have calculated the total number of computations performed by the model. This is where we mention the term Floating Point Operation (FLOP). This could be an addition, subtraction, division, multiplication, or any other operation that involves a floating point value. The FLOPs provide the complexity of the model. \[\text{Convolutions}-\text{FLOPs}=2\times\text{Number of Kernel}\times\text{Kernel Shape}\times\text{Output Shape}\] (5) \[\text{Fully Connected Layers}-\text{FLOPs}=2\times\text{InputSize}\times\text{ Output Size}\] (6)
* **FLOPS**: The next term is the Floating Point Operations per Second (FLOPS). This term provides information on the efficiency of the hardware system. For this study, 1 FLOPS is considered as 1,000,000,000 operations per second. For a real-time food calorie estimation system, calculating space complexity is very important. The space complexity of a CNN model is realized by the following equation. \[\text{CNN model space complexity}=(cwhk+k)\times p \tag{7}\] \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Width shift range (\%) & 10 \\ \hline Height shift range (\%) & 10 \\ \hline Zoom range (\%) & 20 \\ \hline Shear range (\%) & 10 \\ \hline Rotation range (deg) & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Augmentation parameters. Figure 2: Architecture of the proposed CNN. where \(c\), \(w\), \(h\), and \(k\) stand for the number of kernels, wide, height, and the number of output channels, respectively. \(p\) stands for the number of bytes per element. For this study, 4 bytes (floating point) per element are considered. The model with higher accuracy, and a lesser disparity between the training and validation accuracy ensures the higher performance of the model. On the other hand, the loss function is evaluated by discovering the most suitable hyperparameter for the particular model. All models have been trained applying 80 epochs. Fine tuning of models and model-oriented parameters are used to improve the performance of the models. For model tuning, the filter numbers are set to [16; 32; 64], and the filter sizes are set to [(3,3), (5,5), (7,7)]. Filtering is usually applied to remove noise and undesirable artifacts from the image data set. Model-oriented parameters such as pool size (2,2) are used for feature extraction, dense node (512) is used for the comparison of the images, and the drop (0.5) function is used to prevent overfitting problem. Activation functions such as ReLu and SoftMax are used to prevent the interrupted probabilities of the feature map. Adam optimizer is used to optimize the data. A total of 81 models were generated and these are divided into four groups, which are shown in Table 4. Most of the models were unable to perform as expected. The accuracy is almost 79-80%. However, models with filter size (5,5) provide better validation accuracy than models with filter sizes (3,3). The finest 10 models are shown in Table 5 with detail comparison. The table illustrates that model 44 reveals the best result where  
Figure 4: Real-time food image recognition using model 44.