## 4 Implementation and Experiments
### Experimental Results
#### 4.4.2 Food Volume Estimator Experimental Results  
In this section, we will demonstrate the accuracy of food volume measurement based on multi-view stereo using the NutritionVerse3D dataset. Based on the experimental results from E-Volume [7], we found that the best prediction results were achieved with a shooting angle of 60 degrees and 40 images. Therefore, this experimental setup is used for the comparison experiment. To analyze the results more clearly, we divided the foods in the dataset into four categories: meals, meat, fruit and vegetables, and snacks, and we calculated their L1 loss and MAPE (mean absolute percentage error). Table 12 below summarizes the results for each category.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **Top1** & **Top5** & **Precision** & **Recall** & **F1 Score** \\ \hline ResNet50-quickgelu & 0.08 & 0.24 & 0.09 & 0.08 & 0.06 \\ ALIGN & 0.13 & 0.35 & 0.16 & 0.13 & 0.11 \\ SigLIP & 0.01 & 0.02 & 0.00 & 0.00 & 0.00 \\ CLIP & 0.19 & 0.42 & 0.19 & 0.17 & 0.15 \\ NutritionCLIP & 0.91 & 0.97 & 0.88 & 0.90 & 0.89 \\ \hline \end{tabular}
\end{table}
Table 10: Zero-shot learning model performance on the CNFOOD-241 dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Model** & **Top1** & **Top5** & **Precision** & **Recall** & **F1 Score** \\ \hline ResNet50-quickgelu & 0.19 & 0.43 & 0.18 & 0.19 & 0.16 \\ ALIGN & 0.28 & 0.58 & 0.35 & 0.28 & 0.26 \\ SigLIP & 0.51 & 0.79 & 0.54 & 0.51 & 0.49 \\ CLIP & 0.29 & 0.56 & 0.32 & 0.29 & 0.26 \\ NutritionCLIP & 0.73 & 0.94 & 0.79 & 0.74 & 0.74 \\ \hline \end{tabular}
\end{table}
Table 11: Zero-shot learning model performance on the Taiwanese Food 101 dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **Top1** & **Top5** & **Precision** & **Recall** & **F1 Score** \\ \hline ResNet50-quickgelu & 0.43 & 0.72 & 0.46 & 0.43 & 0.42 \\ ALIGN & 0.48 & 0.73 & 0.55 & 0.48 & 0.48 \\ SigLIP & 0.62 & 0.82 & 0.71 & 0.62 & 0.64 \\ CLIP & 0.56 & 0.84 & 0.62 & 0.56 & 0.55 \\ NutritionCLIP & 0.80 & 0.96 & 0.82 & 0.80 & 0.80 \\ \hline \end{tabular}
\end{table}
Table 9: Zero-shot learning model performance on the Food 101 dataset.  
The table above indicates varying results in volume measurement across four categories. The meals category shows higher L1 loss and MAPE values, at 67.90 and 37.76, respectively, suggesting that predicting volume for this category is relatively challenging. This difficulty may be attributed to the greater diversity in the shapes and sizes of these foods. Conversely, the meat category exhibits a relatively low L1 loss of 29.47 and MAPE of 25.89, indicating good accuracy in volume measurement for this group.  
For the fruits and vegetables category, the L1 loss is 12.25, and MAPE is 27.70. These figures suggest a good absolute error in volume measurement but a slightly higher percentage error, possibly due to the irregular shapes of the items in this category. The snacks category, with an L1 loss of 18.10 and a MAPE of 23.50, demonstrates high accuracy in volume measurement. This is likely because snacks usually have regular shapes and smaller volumes.  
Overall, the MVS-based volume measurement method performs differently across various food categories. These results suggest room for improvement for foods with diverse shapes and sizes.