# Research on the Classification of Sun-Dried Wild Ginseng Based on an Improved ResNeXt50 Model
## 5 Results  
### Experimental Result  
To demonstrate the effectiveness of the experimental model, comparisons were made with several classic convolutional neural network models: ResNet50, AlexNet [32], iRes \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Bottleneck** & **Accuracy \%** & **Loss** & **Training Time per Round/s** & **Parameter/M** \\ \hline ResNeXt50 & 87.73 & 0.131 & 79 & 92.53 \\ Ghost Module & 93.14 & 0.105 & 76 & 82.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison before and after replacing Ghost module. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Attention Mechanism** & **Accuracy \%** & **Loss** & **Convergence Rounds** & **Training Time per Round/s** & **Parameter/M** \\ \hline No Attention & 86.71 & 0.384 & 77 & 83 & 74.47 \\ CBAM & 87.18 & 0.279 & 72 & 80 & 89.93 \\ ECA & 89.02 & 0.126 & 71 & 79 & 84.54 \\ SK & 89.96 & 0.162 & 69 & 77 & 95.48 \\ SE & 93.14 & 0.105 & 63 & 76 & 82.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of experimental results on multiple attention mechanisms. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Activation Function** & **Accuracy \%** & **Loss** & **Parameter/M** \\ \hline Sigmoid & 79.11 & 0.857 & 85.59 \\ Leaky ReLU & 84.55 & 0.681 & 86.74 \\ ReLU & 86.88 & 0.248 & 83.27 \\ Tanh & 89.72 & 0.173 & 87.29 \\ ELU & 93.14 & 0.105 & 82.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of experimental results of multiple activation functions. Net50 [33], and EfficientNet_v2_s [34]. As shown in Table 5, the improved model shows significant advantages in terms of convergence epochs and training time per epoch. Its accuracy increased by 10.22%, 5.92%, 4.63%, and 3.4% compared to these four classic models, while the loss values decreased by 0.261, 0.103, 0.03, and 0.007, respectively. The parameter count was reduced by 68% compared to AlexNet, and both convergence epochs and training time per epoch were reduced. Figure 7 compares the accuracy and loss values of each model. To make the model's position more transparent and convincing, we used the Saposhnikovia diveratica dataset from [PERSON] et al., consisting of 2001 images categorized into five types based on their origin [35]. Below are the conclusions drawn from comparisons: experiments demonstrate that the model proposed in this study is superior to the improved iResNet model. Furthermore, the morphological characteristics of sun-dried wild ginseng are more complex and variable, with interwoven roots, providing sufficient evidence that the improved ResNeXt50 model has better expressive ability and performance. The experimental results are shown in Table 6.  
Heatmaps generated using the Grad-CAM [36] tool can visually display the areas the model focuses on when processing images, aiding in understanding the model's decision-making process. Figure 8 compares the heatmaps of the improved model. The improved model exhibits more refined feature extraction and focus, with more accurate attention to regions and a stronger ability to extract ginseng features. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Loss** & **FLOPs** & **Inference Time** & **FPS** \\ \hline Our Model & 94.91\% & 0.1478 & 3.21 Gmac & 6.05 ms & 165.2 fps \\ Improved iResNet & 93.72\% & 0.2192 & 3.32 Gmac & 6.29 ms & 158.9 fps \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison results using the Saposhnikovia diveratica dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy/\%** & **Loss** & **Converg-Ence Rounds** & **Training Time per Round/s** & **Parameter/M** \\ \hline ResNet50 & 82.92 & 0.366 & 80 & 78 & 98.75 \\ AlexNet & 87.22 & 0.208 & 96 & 77 & 233.08 \\ iResNet50 & 88.51 & 0.135 & 85 & 81 & 97.49 \\ Effcientnet\_v2\_s & 89.74 & 0.112 & 77 & 79 & 83.46 \\ Our model & 93.14 & 0.105 & 63 & 76 & 82.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of experimental results of multiple models.