# Deep Learning-Based Imagery Style Evaluation for Cross-Category Industrial Product Forms
## 4 Imagery Style Evaluation (ISE) Method for Cross-Category Industrial Product Forms
### Testing and Comparative Analysis of the Imagery Style Evaluation Model  
This paper employs several metrics to evaluate model performance, including Precision (P), Recall (R), F1-score, Frames Per Second (FPS), Average Precision (AP), and mean Average Precision (mAP). The F1-score, being the harmonic mean of precision and recall, provides a comprehensive assessment of model quality. FPS indicates the model's real-time processing speed. AP measures the model's effectiveness in detecting specific categories, while mAP reflects the overall performance of the detection model. This experiment aims to assess the enhanced YOLOv5 model within the ISE method for evaluating the imagery style of various product forms. Figure 10 presents the loss convergence curves of both the enhanced YOLOv5 model and the original YOLOv5 model. Starting from epoch 0, the loss values consistently decrease and gradually stabilize as training approaches approximately 350 epochs. Therefore, this study employs the weights obtained after 400 training iterations for the enhanced YOLOv5 model. Figure 11 shows the results of the enhanced YOLOv5 model. During testing, the Precision-Recall curve gradually approaches 1, and the confidence score associated with the F1 curve also approaches 1. Additionally, the [EMAIL_ADDRESS] across all categories reaches 0.861. These results indicate a strong positive correlation between product forms that align with user emotional preferences and the model's performance in the test set. Figure 11: Test results of the enhanced YOLOv5 model. Figure 10: Loss function convergence curves. As shown in Figure 12, the confusion matrix of imagery styles in the test set demonstrates how semantic similarity influences misclassification rates. Semantically opposite imagery style pairs, such as \"Dynamism (Dy)\" vs. \"Steadiness (St)\", \"Softness (So)\" vs. \"Toughness (To)\", or \"Naturalness (Na)\" vs. \"Technology (Te)\", exhibit relatively low misclassification rates. In contrast, due to complex and ambiguous associations in product form features, \"Dynamism (Dy)\" is often misclassified as \"Technology (Te)\", \"Softness (So)\" as \"Steadiness (St)\", and \"Toughness (To)\" as \"Technology (Te)\". Among these, \"Toughness (To)\" exhibits the highest misclassification rate, primarily because it shares overlapping and ambiguous semantic characteristics with both \"Technology (Te)\" and \"Steadiness (St)\", which makes it more difficult for the model to distinguish accurately. The confusion matrix further validates the consistency between the model's recognition and human visual perception. As revealed in the user preference survey (Section 3.1.3), morphological features of different imagery styles can be ambiguous or overlapping in certain scenarios, thereby affecting perceptual consistency. This is especially challenging for designers with limited experience, who may struggle to differentiate nuanced imagery styles in product forms. Therefore, incorporating diverse user preference data during model training not only helps mitigate individual subjective bias but also enhances the model's capacity to distinguish complex morphological features across styles. Traditional design evaluation methods (such as expert scoring and user surveys) played an important role in early research. However, their limitations have been systematically summarized in the literature [73]. These methods require repeated, time-consuming, and labor-intensive investigations for each product category, with low reusability of results, leading to low efficiency and poor scalability. In the field of product design evaluation, Support Vector Machines (SVM) remains a commonly used machine learning approach. However, its applicability is relatively limited. As noted in [13; 14], SVM performs well only in scenarios involving small sample sizes with clearly defined product characteristics, but its effectiveness declines significantly in contexts involving multiple categories with ambiguous attributes.