# Methodology for Evaluating the Generalization of ResNet
## 6 Conclusions  
This study presents a new perspective on evaluating the generalization ability of CNN models by using IoU, which addresses the issue of poor accuracy and applicability of CNN model generalization evaluation methods for the task of image classification. The method, based on the ResNet architecture, leverages IoU to quantify the ratio of task-relevant features within the model's learned feature set. Task-relevant features are identified through Class Activation Maps (CAMs), focusing on the object's location within  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Accuracy Threshold** & \(\geq\)**95** & \(\geq\)**90** & \(\geq\)**85** & \(\geq\)**80** \\ \hline IoU (using saliency map) & \(-0.8465\) & \(-0.8425\) & \(-0.8322\) & \(-0.8215\) \\ IoU (using bounding box) & \(-0.9144\) & \(-0.9096\) & \(-0.8996\) & \(-0.8911\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: The correlation between generalization gap and IoU calculated based on manually labeled bounding boxes and automatically generated saliency maps from the ResNet18 models.  
Figure 6: Examples of generated saliency maps and original manually labeled bounding boxes (i.e., the green box), where the first row is the original image and the second row is the corresponding saliency map and bounding box. Columns (**a–e**) are five examples, in descending order of quality from the saliency map.  
an image. To validate our method, we trained 2000 ResNet models on a subset of ImageNet, providing a robust dataset for generalization analyses.  
Our findings underscore the method's effectiveness and accuracy, with the IoU calculations only requiring training data and a trained model, thereby enhancing computational efficiency. This approach not only evaluates model generalization but also aids in selecting optimal models and tuning hyperparameters. The method's robustness was evidenced by its minimal sensitivity to variations in CAM algorithms. However, it should be noted that the reliance on bounding box annotations for the IoU calculation presents a limitation.  
In conclusion, the IoU-based method offers a promising framework for evaluating CNN model generalization in image classification tasks. The extensive model set developed in this study lays a solid foundation for future research. While current practices necessitate manual bounding box annotations, future work will explore automated annotation techniques to address this limitation.  
Conceptualization, Q.Z.; methodology, A.D.; software, A.D.; validation, A.D. and Y.D.; resources, Q.Z.; data curation, A.D.; writing--original draft preparation, A.D.; writing--review and editing, Y.D.; supervision, Q.Z.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data and code will be available at [[https://github.com/[PERSON]/generalizationEvaluate](https://github.com/[PERSON]/generalizationEvaluate)]([https://github.com/[PERSON]/generalizationEvaluate](https://github.com/[PERSON]/generalizationEvaluate)), accessed on 29 April 2024.  
The authors declare no conflicts of interest.