## 3 Results
### Experiments
#### 3.1.2 Experimental Parameter Setting  
In order to avoid memory overflow, the ESERNet model with SERNet, repVGG, and ResNeXtare were involved for performance comparison, in which the same training set and verification set were used. The model is built by pytorch and the experiment was carried out on pycharm. The number of iteration rounds was set to 100, and the loss function was CrossEntropyLoss. Stochastic gradient descent (SGD) was selected as the optimization algorithm. The setting of the batch size and learning rate was carried out by the grouping and combination method. Among them, the batch sizes were set to 16, 32, and 64, and the learning rates were set to 0.1, 0.01, and 0.001, which formed a total of nine combinations. At the same time, the results of the dataset training under different partitioning methods fluctuated greatly, and the average results obtained from the repeated experiments could obtain a good approximation to the generalization results. Therefore, this paper adopts k-fold cross-validation to select the parameters, improve the accuracy of the model evaluation, and finally determine the optimal parameter combination. The model structure and parameters are used to classify the abnormal state of the blast furnace tuyere images.  
In this paper, k is set to five, which means that there are five folds in the cross-validation. The dataset is equally divided into five parts, four parts are used as the training set and one part is used as the validation set. The experiment is run five times as a whole, and the average value of the five validation results is taken as the validation error of this model. The k-fold cross-validation results are shown in Figure 7. The results suggest that in the ESERNet model, when the batch size is 16 and the learning rate is 0.1, the average accuracy reaches 96.73%, which yields a best performance; when the learning rates are 0.01 and 0.001, the average accuracy decreases significantly. When the batch size was set to 32 or 64, the average accuracy of the predictions was the highest when the learning rate was 0.1, while when the learning rates were 0.01 and 0.001, the average accuracy decreased significantly and finally reached about 50%. The SERNet and repVGG models are consistent with the ESERNet model. In the ResNeXt model, when the batch size is 16 and the learning rate is 0.01, the average accuracy of the model is the highest, while when the learning rates are 0.1 and 0.001, the average accuracy rate decreases significantly. When the batch sizes are 32 and 64, the average accuracy rate is the highest when the learning rate is 0.1, while the average accuracy rate decreases significantly when the learning rates are 0.01 and 0.001. The optimal parameter combination of each model is shown in Table 1. The batch size of the ESERNet model is 32, the learning rate is 0.1, and the average accuracy rate is 96.73%; the batch size of the ERNet model is 16, the learning rate is 0.1, and the average accuracy rate is 97.01%; the batch size of the repVGG model is 32, the learning rate is 0.1, and the average accuracy rate is 96.74%; the batch size of the ResNeXt model is 32, the learning rate is 0.1, and the average accuracy rate is 96.69%. In contrast, the SERNet model has the best effect. Due to the introduction of the squeeze-and-excitation and residual modules,the model can better learn the hidden information in different data so as to obtain better results; the ESERNet introduces the squeeze-and-excitation and residual modules, but due to the reduction in the number of channels, the complexity of the model is reduced, and the ability of the model to extract features is also slightly reduced.