用专业英语翻译下面这段话：表4显示了具有相同数据集和训练策略的三个网络模型在训练过程的最终准确率和召回率及单一类准确率。图7为经过50个epoch训练的三个网络的验证损失的变化情况。这三个网络在大约40次训练后趋于稳定。从误差和准确性两方面可以看出本文提出的每一个改进都能对分类任务产生积极的影响。通过增加CBAM模块和修改下采样方法得到的ResNet-50- a模型比原ResNet-50模型精度提

Table 4 shows the final accuracy, recall, and single-class accuracy of three network models trained on the same dataset and training strategy. Figure 7 displays the validation loss of the three networks after 50 epochs, which stabilized at around 40 epochs. Both the error and accuracy metrics indicate that each proposed improvement has a positive impact on the classification task. The ResNet-50-a model, achieved by adding a CBAM module and modifying the downsampling method, outperforms the original ResNet-50 model by 2.50% in accuracy, with improvements in recall and single-class accuracy. The ResNet-50-b model, with further improvements, surpasses the ResNet-50-a model by 1.92% in accuracy and the original ResNet-50 model by 4.62%, achieving the highest recall and single-class accuracy. As the complexity of the model structure and the number of parameters increase from the original ResNet-50 to ResNet-50-a and ResNet-50-b, the time complexity of ResNet-50-a increases by 32.6%, while its accuracy improves by 2.50%. ResNet-50-b achieves a 4.62% improvement in accuracy with a 40.5% increase in time complexity compared to the original ResNet-50 model. These results demonstrate that the proposed improvements can significantly enhance the network's classification performance while only slightly increasing its time complexity