用专业英语翻译下面这段话：为了验证改进ResNet网络的分类性能需要设置评价指标。在学术界普遍接受的评价指标是准确性损失和召回率。本文的准确性和损失作为评价指标和召回率进行了讨论。假设分类类别共10类现在有10个测试图片那么一个图片输入网络得到10个类别概率而top-5就是这10个类别概率中取概率最高的前五个类别如果此测试图片的类别在这五个类别中则表明预测正确反之预测错误top-5错误率就是预测错

In order to evaluate the classification performance of the improved ResNet network, evaluation metrics need to be set. The commonly accepted evaluation metrics in the academic community are accuracy, loss, and recall. In this article, accuracy and loss are discussed as evaluation metrics along with recall. Assuming there are 10 classification categories, and there are 10 test images, when one image is input into the network, 10 category probabilities are obtained. The top-5 refers to the top five categories with the highest probabilities among these 10 categories. If the category of this test image is among these five categories, it is considered a correct prediction, otherwise, it is a wrong prediction. The top-5 error rate is the number of wrong predictions divided by all samples, and the top-5 accuracy rate is the number of correct predictions divided by all samples. The top-1 refers to the category with the highest probability among these 10 categories. If the category of this test image is the category with the highest probability, it is considered a correct prediction, otherwise, it is a wrong prediction. The top-1 error rate is the number of wrong predictions divided by all samples, and the top-1 accuracy rate is the number of correct predictions divided by all samples. Recall is a measure of coverage, which measures the proportion of true positives among all actual positives. It can be seen that recall considers the proportion of true positives recalled. Its formula can be derived as follows