Nowadays, a large number of deep convolutional neural network (CNN) models are applied to image classification tasks. However, the authors find that the most widely used evaluation indicator, the Top- N Accuracy indicator, cannot discriminate these models effectively. In this study, they propose a new indicator called Maximum-Spanning-Confusion-Tree indicator to solve this problem. The Maximum-Spanning-Confusion-Tree indicator is computed based on the hierarchical structure of the Maximum Spanning Confusion Tree of the deep CNN model on the dataset and reflect the ability of deep CNN models to discriminate confused categories in the dataset. The hierarchical structure of the Maximum Spanning Confusion Tree can reveal the confused category set of one selected category in the dataset efficiently and flexibly. Experiments show that they can discriminate ten different deep CNN models more accurately with the Maximum Spanning Confusion Tree indicator than the Top- N Accuracy indicator and the Maximum Spanning Confusion Tree intuitively shows the distribution of confused category sets in the dataset so they can find out the weakness of deep CNN models effectively.

中文翻译：

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超越顶级ñ 准确性指标：CNN模型在图像分类中的综合评估指标

如今，大量的深度卷积神经网络（CNN）模型已应用于图像分类任务。但是，作者发现，使用最广泛的评估指标是 ñ精度指标，无法有效地区分这些模型。在这项研究中，他们提出了一个名为“最大跨度混淆树”的新指标来解决此问题。最大扩展混淆树指标是基于数据集上的深CNN模型的最大生成混淆树的层次结构计算的，反映了深CNN模型在数据集中区分混淆类别的能力。最大生成混淆树的层次结构可以有效，灵活地显示数据集中一个选定类别的混淆类别集。实验表明，使用最大生成树混淆树指标，它们可以比使用顶部- ñ 准确性指标和最大生成树混淆树直观地显示了数据集中混淆类别集的分布，因此他们可以有效地找出深度CNN模型的弱点。