Existing model evaluation tools mainly focus on evaluating classification models, leaving a gap in evaluating more complex models, such as object detection. In this paper, we develop an open-source visual analysis tool, Uni-Evaluator, to support a unified model evaluation for classification, object detection, and instance segmentation in computer vision. The key idea behind our method is to formulate both discrete and continuous predictions in different tasks as unified probability distributions. Based on these distributions, we develop 1) a matrix-based visualization to provide an overview of model performance; 2) a table visualization to identify the problematic data subsets where the model performs poorly; 3) a grid visualization to display the samples of interest. These visualizations work together to facilitate the model evaluation from a global overview to individual samples. Two case studies demonstrate the effectiveness of Uni-Evaluator in evaluating model performance and making informed improvements.

翻译：现有的模型评估工具主要聚焦于分类模型的评估，在评估更复杂的模型（如目标检测）时存在空白。本文开发了一款开源可视化分析工具Uni-Evaluator，支持计算机视觉中分类、目标检测与实例分割的统一模型评估。该方法的核心思想是将不同任务中的离散和连续预测结果统一表示为概率分布。基于这些分布，我们开发了：1）基于矩阵的可视化方法，用于呈现模型性能概览；2）表格可视化方法，用于识别模型表现欠佳的问题数据子集；3）网格可视化方法，用于展示感兴趣样本。这些可视化方法协同工作，从全局概览到单个样本逐步推进模型评估。两个案例研究证明了Uni-Evaluator在评估模型性能及指导模型优化方面的有效性。