A Unified Interactive Model Evaluation for Classification, Object
  Detection, and Instance Segmentation in Computer Vision
Changjian Chen Yukai Guo Fengyuan Tian Shilong Liu Weikai Yang Zhaowei Wang Jing Wu Hang Su Hanspeter Pfister Shixia Liu
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.