A Unified Interactive Model Evaluation for Classification, Object Detection, and Instance Segmentation in Computer Vision
This addresses a gap in model evaluation for computer vision researchers and practitioners, but it is incremental as it builds on existing evaluation concepts with a new tool.
The paper tackles the lack of evaluation tools for complex computer vision models like object detection by developing Uni-Evaluator, an open-source visual analysis tool that unifies evaluation for classification, object detection, and instance segmentation, with case studies demonstrating its effectiveness.
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.