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A Unified Interactive Model Evaluation for Classification, Object Detection, and Instance Segmentation in Computer

Changjian Chen, Yukai Guo, Fengyuan Tian

    IEEE Transactions on Visualization and Computer Graphics
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    Summary
    This summary is machine-generated.

    Uni-Evaluator offers unified model evaluation for computer vision tasks like object detection. This open-source tool visualizes performance across classification, detection, and segmentation, aiding model improvement.

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    Area of Science:

    • Computer Vision
    • Machine Learning Model Evaluation

    Background:

    • Current model evaluation tools primarily support classification tasks.
    • A significant gap exists in evaluating complex computer vision models like object detection and instance segmentation.

    Purpose of the Study:

    • To introduce Uni-Evaluator, an open-source visual analysis tool for unified model evaluation.
    • To address the limitations of existing tools by supporting classification, object detection, and instance segmentation.

    Main Methods:

    • Formulating discrete and continuous predictions from different tasks into unified probability distributions.
    • Developing matrix-based, table, and grid visualizations for comprehensive model performance analysis.
    • Enabling evaluation from a global overview down to individual sample inspection.

    Main Results:

    • Uni-Evaluator provides a matrix visualization for an overall performance overview.
    • Table visualizations help identify problematic data subsets where models underperform.
    • Grid visualizations display specific samples for detailed analysis.

    Conclusions:

    • Uni-Evaluator facilitates effective model evaluation across diverse computer vision tasks.
    • The tool aids researchers in making informed decisions for model performance enhancement.
    • Case studies confirm the tool's efficacy in practical model improvement scenarios.