Abstract: Methods and systems are disclosed to enable users to analyze failure modes of computer vision machine learning models using error featurization. In one embodiment, an image classification model is expressed in a scatter plot of prediction errors over a labeled dataset. A user interface allows practitioners to identify patterns in data that cause the model to fail and supports high precision analysis of critical failure modes of trained machine learning (ML) models. The embodiment helps ML practitioners improve their curation, labeling, and training processes. The embodiments allow ML practitioners to choose the most relevant data for subsequent improvement of an ML model. The highly targeted data curation leads to a multifold reduction in costs and time for labeling and training data.

Abstract: Methods and systems are disclosed to enable users to analyze failure modes of computer vision machine learning models using error featurization. In one embodiment, an image classification model is expressed in a scatter plot of prediction errors over a labeled dataset. A user interface allows practitioners to identify patterns in data that cause the model to fail and supports high precision analysis of critical failure modes of trained machine learning (ML) models. The embodiment helps ML practitioners improve their curation, labeling, and training processes. The embodiments allow ML practitioners to choose the most relevant data for subsequent improvement of an ML model. The highly targeted data curation leads to a multifold reduction in costs and time for labeling and training data.