An unforeseen change in the input data is called “drift,” and may impact the accuracy of machine learning models. In this talk, we present a novel scheme for diagnosing data drift in the input streams of image classification neural networks. Our proposed method for drift detection and quantification uses a threshold dictionary for the prediction probabilities of each class in the neural network model. The method is applicable to any drift type in images such as noise and weather effects, among others. We will share experimental results on various data sets, drift types and neural network models to show that our proposed method estimates the drift magnitude with high accuracy, especially when the level of drift significantly impacts the model’s performance.