As machine learning models become more prevalent and widely used in various fields, it is becoming increasingly important to monitor them for changes in the data they were trained on. This phenomenon is known as “data drift,” and it can significantly impact the performance and reliability of a model. This is especially true for computer vision (CV) models, which are trained on large datasets and are sensitive to even small changes in the input data.
At evoila, we have assessed various methods to effectively and reliably implement data drift monitoring for CV models, as there are currently no widely recognized techniques in the industry to address this issue in image data.
In this blog post, we will discuss data drift monitoring for CV models, why it is important, and how it can be done effectively.
Data drift refers to the change in the statistical properties of the input data used to train a model over time. This can be caused by various factors such as changes in the environment, changes in the input data source, or changes in user behavior. When data drift occurs, the model may become less accurate or even fail to work altogether.
Data drift monitoring is particularly important for CV models due to the nature of the data they operate on. Images, for example, can change over time due to changes in lighting conditions, camera angles, or other factors. If a CV model is trained on a dataset of images taken under certain conditions and then deployed in a different environment, it may not perform as well as expected due to data drift.
In addition, CV models are often used in critical applications such as medical imaging or autonomous vehicles. In these scenarios, any decrease in model accuracy due to data drift can have serious consequences. Therefore, it is essential to monitor CV models for data drift and take appropriate action when necessary.
There are several approaches to monitoring data drift in CV models. One common method is to collect new data over time and compare it to the original training data. This can be done using statistical techniques such as hypothesis testing or by calculating distance metrics between the two datasets. If the statistical properties of the new data are significantly different from those of the training data, it may indicate data drift.
To utilize statistical hypothesis testing for comparing feature distributions in CV models, which typically contain high-dimensional data (e.g., a 96×96 RGB image has 27,648 potentially significant features), a pipeline is required that involves applying dimension reduction techniques. At evoila, we conducted experiments on drifted datasets from the WILDS Collection of Datasets, evaluating various dimensionality reduction methods and statistical tests. Our findings showed that the best approach involved training an Autoencoder Model on the original data and using multiple univariate tests such as Kolmogorov-Smirnov and Cramer von Mises, both with Bonferroni Correction.
Finally, it is important to regularly evaluate model performance on a validation dataset and compare it to the performance on the training dataset. If the performance on the validation dataset decreases significantly over time, it may indicate data drift.
Data drift is a significant challenge in the deployment of CV models, and monitoring for it is essential to maintain model accuracy and reliability. By using statistical techniques and regular performance evaluation, it is possible to effectively monitor for data drift and take appropriate action to maintain model performance.