How to Fix Computer Vision Data Drift

Computer vision models are susceptible to performance decline over time due to two types of drift: data drift and concept drift. Data drift occurs when environmental changes, such as updates in object designs or lighting conditions, cause the input data to deviate from the data on which the model was originally trained. Concept drift happens when the model's objectives evolve, necessitating adjustments in the model's focus. Maintaining performance requires regularly monitoring and addressing these drifts through tools like Roboflow Collect, which passively collects images to evaluate data drift. By analyzing the similarity between new data and the validation set, users can identify drift and update their models accordingly. The process involves setting up datasets, collecting and annotating images, and using scripts to measure and correct drift, ensuring that computer vision models remain accurate and effective in changing environments.