How to select the best machine learning models for computer vision?

Choosing the best machine learning models for computer vision involves assessing various factors beyond standard metrics like precision, recall, and RMSE. The complexity arises because these metrics can obscure crucial information about model performance in specific contexts, such as different customer sites or image variations. Developers should employ subgroup analysis to ensure models perform consistently across different conditions, and robustness testing to gauge how models handle input variations. It's also vital to consider model biases and fairness, especially when these could affect user experience or safety. Production constraints, like memory footprint and inference time, can influence the model choice as they may diverge from performance observed in controlled environments. The use of tools like MLTest can help manage these complexities by providing a comprehensive view of model performance, including robustness, biases, and operational metrics, thereby enabling better decision-making in model selection.