What is one potential consequence of training an ML model with high-resolution images?

Training a machine learning model with high-resolution images can indeed lead to insufficient computing power. High-resolution images mean that the amount of data fed into the model significantly increases, resulting in larger input sizes. Processing this volume of data requires more memory and computational resources. If the computing infrastructure is not sufficiently robust—such as lacking adequate GPU power or RAM—the model may experience slow performance or even fail to train altogether.

Additionally, larger image sizes add complexity to the model training process. The model must learn to identify features in more detailed images, which can require sophisticated architecture and longer training durations. This situation can lead to resource bottlenecks and ultimately hinder the model’s ability to train effectively. Thus, it is essential to ensure that the computing environment is capable of handling the demands of high-resolution training to avoid the risk of running into issues related to insufficient processing power.