Which statistical performance metric should you prioritize when evaluating deep learning models for image classification?

When evaluating deep learning models specifically for image classification tasks, prioritizing Cross-Entropy Loss is particularly relevant because it directly measures the performance of a classification model where the output is a probability value between 0 and 1. Cross-Entropy Loss quantifies the difference between the true labels and the predicted probabilities, which is essential in classification tasks where the goal is to assign an image to one of several categories.

In image classification, models often output a probability distribution across different classes using softmax activation. Cross-Entropy Loss is designed to penalize incorrect classifications effectively, providing a gradient that helps during thebackpropagation process to update the model weights. A lower Cross-Entropy Loss indicates better performance, as it reflects that the predicted probabilities are closer to the actual class labels.

While accuracy is also an important metric for gauging how many instances were classified correctly, it does not provide information about how confidently a model is making those classification decisions. For more nuanced evaluation, especially when dealing with imbalanced datasets or when a model classifies probabilities rather than categorical outputs, Cross-Entropy Loss becomes more insightful.

Mean Squared Error (MSE) and Mean Absolute Error (MAE) are metrics more commonly used in regression tasks rather than classification. These metrics measure