What is F1 Score? A Computer Vision Guide.

The article by Abirami Vina on the Roboflow blog provides an in-depth exploration of the F1 score, a crucial metric for evaluating computer vision models by balancing precision and recall. It highlights the significance of the F1 score in scenarios where false positives and negatives are critical, such as medical imaging systems, and explains its calculation using the harmonic mean of precision and recall. The text describes how precision measures a model's accuracy in identifying positive cases by avoiding false positives, while recall assesses the model's ability to capture all positive instances. It further delves into calculating the F1 score for binary and multiclass classifications, discussing the Macro, Micro, and Weighted approaches for multiclass scenarios. The article also addresses the advantages of the F1 score in providing a robust evaluation metric for imbalanced datasets and its limitations, such as its assumption of equal importance for precision and recall and its focus on positive instances without considering true negatives. Ultimately, the F1 score is presented as a valuable tool for understanding a model's performance beyond basic accuracy.