Early Stopping Effectiveness for YOLOv4

Abstract

Background: YOLOv4 is one of the fastest algorithms for object detection. Its methods, i.e., bag of freebies and bag of specials, can prevent overfitting, but this can be combined with early stopping as it could also prevent overfitting. Objective: This study aims to identify the effectiveness of early stopping in preventing overfitting in the YOLOv4 training process. Methods: Four datasets were grouped based on the training data size and object class, These datasets were tested in the experiment, which was carried out using three patience hyperparameters: 2, 3, and 5. To assess the consistency, it was repeated eight times. Results: The experimental results show that early stopping is triggered more frequently in training with data below 2,000 images. Of the three patience hyperparameters used, patience 2 and 3 were able to halve the training duration without sacrificing accuracy. Patience 5 rarely triggers early stopping. There is no pattern of correlation between the number of object classes and early stopping. Conclusion: Early stopping is useful only in training with data below 2,000 images. Patience with a value of 2 or 3 are recommended. Keywords: Early Stopping, Overfitting, Training data, YOLOv4