Data from: Autonomous classification of wave breaker type in a large wave flume

This dataset accompanies the article "Autonomous Classification of Wave Breaker Type in a Large Wave Flume." It contains the Python code used to train, test, and implement a You Only Look Once-random forest (YOLO-RF) machine learning (ML) model for classifying breaking waves (plunging or spilling) from GoPro videos collected in a wave flume. In addition to the Python code, it contains supplemental files, including the training and testing data sets for the YOLO and RF models, full-length input videos, an example of the model applied to one set of wave conditions, and examples of all files (including intermediary files) generated while training and testing the model. The YOLO model, which classifies five wave features (e.g., prebreaking, curling, splashing, whitewash, crumbling) in a set of video frames, is coupled to an RF model that takes normalized feature counts over multiple frames as inputs and outputs a wave-breaking type for each detected wave. The model, trained and validated with data from a large-scale wave-flume experiment, identifies breaker type with 94% accuracy.