Roboflow Training Graphs Guide

Understanding training graphs in computer vision is essential for diagnosing a model's performance, as they reveal whether a model is learning effectively or merely guessing. This guide outlines how to interpret various training graphs generated by Roboflow for models like RF-DETR, YOLOv11, and Roboflow 3.0, including metrics such as Mean Average Precision (mAP) and different forms of loss like box loss, class loss, and object loss. The mAP graph indicates a model's precision in object detection, while box loss measures localization errors, class loss assesses label accuracy, and object loss evaluates the model's ability to detect the presence of objects. Advanced graphs distinguish between training and validation performance, highlighting issues like overfitting, where a model memorizes rather than learns from data. The guide emphasizes the importance of using these graphs for fine-tuning hyperparameters, diagnosing specific errors, and auditing data quality to enhance model performance. By offering detailed visualizations, Roboflow aims to facilitate better decision-making and improve the efficiency of model training in computer vision.