In the first part of this series, we introduced the basic concepts of YOLO and its data format. We also specifically discuss how it can be used for real-time object detection and fire detection.
In the second part, we will dig a little deeper and build a model for a real-time fire detection application. We'll start by discussing the prerequisites and necessary libraries for setting up the environment, preparing the dataset, and finally training the model. Also how to evaluate the model and its use.
Throughout the blog, code snippets and examples are provided to help you follow along and build your own real-time fire detection model using YOLO. After reading this blog, you will have a solid understanding of how YOLO works and how it can be applied to real applications.
Training and Validation
When training a machine learning model, it is important to understand the difference between training and validation sets. The training set is the data we use to train the model, while the validation set is used to evaluate the performance of the model and make necessary adjustments. For this project, we use the KFold method , which divides the data into k folds and selects one of the folds as the validation set. left