How to Train YOLOv5-Classification on a Custom Dataset

YOLOv5, a widely used object detection network, has expanded its functionality to include classification tasks as of August 2022. The blog post provides a detailed guide on how to train YOLOv5 for classification using a custom dataset, exemplified by a tomato classification model that can aid in precision agriculture. The process involves setting up the environment by cloning the Ultralytics YOLOv5 repository, preparing and labeling the dataset using tools like Roboflow Annotate, and applying preprocessing and augmentations to enhance model performance. It also covers training the model with pre-trained weights to accelerate learning and improve results, and finally testing and validating the custom model using a classification Colab notebook. The post suggests using Roboflow Train for model-assisted labeling to streamline the annotation process and emphasizes the importance of active learning to refine the model by adding more data based on its performance.