How to Train YOLOv5 Instance Segmentation on a Custom Dataset

In September 2022, a blog post by Paul Guerrie and Trevor Lynn on the Roboflow Blog detailed the process of training a YOLOv5 model for instance segmentation using a custom dataset. The post explains how YOLOv5, originally known for object detection, has expanded to support instance segmentation tasks and provides a step-by-step guide for setting up the environment, preparing a dataset, and training the model. The authors emphasize the utility of Roboflow Annotate for data labeling and augmentation, making it easier to train models by providing a comprehensive suite of tools including preprocessing options and the ability to use polygon annotations for instance segmentation. Additionally, the blog discusses using pre-trained weights to accelerate training and improve results, as well as testing and validating the custom-trained model. The authors also highlight the potential for further model improvement through active learning and deploying models to the edge using Roboflow Inference, a solution that supports deployment on various devices. The blog post serves as a comprehensive guide for those interested in leveraging YOLOv5's instance segmentation capabilities for practical applications.