How to Train YOLOv7 Instance Segmentation on a Custom Dataset

In December 2022, Piotr Skalski published a detailed guide on training a YOLOv7 instance segmentation model using a custom dataset. The tutorial highlights the efficiency of the YOLOv7 model, which, despite being less accurate than models like OneFormer, offers significantly faster performance, making it suitable for real-time applications. Skalski outlines the process of setting up a Python environment with GPU access, installing YOLOv7, and inferring with pre-trained models, emphasizing the importance of balancing speed and accuracy in model selection. The guide also details preparing a custom dataset using Roboflow, focusing on the significance of using polygon annotations for precise object shape learning, and discusses the steps for applying data augmentations and exporting the dataset for training. Finally, Skalski explains the evaluation of model performance on unseen data, the deployment of trained models using Roboflow, and the potential applications of instance segmentation in various fields such as structural damage assessment in buildings, highlighting the usefulness of the YOLOv7 model for practical, real-world tasks.