How to Use YOLOv8 with SAM

The blog post by Arty Ariuntuya explores how the Segment Anything Model (SAM) can be integrated with YOLOv8 to enhance computer vision tasks such as instance segmentation and text-to-mask predictions. It discusses the importance of transforming bounding boxes into segmentation masks, which provide more precise object boundaries and facilitate advanced tasks like background removal. Utilizing a Jupyter notebook, the post outlines a process that combines the capabilities of Roboflow for data preparation and Ultralytics for object detection, enabling users to convert bounding boxes to segmentation masks effectively. By employing the SAM model in this workflow, practitioners can generate accurate instance segmentation datasets and manipulate visual data in innovative ways, thereby expanding the potential for analysis and application in fields ranging from autonomous vehicles to medical imaging.