How to Use Grounded EdgeSAM

EdgeSAM is an advanced image segmentation model based on the Segment Anything (SAM) architecture from Meta AI, designed to identify object locations in images with increased speed but slightly reduced accuracy compared to its predecessor. Enhanced by the combination with Grounding DINO in the Grounded EdgeSAM setup, this model enables zero-shot object detection and segmentation by generating pixel-level masks for identified objects, making it ideal for applications like auto-labeling datasets for training fine-tuned models, such as YOLOv8. The guide provides a detailed walkthrough on using Grounded EdgeSAM to detect and label objects in images, emphasizing its practical utility in scenarios requiring high-speed performance, such as on mobile devices. Despite trade-offs in mask precision, the use of Grounded EdgeSAM with tools like Roboflow allows for efficient dataset preparation and model training, highlighting its versatility in computer vision tasks.