FastSAM  for Image Segmentation Tasks — Explained Simply

FastSAM is a groundbreaking image segmentation model that offers performance comparable to Meta's original SAM but at a staggering 50 times the speed. This incredible efficiency comes from its architecture, which replaces the heavy Vision Transformer used by SAM with a much lighter Convolutional Neural Network (CNN) based on YOLOv8-seg. The model operates in two stages, first generating segmentation masks for every potential object in an image. Afterwards, a prompt—such as a point, a bounding box, or even text processed by CLIP—is used to intelligently select and combine the correct masks to isolate the desired object.