How to Use FastSAM

Segment Anything (SAM) is a vision foundation model known for its accuracy in segmenting objects within images based on user prompts but is limited by the computational demands of its Transformer architecture. FastSAM, an open-source model trained on just 2% of the SA-1B dataset that SAM uses, addresses these constraints by operating 50 times faster through a decoupled approach involving all-instance segmentation followed by prompt-guided selection. Utilizing a CNN-based detector trained via dataset distillation, FastSAM achieves near-equivalent performance to SAM while being more suitable for real-time applications. The blog post provides practical examples and code snippets for using FastSAM to perform image segmentation tasks, such as segmenting pills and chip parts, and illustrates a comparison between the masks generated by SAM and FastSAM, highlighting FastSAM's efficiency and accuracy across various tasks.