Fast Segment Anything
Xu Zhao Wenchao Ding Yongqi An Yinglong Du Tao Yu Min Li Ming Tang Jinqiao Wang
The recently proposed segment anything model (SAM) has made a significant
influence in many computer vision tasks. It is becoming a foundation step for
many high-level tasks, like image segmentation, image caption, and image
editing. However, its huge computation costs prevent it from wider applications
in industry scenarios. The computation mainly comes from the Transformer
architecture at high-resolution inputs. In this paper, we propose a speed-up
alternative method for this fundamental task with comparable performance. By
reformulating the task as segments-generation and prompting, we find that a
regular CNN detector with an instance segmentation branch can also accomplish
this task well. Specifically, we convert this task to the well-studied instance
segmentation task and directly train the existing instance segmentation method
using only 1/50 of the SA-1B dataset published by SAM authors. With our method,
we achieve a comparable performance with the SAM method at 50 times higher
run-time speed. We give sufficient experimental results to demonstrate its
effectiveness. The codes and demos will be released at
https://github.com/CASIA-IVA-Lab/FastSAM.