title: Fast Segment Anything
summary: 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.
paper_authors._id: 6493c9fa897021202a0d0df9
paper_authors.name: Xu Zhao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-26T07:33:18.561Z
paper_authors._id: 635d58aae7aef2358aa12c96
paper_authors.avatarUrl: /avatars/6720fa8f8e58a20a9a4178c4cf8c9912.svg
paper_authors.fullname: zhaoxu
paper_authors.type: user
paper_authors.user: zxairdeep
paper_authors._id: 6493c9fa897021202a0d0dfa
paper_authors.name: Wenchao Ding
paper_authors._id: 6493c9fa897021202a0d0dfb
paper_authors.name: Yongqi An
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-23T10:02:36.121Z
paper_authors._id: 63bf6e6d4a2beec65569faf8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63bf6e6d4a2beec65569faf8/hMLhTtUQ2vxo2OI_VyR8K.png
paper_authors.fullname: Yongqi An
paper_authors.type: user
paper_authors.user: An-619
paper_authors._id: 6493c9fa897021202a0d0dfc
paper_authors.name: Yinglong Du
paper_authors._id: 6493c9fa897021202a0d0dfd
paper_authors.name: Tao Yu
paper_authors._id: 6493c9fa897021202a0d0dfe
paper_authors.name: Min Li
paper_authors._id: 6493c9fa897021202a0d0dff
paper_authors.name: Ming Tang
paper_authors._id: 6493c9fa897021202a0d0e00
paper_authors.name: Jinqiao Wang
paper_title: Fast Segment Anything
paper_summary: 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.
paper_ai_keywords: segment anything model (SAM) Transformer architecture CNN detector instance segmentation branch instance segmentation task SA-1B dataset