Fast Segment Anything
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.
6493c9fa897021202a0d0df9
Xu Zhao
claimed_verified
2023-06-26T07:33:18.561Z
635d58aae7aef2358aa12c96
/avatars/6720fa8f8e58a20a9a4178c4cf8c9912.svg
zhaoxu
user
zxairdeep
6493c9fa897021202a0d0dfa
Wenchao Ding
6493c9fa897021202a0d0dfb
Yongqi An
claimed_verified
2023-06-23T10:02:36.121Z
63bf6e6d4a2beec65569faf8
https://cdn-avatars.huggingface.co/v1/production/uploads/63bf6e6d4a2beec65569faf8/hMLhTtUQ2vxo2OI_VyR8K.png
Yongqi An
user
An-619
6493c9fa897021202a0d0dfc
Yinglong Du
6493c9fa897021202a0d0dfd
Tao Yu
6493c9fa897021202a0d0dfe
Min Li
6493c9fa897021202a0d0dff
Ming Tang
6493c9fa897021202a0d0e00
Jinqiao Wang
Fast Segment Anything
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.
A speed-up method using a CNN detector with an instance segmentation branch achieves comparable performance to SAM with 50 times faster runtime by training on a small subset of SAM's dataset.
segment anything model SAM Transformer architecture CNN detector instance segmentation instance segmentation branch