​​Fast Segment Anything
The Segment Anything Model (SAM), a revolutionary tool in computer vision tasks, has significantly impacted various high-level tasks like image segmentation, image captioning, and image editing. However, its application has been restricted in industry scenarios due to its enormous computational demand, largely attributed to the Transformer architecture handling high-resolution inputs.
The authors of this paper have proposed a speedier alternative method that accomplishes this foundational task with performance on par with SAM, but at a staggering 50 times faster! By ingeniously reformulating the task as segments-generation and prompting and employing a regular CNN detector with an instance segmentation branch, they've converted this task into the well-established instance segmentation task. The magic touch? They've trained the existing instance segmentation method using just 1/50 of the SA-1B dataset, a stroke of brilliance that led to a solution marrying performance and efficiency.
Paper link: https://huggingface.co/papers/2306.12156
Code link: https://github.com/CASIA-IVA-Lab/FastSAM
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-fastsam
#deeplearning #cv #segmentanythingmodel #efficiency
The Segment Anything Model (SAM), a revolutionary tool in computer vision tasks, has significantly impacted various high-level tasks like image segmentation, image captioning, and image editing. However, its application has been restricted in industry scenarios due to its enormous computational demand, largely attributed to the Transformer architecture handling high-resolution inputs.
The authors of this paper have proposed a speedier alternative method that accomplishes this foundational task with performance on par with SAM, but at a staggering 50 times faster! By ingeniously reformulating the task as segments-generation and prompting and employing a regular CNN detector with an instance segmentation branch, they've converted this task into the well-established instance segmentation task. The magic touch? They've trained the existing instance segmentation method using just 1/50 of the SA-1B dataset, a stroke of brilliance that led to a solution marrying performance and efficiency.
Paper link: https://huggingface.co/papers/2306.12156
Code link: https://github.com/CASIA-IVA-Lab/FastSAM
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-fastsam
#deeplearning #cv #segmentanythingmodel #efficiency
