An Intelligent Image Segmentation Annotation Method Based on Segment Anything Model

Training of supervised neural network models requires a large amount of high-quality datasets with true values. In computer vision tasks such as object detection and image segmentation, the process of annotating a large number of original two-dimension data segments is extremely costly, which greatly affects the application rate of AI for HEP (High Energy Physics). The SAM(Segment Anything Model) based on transformer provides a promising solution to this problem. This paper proposes an intelligent image segmentation annotation method based on the SAM, by which the annotation efficiency can be increased by 50 times. Examples of annotations, the API (Application Programming Interfaces), and GUI (Graphical User Interfaces) are also provided. The use of this tool will greatly accelerate the process of transforming high-energy physics image-style data from raw data to AI-Ready data.