Development of new materials in hard drive designs requires characterization of nanoscale materials through grain segmentation. The high-throughput quickly changing research environment makes zero-shot generalization an incredibly desirable feature. For this reason, we explore the application of Meta's Segment Anything Model (SAM) to this problem. We first analyze the out-of-the-box use of SAM. Then we discuss opportunities and strategies for improvement under the assumption of minimal labeled data availability. Out-of-the-box SAM shows promising accuracy at property distribution extraction. We are able to identify four potential areas for improvement and show preliminary gains in two of the four areas.
@article{arxiv.2408.12732,
title = {Segment Anything Model for Grain Characterization in Hard Drive Design},
author = {Kai Nichols and Matthew Hauwiller and Nicholas Propes and Shaowei Wu and Stephanie Hernandez and Mike Kautzky},
journal= {arXiv preprint arXiv:2408.12732},
year = {2024}
}
Comments
This paper has been accepted by the International Workshop on Computer Vision for Materials Science in conjunction with the IEEE/CVF CVPR 2024