Segment Anything Model for Grain Characterization in Hard Drive Design

Kai Nichols, Matthew Hauwiller, Nicholas Propes, Shaowei Wu, Stephanie Hernandez, Mike Kautzky; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 8120-8124

Abstract

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.

Related Material

[pdf] [arXiv]
[bibtex]
@InProceedings{Nichols_2024_CVPR, author = {Nichols, Kai and Hauwiller, Matthew and Propes, Nicholas and Wu, Shaowei and Hernandez, Stephanie and Kautzky, Mike}, title = {Segment Anything Model for Grain Characterization in Hard Drive Design}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {8120-8124} }