Segment Anything Model for Grain Characterization in Hard Drive Design
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914921332080640 |
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| author | Nichols, Kai Hauwiller, Matthew Propes, Nicholas Wu, Shaowei Hernandez, Stephanie Kautzky, Mike |
| author_facet | Nichols, Kai Hauwiller, Matthew Propes, Nicholas Wu, Shaowei Hernandez, Stephanie Kautzky, Mike |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_12732 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Segment Anything Model for Grain Characterization in Hard Drive Design Nichols, Kai Hauwiller, Matthew Propes, Nicholas Wu, Shaowei Hernandez, Stephanie Kautzky, Mike Computer Vision and Pattern Recognition Materials Science Machine Learning 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. |
| title | Segment Anything Model for Grain Characterization in Hard Drive Design |
| topic | Computer Vision and Pattern Recognition Materials Science Machine Learning |
| url | https://arxiv.org/abs/2408.12732 |