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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.04920 |
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| _version_ | 1866916370508152832 |
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| author | Zwart, Petrus |
| author_facet | Zwart, Petrus |
| contents | In scientific imaging, deep learning has become a pivotal tool for image analytics. However, handling large volumetric datasets, which often exceed the memory capacity of standard GPUs, require special attention when subjected to deep learning efforts. This paper introduces qlty, a toolkit designed to address these challenges through tensor management techniques. qlty offers robust methods for subsampling, cleaning, and stitching of large-scale spatial data, enabling effective training and inference even in resource-limited environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_04920 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | qlty: handling large tensors in scientific imaging Zwart, Petrus Computer Vision and Pattern Recognition Image and Video Processing In scientific imaging, deep learning has become a pivotal tool for image analytics. However, handling large volumetric datasets, which often exceed the memory capacity of standard GPUs, require special attention when subjected to deep learning efforts. This paper introduces qlty, a toolkit designed to address these challenges through tensor management techniques. qlty offers robust methods for subsampling, cleaning, and stitching of large-scale spatial data, enabling effective training and inference even in resource-limited environments. |
| title | qlty: handling large tensors in scientific imaging |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2407.04920 |