计算机科学
深度学习
人工智能
超参数
人工神经网络
模式识别(心理学)
航程(航空)
光谱成像
机器学习
遥感
地质学
复合材料
材料科学
作者
Conor C. Horgan,Mads S. Bergholt
出处
期刊:Cornell University - arXiv
日期:2021-08-17
被引量:1
标识
DOI:10.48550/arxiv.2108.07595
摘要
Deep learning computer vision techniques have achieved many successes in recent years across numerous imaging domains. However, the application of deep learning to spectral data remains a complex task due to the need for augmentation routines, specific architectures for spectral data, and significant memory requirements. Here we present spectrai, an open-source deep learning framework designed to facilitate the training of neural networks on spectral data and enable comparison between different methods. Spectrai provides numerous built-in spectral data pre-processing and augmentation methods, neural networks for spectral data including spectral (image) denoising, spectral (image) classification, spectral image segmentation, and spectral image super-resolution. Spectrai includes both command line and graphical user interfaces (GUI) designed to guide users through model and hyperparameter decisions for a wide range of applications.
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