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• W4313291818 abstract "ABSTRACT Image-based machine learning methods are quickly becoming among the most widely-used forms of data analysis across science, technology, and engineering. These methods are powerful because they can rapidly and automatically extract rich contextual and spatial information from images, a process that has historically required a large amount of manual labor. The potential of image-based machine learning methods to change how researchers study the ocean has been demonstrated through a diverse range of recent applications. However, despite their promise, machine learning tools are still under-exploited in many domains including species and environmental monitoring, biodiversity surveys, fisheries abundance and size estimation, rare event and species detection, the study of wild animal behavior, and citizen science. Our objective in this article is to provide an approachable, application-oriented guide to help researchers apply image-based machine learning methods effectively to their own research problems. Using a case study, we describe how to prepare data, train and deploy models, and avoid common pitfalls that can cause models to underperform. Importantly, we discuss how to diagnose problems that can cause poor model performance on new imagery to build robust tools that can vastly accelerate data acquisition in the marine realm. Code to perform our analyses is provided at https://github.com/heinsense2/AIO_CaseStudy" @default.
• W4313291818 created "2023-01-06" @default.
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• W4313291818 creator A5083381174 @default.
• W4313291818 date "2022-12-27" @default.
• W4313291818 modified "2023-09-30" @default.
• W4313291818 title "Demystifying image-based machine learning: A practical guide to automated analysis of field imagery using modern machine learning tools" @default.
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• W4313291818 doi "https://doi.org/10.1101/2022.12.24.521836" @default.
• W4313291818 hasPublicationYear "2022" @default.
• W4313291818 type Work @default.

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