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[See this pdf](https://dev.iris.edu/hq/files/initiatives/das_rcn/DAS-RCN-2022_12_02-Spica.pdf)
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Or sign up to GLOBUS and go [here](https://app.globus.org/file-manager?origin_id=706e304c-5def-11ec-9b5c-f9dfb1abb183&origin_path=%2FFORESEE%2F&two_pane=false)
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## Modules
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The following is an explanation of the project structure
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All autoencoders are based on the BaseAE class
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### Utils
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### Finding anomalies
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Will upload jupyter notebooks soon
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# NOTES:
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* Utils for loss scaling and clipping exist in this repo, but is kinda wonky for certain models. However, f16 inference is easy:
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* Utils for loss scaling and clipping exist in this repo, but is kinda wonky for training certain models. However, F16 inference is easy:
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1. Select model
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2. Load model

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