jschuetzke/synthetic-spectra-benchmark
Benchmarking of 1D pattern classification networks
This project provides a standardized way to test how well machine learning models can classify different types of spectroscopic data, such as X-ray diffraction or Raman spectroscopy. It takes in artificial spectra that mimic real-world measurements and outputs an evaluation of the model's ability to distinguish between various material compositions, even with common experimental noise. This is for scientists, materials engineers, or researchers who develop or use AI for material characterization.
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Use this if you need to objectively validate and benchmark neural networks designed to classify spectroscopic patterns.
Not ideal if you are looking for a tool to process or analyze real experimental spectroscopic data directly, as this focuses on synthetic data for model validation.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Jul 19, 2023
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