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SediNet is a configurable machine-learning framework for estimating either (or both) continuous and categorical variables from a photographic image of clastic sediment.

For more details, please see the paper:

Buscombe, D. (2019). SediNet: a configurable deep learning model for mixed qualitative and quantitative optical granulometry. Earth Surface Processes and Landforms. https://onlinelibrary.wiley.com/doi/abs/10.1002/esp.4760

Free Earth ArXiv preprint here

This repository contains code and data to reproduce the above paper, as well as additional examples and jupyter notebooks that you can run on the cloud and use as examples to build your own Sedinet sediment descriptor

The motivating idea behind SediNet is community development of tools for information extraction from images of sediment. You can use SediNet "off-the-shelf", or other people's models, or configure it for your own purposes.

Within this package there are several examples of different ways it can be configured for estimating categorical variables and various numbers of continuous variables

You can use the models in this repository for your purposes (and you might find them useful because they have been trained on large numbers of images). If that doesn't work for you, you can train SediNet for your own purposes even on small datasets