https://github.com/angusg/deep-learning-dreissenid
Source code for reproducing "Predicting Dreissenid Mussel Abundance using Deep Learning" by Galloway et al..
https://github.com/angusg/deep-learning-dreissenid
artificial-intelligence computer-vision deep-learning dreissena great-lakes machine-learning semantic-segmentation
Last synced: 10 months ago
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Source code for reproducing "Predicting Dreissenid Mussel Abundance using Deep Learning" by Galloway et al..
- Host: GitHub
- URL: https://github.com/angusg/deep-learning-dreissenid
- Owner: AngusG
- License: agpl-3.0
- Created: 2019-11-15T00:57:44.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-06-08T17:48:36.000Z (about 3 years ago)
- Last Synced: 2025-08-14T18:55:11.876Z (10 months ago)
- Topics: artificial-intelligence, computer-vision, deep-learning, dreissena, great-lakes, machine-learning, semantic-segmentation
- Language: Jupyter Notebook
- Homepage:
- Size: 93 MB
- Stars: 4
- Watchers: 2
- Forks: 0
- Open Issues: 1
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Metadata Files:
- Readme: README.md
- License: license.md
Awesome Lists containing this project
README
# Code for: Predicting Dreissenid Mussel Abundance in Nearshore Waters using Underwater Imagery and Deep Learning
A. Galloway, D. Brunet, R. Valipour, M. McCusker, J. Biberhofer, M. K. Sobol, M. Moussa, and G. W. Taylor. (2021). *Predicting Dreissenid Mussel Abundance in Nearshore Waters using Underwater Imagery and Deep Learning*. Limnology and Oceanography: Methods (pending minor revisions).
Datasets can be downloaded [here](https://doi.org/10.5683/SP3/MZEBOJ).
*Note*: Improved documentation and code cleaning for this repository is in progress.
# Overview
- The `predict` folder contains scripts for training and evaluating deep neural networks on DS1, DS2, & DS3.
- The `quadrat-extraction` folder contains code for extracting the contents of quadrat frames from images and video.
- The `label-me` folder contains code related to dataset preparation, preprocessing, and obtaining segmentation labels.