{"id":15975423,"url":"https://github.com/angusg/deep-learning-dreissenid","last_synced_at":"2025-09-03T08:33:49.259Z","repository":{"id":87480023,"uuid":"221814764","full_name":"AngusG/deep-learning-dreissenid","owner":"AngusG","description":"Source code for reproducing \"Predicting Dreissenid Mussel Abundance using Deep Learning\" by Galloway et al..","archived":false,"fork":false,"pushed_at":"2023-06-08T17:48:36.000Z","size":97486,"stargazers_count":4,"open_issues_count":1,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-08-14T18:55:11.876Z","etag":null,"topics":["artificial-intelligence","computer-vision","deep-learning","dreissena","great-lakes","machine-learning","semantic-segmentation"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"agpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/AngusG.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"license.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-11-15T00:57:44.000Z","updated_at":"2023-04-21T03:46:06.000Z","dependencies_parsed_at":null,"dependency_job_id":"7a7212ea-0c74-45a0-be57-f6bac7021196","html_url":"https://github.com/AngusG/deep-learning-dreissenid","commit_stats":{"total_commits":202,"total_committers":2,"mean_commits":101.0,"dds":0.00990099009900991,"last_synced_commit":"b6a91d4bac8c162105b268107fe6cf2c38589d93"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/AngusG/deep-learning-dreissenid","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AngusG%2Fdeep-learning-dreissenid","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AngusG%2Fdeep-learning-dreissenid/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AngusG%2Fdeep-learning-dreissenid/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AngusG%2Fdeep-learning-dreissenid/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AngusG","download_url":"https://codeload.github.com/AngusG/deep-learning-dreissenid/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AngusG%2Fdeep-learning-dreissenid/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":273414408,"owners_count":25101398,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-09-03T02:00:09.631Z","response_time":76,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["artificial-intelligence","computer-vision","deep-learning","dreissena","great-lakes","machine-learning","semantic-segmentation"],"created_at":"2024-10-07T22:01:43.890Z","updated_at":"2025-09-03T08:33:49.224Z","avatar_url":"https://github.com/AngusG.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Code for: Predicting Dreissenid Mussel Abundance in Nearshore Waters using Underwater Imagery and Deep Learning\n\nA. 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).\n\nDatasets can be downloaded [here](https://doi.org/10.5683/SP3/MZEBOJ).\n\n*Note*: Improved documentation and code cleaning for this repository is in progress.\n\n# Overview\n- The `predict` folder contains scripts for training and evaluating deep neural networks on DS1, DS2, \u0026 DS3.\n- The `quadrat-extraction` folder contains code for extracting the contents of quadrat frames from images and video.\n- The `label-me` folder contains code related to dataset preparation, preprocessing, and obtaining segmentation labels.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fangusg%2Fdeep-learning-dreissenid","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fangusg%2Fdeep-learning-dreissenid","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fangusg%2Fdeep-learning-dreissenid/lists"}