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deepoyster499

Independent Study where the research of oyster health, particularly the invasion of mud blisters, is being conducted utilizing deep learning methods. Image Recognition is the primary method of Machine Learning on this project

https://github.com/paedarr/deepoyster499

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
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    DOI references
  • ○
    Academic publication links
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    Academic email domains
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    Institutional organization owner
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    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (9.3%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Independent Study where the research of oyster health, particularly the invasion of mud blisters, is being conducted utilizing deep learning methods. Image Recognition is the primary method of Machine Learning on this project

Basic Info
  • Host: GitHub
  • Owner: paedarr
  • License: gpl-3.0
  • Language: Python
  • Default Branch: main
  • Size: 12.8 MB
Statistics
  • Stars: 3
  • Watchers: 2
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 1 year ago · Last pushed about 1 year ago
Metadata Files
Readme License Citation

README.md

Deep Learning Research - COMP 499
Drew Davinack (PhD), Mark LeBlanc (PhD), Avery Chan, Paedar Rader, Sayed Ibrahimi

Oyster with some mud blister infection

This project is a study of the health of oysters, specifically the infestation of mud blisters from burrowing worms, to build an image recognition model that can accurately predict how much surface area of an oyster is infected with the parasites. This section is a work in progress.

Installations

Make sure the following are installed on local machine/cloud device:

  • Lastest version of Anaconda (Python 3.12)
  • PyTorch
    • Install with Conda:
      • conda install pytorch torchvision -c pytorch
    • Install with pip:
      • pip3 install torch torchvision

  • Skorch (NN Dependency)
    • Install with Conda:
      • git clone https://github.com/skorch-dev/skorch.git
      cd skorch
      conda create -n skorch-env python=3.10
      conda activate skorch-env

      install pytorch version for your system (see below)

      python -m pip install -r requirements.txt
      python -m pip install .

    • Install with pip:
      • python -m pip install -U skorch
  • Latest version of Python (3.12)
  • This section is subject to changes

Usage of Resnet-50:

-- Resnet-50 --
@article{He2015, author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun}, title = {Deep Residual Learning for Image Recognition}, journal = {arXiv preprint arXiv:1512.03385}, year = {2015} }

  • Corresponding Repo: *

https://github.com/KaimingHe/deep-residual-networks

Owner

  • Name: Paedar Rader
  • Login: paedarr
  • Kind: user
  • Location: Norton, MA - Guilford, CT
  • Company: Wheaton College, MA

Computer Science student @ Wheaton College

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Davinack"
  given-names: "Drew"
- family-names: "LeBlanc"
  given-names: "Mark"
- family-names: "Rader"
  given-names: "Paedar"
- family-names: "Chan"
  given-names: "Avery"
- family-names: "Ibrahimi"
  given-names: "Sayed"
title: "Deep Oyster 499"
version: 0.1.0
date-released: 2024-12-4
url: "https://github.com/paedarr/DeepOyster499"

GitHub Events

Total
  • Watch event: 1
  • Delete event: 3
  • Public event: 1
  • Push event: 9
  • Create event: 2
Last Year
  • Watch event: 1
  • Delete event: 3
  • Public event: 1
  • Push event: 9
  • Create event: 2

Dependencies

requirements.txt pypi
  • matplotlib <=3.9.2
  • numpy <=2.1
  • os <=3.13
  • torch ==2.5.0
  • torchvision <=0.20