https://github.com/mdcjansen/cnns

Various codes and scripts used during AI research. Orginally developed in the Binary_label_predictions_with_CNNs repository
https://github.com/mdcjansen/cnns

alexnet artificial-intelligence bioinformatics bioinformatics-analysis bioinformatics-scripts cnn convnext convolutional-neural-networks densenet121 groovy inceptionv3 python pytorch resnet-50 shufflenet

Last synced: 6 days ago
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Various codes and scripts used during AI research. Orginally developed in the Binary_label_predictions_with_CNNs repository

• Host: GitHub
• URL: https://github.com/mdcjansen/cnns
• Owner: mdcjansen
• License: mit
• Created: 2024-05-21T08:16:31.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-05-21T08:46:30.000Z (8 months ago)
• Last Synced: 2024-11-19T21:33:59.420Z (2 months ago)
• Topics: alexnet, artificial-intelligence, bioinformatics, bioinformatics-analysis, bioinformatics-scripts, cnn, convnext, convolutional-neural-networks, densenet121, groovy, inceptionv3, python, pytorch, resnet-50, shufflenet
• Language: Python
• Homepage:
• Size: 47.9 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# CNNs

This repo contains various scripts produced during my bachelors graduation internship. These include the scripts for

six CNNs to train and test, image preprocessing, and post-training analysis. 

The CNNs can be trained to perform binary classification tasks on a set of input images

## Table of contents

* [Requirements](#requirements)

* [CNN testing](#cnn-testing)

* [CNN training](#cnn-training)

* [WIP](#WIP)

* [Post training analysis](#post-training-analysis)

* [Preprocessing](#preprocessing)

* [Acknowledgements](#Acknowledgements)

## Requirements

All models were run and trained on an in-house anaconda environment. This environment mirrors the freely available

[pytorch2 environment](https://pytorch.org/get-started/pytorch-2.0/#getting-started). Additionally, 

[wandb.ai](https://wandb.ai/site) is required for logging the training and validation metrics produced by the CNNs

## CNN testing

The CNN scripts within this folder are capable of loading the models produced during training and running them on a test

set or functional dataset in order to perform predictions with the trained models.

## CNN training

Here, the six CNNs [AlexNet](https://doi.org/10.1145/3065386), [DenseNet121](https://doi.org/10.48550/arXiv.1608.06993),

[ConvNeXt Tiny](https://doi.org/10.48550/arXiv.2201.03545), [InceptionV3](https://doi.org/10.48550/arXiv.1512.00567), 

[ResNet50](https://doi.org/10.48550/arXiv.1512.03385), and [ShuffleNet](https://doi.org/10.48550/arXiv.1807.11164) 

can be found. All scripts are in working order and capable of performing binary classification tasks. These CNNs require

a csv file containing the file path and hyperparameters for the model. An example file can be found within this folder.

## Post training analysis

This folder contains all scripts that can be utilised after training or testing of the models has been completed

## Preprocessing

In this folder, various scripts can be found that aid in the preprocessing of input data, before training or testing the

CNNs.

## Acknowledgements

* [AlexNet](https://doi.org/10.1145/3065386)

* [DenseNet121](https://doi.org/10.48550/arXiv.1608.06993)

* [ConvNeXt Tiny](https://doi.org/10.48550/arXiv.2201.03545)

* [InceptionV3](https://doi.org/10.48550/arXiv.1512.00567)

* [PyTorch](https://dl.acm.org/doi/10.5555/3454287.3455008)

* [ResNet50](https://doi.org/10.48550/arXiv.1512.03385)

* [ShuffleNet](https://doi.org/10.48550/arXiv.1807.11164)

* [wandb.ai](https://wandb.ai/site)