Image Classification

Quick Links

Dataset EDA

Data ReadMe file Data ReadMe describes the dataset used in this study and provides link to download the dataset needed to replicate our study.
We also show data distribution after augmentation and after synthetic image generation using AC-GAN.

Models

Models ReadMe file contains all the information about the models used in this study.
Implementation and training of AC-GAN

Setup

Clone GitHub Repository

git clone [repo link]
Create Environment

We used conda environment for our virtual environment

conda create -n dermcv python=3.7 -y

conda activate dermcv
Install Dependencies pip install -r requirements.txt
Install Pytorch with GPU Verify that you pytorch is installed and cuda is configured.

pip install torch==1.12.0+cu116 torchvision==0.13.0+cu116 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu116

Verify Pytorch has Cuda

import torch
torch.cuda.is_available()

Repository Structure

├── README.md
├── csv
├── data
├── logs
├── models
├── notebooks
│   ├── GAN-Augmentation.ipynb
│   └── data_explration.ipynb
├── requirements.txt
├── save
└── src
    ├── GAN-PyTorch.py
    ├── config.py
    ├── dataset.py
    ├── imports.py
    ├── models.py
    ├── train.py
    └── utils.py

Running

Training

train.py contain all the training scripts while the parameters used are located in the config.py. To make changes to the parameters just change the default values in the config.py or pass it with the training script.

python train.py

Preprocessing

Augmentation
Generative Adversarial Networks

GAN Training

We used AC-GAN to generate Images. It needs to be trained on the minority class image that needs to be extracted from the meta file on the HAM10k meta file. Data folder should contain a single folder named HAM10k that contains all the images, while the meta files should be placed in the csv folder as train/test.

python GAN-Pytorch.py --data your-data-folder --csv_files csv-files-folder --n_epochs epochs --batch_size 64 --n_classes minority-classes

Models to be used

Efficientnet
ViT
ConvNext
ResNet50
CNN

Contribution

@malsaidi @mjan2021

Contact

@mjan2021

mjan2021/ImageClassificationPyTorch