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Data ReadMe file Data ReadMe describes the dataset used in this study and provides link to download the dataset needed to replicate our study.
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We also show data distribution after augmentation and after synthetic image generation using AC-GAN.
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Models ReadMe file contains all the information about the models used in this study.
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Implementation and training of AC-GAN
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Clone GitHub Repository
git clone [repo link] -
Create Environment
We used conda environment for our virtual environment
conda create -n dermcv python=3.7 -yconda 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()
├── 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
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
- Augmentation
- Generative Adversarial Networks
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
- Efficientnet
- ViT
- ConvNext
- ResNet50
- CNN
@malsaidi @mjan2021
@mjan2021