CNN classification for dentistry project

To use the classifier consider the following parameters: usage: main.py [-h] [--arch {resnet18,resnet50,vgg19_bn,inception_v3,resnetusm}] [--val_mode {once,k-fold,randomsampler}] [--batch_size BATCH_SIZE] [--workers WORKERS] [--k_fold_num K_FOLD_NUM] [--val_num VAL_NUM] [--random_seed RANDOM_SEED] [--data_dir DATA_DIR] [--cuda CUDA] [--shuffle_dataset SHUFFLE_DATASET] [--pretrained PRETRAINED] [--lr LR] [--momentum MOMENTUM] [--weights WEIGHTS] [--train TRAIN] [--num_epochs NUM_EPOCHS] [--save SAVE] [--image IMAGE] [--folder FOLDER]

optional arguments: -h, --help show this help message and exit --arch {resnet18,resnet50,vgg19_bn,inception_v3,resnetusm} The network architecture --val_mode {once,k-fold,randomsampler} Type of validation you want to use: f.e: k-fold --batch_size BATCH_SIZE Input batch size for using the model --workers WORKERS Number of data loading workers --k_fold_num K_FOLD_NUM Number of folds you want to use for k-fold validation --val_num VAL_NUM Number of times you want to run the model to get a mean --random_seed RANDOM_SEED Random seed to shuffle the dataset --data_dir DATA_DIR Directory were you take images, they have to be separeted by classes --cuda CUDA Use gpu by cuda --shuffle_dataset SHUFFLE_DATASET Number of folds you want to use for k-fold validation --pretrained PRETRAINED The model will be pretrained with --lr LR, --learning-rate LR initial learning rate --momentum MOMENTUM momentum --weights WEIGHTS The .pth doc to load as weights --train TRAIN The .pth doc to load as weights --num_epochs NUM_EPOCHS number of epochs to train for --save SAVE If you want to save weights and csvs from the trained model or evaluation --image IMAGE The source file path of image you want to process --folder FOLDER The folder where you want to save