In this lab, we analyzes diabetic retinopathy using ResNet, specifically using ResNet18 and ResNet50. We will also compare the results of ResNet18, ResNet50, and both networks with pre-trained weights. We also calculate the confusion matrix between the true label and the predicted label.
You can download the images here, and save the images in ./Data/data. You can find the training and testing corresponding image name and label in ./Data.
conda env create -f environment.yml
conda activate resnet
python train.py \
--model resnet50 \
--pretrain True \
--act relu \
--device cuda:0 \
--batch-size 32 \
--lr 1e-3 \
--epochs 10 \
--load False \
--img-size 256
