change the model in train.py and eval.py
modelFns = { 'unet':Models.VanillaUnet.VanillaUnet, 'segnet':Models.Segnet.Segnet , 'vgg_unet':Models.VGGUnet.VGGUnet , 'vgg_unet2':Models.VGGUnet.VGGUnet2 , 'fcn8':Models.FCN8.FCN8, 'fcn32':Models.FCN32.FCN32, 'crfunet':Models.CRFunet.CRFunet }
data hierarchy ''' Use the Keras data generators to load train and test Image and label are in structure: train/ img/ 0/ gt/ 0/
test/
img/
0/
gt/
0/
'''
-
Train your model
python train.py --data_path ./datasets/ --checkpoint_path ./checkpoints/
-
Visualize the train loss, dice score, learning rate, output mask, and first layer convolutional kernels per iteration in tensorboard
tensorboard tensorboard --logdir=./checkpoints
-
Evaluate your model
python eval.py --data_path ./datasets/ --load_from_checkpoint ./checkpoints/model-0