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Edge-aware FCN with CRF-RNN layer

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/

'''

Usage

  • 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