- In this work, we propose Dense Transformer Networks to apply spatial transformation to semantic prediction tasks.
- The third and fourth rows are the segmentation results of U-Net and DTN, respectively.
- max_epoch: how many iterations or steps to train
test_step: how many steps to perform a mini test or validation
save_step: how many steps to save the model
summary_step: how many steps to save the summary
sampledir: where to store predicted samples, please add a / at the end for convinience
model_name: the name prefix of saved models
test_epoch: which step to test or predict
network_depth: how deep of the U-Net including the bottom layer
class_num: how many classes.
- We have conv2d for standard convolutional layer, and ipixel_cl for input pixel convolutional layer proposed in our paper.
- We have deconv for standard deconvolutional layer, ipixel_dcl for input pixel deconvolutional layer, and pixel_dcl for pixel deconvolutional layer proposed in our paper.
Contribute to dtn development by creating an account on GitHub.
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