Martjin,

I am very confused on how to load the paclets to run UNET. What is the file structure and where should I put it? When I use "File-Install-Package", what paclets should I use? I am not really familiar with the paclet structure in Mathematica.

Your help would be very much appreciated.

Claudio

Thank you!

Is there any reason, other than memory limitations, to limit the maximum input sizes to 256 for each dimension? Shouldn't it be enough with being multiple of 16?

I have overriden the definition of TrainUNET so as to allow that circunventing the size cap and am training on 400x304 images, and definitely training- time blows up, but hope the rest is OK. Maybe some hyper parameters would need to be adapted for the larger images?

Be sure to put it on PackageData:

http://packagedata.net/

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Thanks! What do you mean with different amounts of outputs?

This is my understanding, but i might be mistaken. In my functions i layout the network architecture. Once the function is called you have to fill in the number of input classes, data dimensions, number of features and the number of output classes. From that moment everything is fixed. The UNET only has one output, an array with the binary segmentation.

The only place where there are multiple output ports is in the loss function, which is well explained in the help of mathematica LossFunction. But these are only used in training.

My approach was to create the neural net with only an Input, the data, and an Output, the segmentation, defined. Only when I start training the net I add the appropriate loss functions which have as input the Output of the net and the ground truth Target for comparison. The loss function it self just outputs a number. You can add as many loss functions as needed and you can even provide a different Target to each. As i understand the training tries to minimize the sum of all the loss functions.

Hope this helps.

Hope this helps

We have one input channel Nchan = 1

Step 1: split the lables into classes, for this exmaple Nclass = 4 (background, sphere, rectangle and overlap), so the network looks for 4 output lables.

Step 2: The first encoding layer split our input channel into 32 channels 1->32

Step 3: by the time we arrive at the deepest level our channels are downsampled but we have a lot of them

Step 4: After decoding we go back to 32 channels again. Hopefully by now each channel contains unique information about the image.

Step 5: We are looking for 4 labels so the 32 channels are mapped to 4 channels

Step 6: The 4 channels are converted to probability maps.

Step 7: The probability map become our 4 labels. In this case the network fail to correctly segment our example.

Our background and overlap are segmented whit a very high probability because they have a very distinct contrast which is easy to detect. However our circle and rectangle have the same contrast and have to be labeled based on their shape and not contrast. This is much more difficult and as such mistakes are made.