When training a neural network does every training image need to have every pixel class?
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Robin Hartley on 5 Jan 2023
Commented: Robin Hartley on 9 Jan 2023
I am trying to train a neural network that is able to take micrographs of polished cross-sections of polymer containing fibres and particles. I want the trained neural network to identiffy the pixels that correspond to the five different features: polymer, fibres, particle type A, particle type B, and background (black eges of images).
I have 126 training images which I have manually segmented. Below shows the original image (left), the normalised ground truth (centre), and the real ground truth (right).
I have a training image datastore (dsTrain) and I have set up the network as follows:
lgraph = unetLayers([296 296 1], 5,'encoderDepth',3);
options = trainingOptions('adam',...
Plots = "training-progress");
net4 = trainNetwork(dsTrain,lgraph_1,options);
My questions are:
- Does every training image have to contain pixels corresponding to each of the five features?
- My ground truth is made up of images where the pixel values are as follows:
- Polymer = 0
- Fibres = 1
- Particle A = 2
- Particle B = 3
- Background = 4
Is this the best way to set this up?
Currently when I train the network the RMSE remains very high (> 500) after several hundred iterations.
Thank you in advance!
Joss Knight on 7 Jan 2023
The answer to question 1 is no. The answer to question 3 is that probably 126 training images is not sufficient, you might need about 10x that amount plus some data augmentation.
I don't know the answer to question 2.
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