Is the 'random extracting' cause the different testing result? about retina-unet HOT 2 CLOSED

Hi,

Thank you for experimenting the code!

Yes, you're right.
Since both the training and the validation datasets are obtained with random extraction of the patches, these datasets will be slightly different at every experiment. Therefore also the performance will be slightly different every time.
I tried 5 different experiments reporting the following scores (rounded at 4th digit): 0.9788, 0.9792, 0.9787, 0.9790, 0.9791. Therefore, the results I show here are referred to the "average" run, the one reporting the score of 0.9790.
I should try with more runs in order to get a minimum statistics. Otherwise I will fix the random seed, so the performance can be repeated more exactly. Please let me know if you get other results very different with respect to the numbers I got.

With my TITAN X I take roughly 10 minutes per epoch, so I guess 900 sec can be reasonable as well.

'loss' is the value of the loss function at the end of each epoch, while 'acc' is the (global) accuracy: the ratio between the number of pixels correctly classified and the total number of pixels. These are referred to the training dataset, while 'val_loss' and 'val_acc' are the same thing but related to the validation dataset. The AUC ROC is related to the accuracy, I think a nice explanation is reported here

I'm sorry, what's DL BBS?

from retina-unet.

Thank you for your rapid reply and your answers are very helpful to me.

I think I had a wrong expression, I mean forums about 'deep learning', best is about 'segmentation' (now we called forum...Not BBS...)

from retina-unet.