How to train end-to-end (image-to-image)?
jauf...@gmail.com
Hello everyone,
We are quite new to caffe, but what we have seen so far, looks really promising.
After reading a few papers [1][2], we wanted to reproduce the result of [1],
specifically about a segmentation challenge [4].
We downloaded the modified caffe from [3] and were able to execute it,
just to see, that the trained network didn't work with the dataset from [4].
At first we thought that the network needs to be trained for the specific problem.
Which lead to the problem of how to do 'image-to-image (aka end-to-end) learning ' ([4], training data).
This lead us to 'holistically nested edge detection' (hed, [2]), where image-to-image learning,
seems to be used.
With hed, we were able to retrain the network on our own. But it doesn't work (it leads to all 0 or 0.5 images - black images :-( ) if we try to train the network for the dataset of [4]. For initialisation we wrote a script to calculate the mean-map witch we use for the dataset of [4]Our question(s) are:
How can we reproduce the result, mentioned in [1] by running image-to-image training?
or:
How do you train networks, where we have image-to-image learning?
Since we only have 30 image-to-image pairs, should we implement deformation as mentioned in [1]/[3] via matlab/python or is there a functionality within caffe already?
Are we missing something simple from [1] or [2]?
Kind regards,
Klaus and Bernhard
[1] http://arxiv.org/abs/1505.04597
[2] https://github.com/s9xie/hed
[3] http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/
[4] EM segmentation challenge at ISBI 2012, http://brainiac2.mit.edu/isbi_challenge/home