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 initialization 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
Ps: We asked the same question at the caffe-user group and intend to post solutions at both locations.
