What means different model/parameters achieve higher score? about retina-unet HOT 2 CLOSED

Hi,

Nice to know you achieved higher score :)
May I ask you what you changed in the g-model (with respect to the "source" model)?
I have never had a GPU crash after so many epochs, I don't really know what could be the reason.

I chose the parameters basically by trial and error:

• The number of epochs, 150, is reasonable because usually the best performance is reached after 70-80 epochs, as you noticed as well. I tried with higher number (till 300) but did not observe any change.
• I tried with more than 190000 patches, but with no improvement.

After updating the cuDNN I got a warning from Theano as well, it looks like this new cuDNN version is not supported. However everything is still going smoothly as before.

Sure, you can use the code. Just out of curiosity, can you share your project with me? And good luck!

from retina-unet.

Thanks the reply.

I am glad to tell you what I changed with respect to the "source" model.
I added a upsampling and a maxpooling layer before the "source" model, and another same layers after the "source" model, and added a new merge layer like the "source" model do.
I will add function get_gnet beside get_unet OR separate get_net function from your source code and add unet, gnet function to it if you want.
I think g-model not good enough because it cost 3x training time with your source model and just increase 0.0005(about 0.05%) of the AUC of ROC, so I didn't tell you before.

I also tested some model "like" the source model, such as "W-net", "V-net" and the paper's model "U-net", there are all naming in shape, but there result are not better than g-model(g-net, that shape like gaussian curve).

I guess GPU crash reason is buffer overflow, I will check it out.
And I hope you can test it make sure that crash reason is not because my GPU is damaged : )

I am undergraduate in a general University, that project is my graduation project, and It will be easy to do, so It will contain some simple image process, such as channels separate, rotate, flip, some filters, color adjust, enhancement and so on. Of course It will contain retina vessels segmentation with THIS code. And I am not start coding yet. So I can't share you now, but I think I will opensource that implement after graduation.

Thanks again for Q&A.

from retina-unet.