juliandewit / kaggle_ndsb2 Goto Github PK

Hi Julian,

Thanks for sharing the code. I have several questions after reading your solution document.

According to U-net paper, the output map is of size (row, column, 2), i.e., it has two feature channels. But looks like you only use 1 channel. Is that right? Would you like to explain more on this?

You once mentioned that "Segmentation nets are numerically unstable", would you like to elaborate more for this point? Are there any references discussing this?

You mentioned that "Note that I used relu activations and batch normalization after every convolution". With respect to "batch normalization" here, do you mean you will add
a normalization layer after convolution layer? If I remember correctly, I once heard that "normalization layer" may not be needed if we use batch normalization in the optimization method. In specific, I am not very clear what do you mean "use batch normalization after every convolution".

How many epochs do you use for training?

Thanks for the help.

I was Going through your Code ..
How do we

1. Determine the Area Covered By Segmentation in the Complete image in Terms of Percentage
2. Determine the size of segmentation which are detected...

Your Suggestion shall be highly appreciated.

Hi Julian,

Thanks for posting this wonderful and clear code. I couldn't find any code in the repo which creates those .png and .lst files in data_segmenter_trainset folder. Please help.

Hey, thanks for the nice code and blog post!

I don't know if it's important, though it confused me a bit. In the blog post the first dropout layer was placed after all downsampling layers, i.e. after pool5, but in the code I see that it was placed after pool4. I think it would be more consistent approach to put it after pool5 as in the post.

...
pool4 = convolution_module(net, kernel_size, pad_size, filter_count=filter_count * 4, down_pool=True)
net = pool4
net = mx.sym.Dropout(net)
pool5 = convolution_module(net, kernel_size, pad_size, filter_count=filter_count * 8, down_pool=True)
net = pool5
net = convolution_module(net, kernel_size, pad_size, filter_count=filter_count * 4, up_pool=True)
...

Hi,

Do you know if there is a keras implementation for the same and if there is something which is missing there prompting you to use mxnet?

Rahul.V.

if you could kindly help me by knowing the software used for creating segmentation labels...

Dear Julian,

thank you for your valuable work and your practical code.

I was running "step2_predict_volumes.py". in line 130:
predictions = pred_model.predict(pred_iter)

the code goes to models.py from mxnet .egg file. and it raises an error which says:
"d:\chhong\mxnet\include\mxnet./ndarray.h:217: Check failed: (shape_[0]) >= (end) Slice end index out of range"

I read about this problems but I couldn't fix it.

Would you please help me?