How to preprocess input for pre trained networks?
Thank you very much
tsterin
smth
Thank you very much!
tsterin
Hi, it looks like the pixel intensities have been rescaled to [0 1] before normalization. It that right?
qianguih
smth
I see. Thank you very much!
qianguih
This is important information, I wonder it’s not put in the doc but in the example code?
ecolss
Agreed. If it wasn’t for this thread, I would have missed this important Normalization step for sure. It would be nice if it could be added to the documentation.
mehdi-shiba
This is pretty key information. Without doing this, and only doing mean centering and stddev normalization of the original Hunsfield units, I need to keep batch normalization enabled during test to see reasonable results from my volumetric segmentation network. This should really be in bold somewhere.
mattmacy
youkaichao
Sphinxs
Deeply
Sphinxs
Or, it is OK to use list sequences as shown in your case?
Deeply
Sphinxs
Deeply
If I use the pretrained model on ImageNet and fine-tune it on my own dataset, should I re-calculate the mean and std with my own dataset?
e3312f50f3ba76f35a60
Using the mean and std on ImageNet is pretty standard practice.  Since the mean and std are calculated using a million of images, the statistics is pretty stable. Also the pretrained model is trained using the mean and std in ImageNet. I do not recommend changing the mean and std to that on your small dataset.
jdhao
Thank you for your comment. I’ve been having that doubt a while ago. So basically what I infer from the comments as a summary is that the best practice is to leverage: in our specific task and dataset to check what’s the best option  to normalise.
r0mer0m
Nikronic