Comments (9)
For the first question: yes, I think so.
For the second question: For now, I have not found any disadvantages except too many patches for a long-time training. However, including too many background patches might results in the ratio of signal decrease. According to my experience, around 10,000 patches for training should be good enough.
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Hello,
Thanks for your interest in our software package! According to my experience, there should not exist signals being eroded. Could you please tell me the training patch numbers?
Best regards
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This is generated from 4 images.
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This is weird. In normal cases, these patches are sufficient to generate good results. I suggest at least 100 epoches for training. If the results are still not ideal, would you like to share some data with me so I can figure it out? Thanks.
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Hi @PENGLU-WashU, I actually trained for 250 epochs. All the other paramters were same as the tutorial notebook. Apologies, I missed that. I am attaching the channel in question.
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Thanks for the sharing. I will try to see if it works with this one image.
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Hi @PENGLU-WashU, I actually trained for 250 epochs. All the other paramters were same as the tutorial notebook. Apologies, I missed that. I am attaching the channel in question.
Hello,
I just used this image as the training set and trained for 100 epoches. I set the parameter "ratio_thresh" as 0.98 so that enough patches for training. (In my case, there are 2448 patches). I have attached the predicted result. I have not found bias. Please check it and let me know any problems.
Best regards
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Thank you. This looks much better.
Is it correct to say that increasing the 'ratio_thresh' parameter incorporates more patches since we include patches that have a large percentage of "background" pixels?
Except for processing time, do you see any other disadvantages of keeping this parameter at a higher value across channels to include more patches?
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Thank you for this explanation and support with this issue! I will close this ticket.
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Related Issues (19)
- Image format HOT 5
- Val_loss HOT 4
- Updating tensorflow, cudnn and cudatoolkit HOT 1
- IMC_Denoise on M1 Mac HOT 3
- Logics behind data preprocessing HOT 2
- Isotype not found HOT 5
- Demo training data generates NaN for loss HOT 21
- Softplus activation function compatible for subsequent data normalisation? HOT 2
- Demo data produces NaN loss on multiple systems HOT 5
- Issue generated patches from MIBI-TOF tiff data HOT 4
- multi markers training HOT 2
- About the issue of GPU usage efficiency HOT 3
- Percentage of masked pixels HOT 4
- running DIMR and DeepSNiF together in the tutorial HOT 1
- Question about how to train DeepSNiF properly & integration with steinbock HOT 24
- Problems running on GPU (NVIDIA A40) HOT 7
- Edge case in training batch generation HOT 1
- N2V2 HOT 1
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