HenriquesLab / ZeroCostDL4Mic

ZeroCostDL4Mic: A Google Colab based no-cost toolbox to explore Deep-Learning in Microscopy
MIT License
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Stardist error when create dataset obeject #256

Closed mdoaskndfweon closed 1 year ago

mdoaskndfweon commented 1 year ago

input image size is 500500, patch size = 128 or 256 or 64, batch size is 1, but when create dataset object, it cause gpu crash for unknown reason, I tried backbone = UNet and grid size increase (8,8) but still failed, does some way can solved this, my image is too big? I had already divided 10001000 into 5*5, that's my parameter below and bug A clear and concise description of what the bug is.

To Reproduce Steps to reproduce the behavior:

  1. Go to '...'
  2. Click on '....' <img width="1623" alt="Screenshot 2023-03-31 at 9 42 43 PM" src="https://user Screenshot 2023-03-31 at 9 54 33 PM

    -images.githubusercontent.com/88061206/229260122-720faa15-8715-45bf-b19a-355af5a22aea.png">

    Screenshot 2023-03-31 at 9 54 12 PM Screenshot 2023-03-31 at 9 54 49 PM Screenshot 2023-03-31 at 9 55 13 PM

    always reach out GPU memory

  3. Scroll down to '....'
  4. See error

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mdoaskndfweon commented 1 year ago

@esgomezm

mdoaskndfweon commented 1 year ago

image is 32-bit

mdoaskndfweon commented 1 year ago

hello, sorry for that confusing , I had already solve above those problem, but I have question relate my result for the nuclei segmentation , suppose that input image size is 1000,

Screenshot 2023-04-01 at 6 42 41 PM

this is the test result ,

Screenshot 2023-04-01 at 6 43 42 PM

for example , right hand side of the grapth is segmentation result, but when I try to predict new image without label,

Screenshot 2023-04-01 at 6 45 31 PM

the result are showed below

Screenshot 2023-04-01 at 6 45 07 PM

you see, totally differnt from the test data, but i want the format look like the same for the test data format.

my input block size x and y is 756, min overlap is256, When I decrease of block size or decrease of lin overlap, it show error and can't output the grapth

mdoaskndfweon commented 1 year ago

@mdoaskndfweon